Publications by authors named "Gabriella Balestra"

30 Publications

  • Page 1 of 1

Evaluation of Muscle Function by Means of a Muscle-Specific and a Global Index.

Sensors (Basel) 2021 Oct 29;21(21). Epub 2021 Oct 29.

Department of Electronics and Telecommunications and PoliToBIOMed Lab, Politecnico di Torino, 10129 Torino, Italy.

Gait analysis applications in clinics are still uncommon, for three main reasons: (1) the considerable time needed to prepare the subject for the examination; (2) the lack of user-independent tools; (3) the large variability of muscle activation patterns observed in healthy and pathological subjects. Numerical indices quantifying the muscle coordination of a subject could enable clinicians to identify patterns that deviate from those of a reference population and to follow the progress of the subject after surgery or completing a rehabilitation program. In this work, we present two user-independent indices. First, a muscle-specific index (MFI) that quantifies the similarity of the activation pattern of a muscle of a specific subject with that of a reference population. Second, a global index (GFI) that provides a score of the overall activation of a muscle set. These two indices were tested on two groups of healthy and pathological children with encouraging results. Hence, the two indices will allow clinicians to assess the muscle activation, identifying muscles showing an abnormal activation pattern, and associate a functional score to every single muscle as well as to the entire muscle set. These opportunities could contribute to facilitating the diffusion of surface EMG analysis in clinics.
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http://dx.doi.org/10.3390/s21217186DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8587884PMC
October 2021

Artificial intelligence for target prostate biopsy outcomes prediction the potential application of fuzzy logic.

Prostate Cancer Prostatic Dis 2021 Sep 3. Epub 2021 Sep 3.

Department of Oncology, Division of Urology, University of Turin, San Luigi Gonzaga Hospital, Orbassano, Turin, Italy.

Background: In current precision prostate cancer (PCa) surgery era the identification of the best patients candidate for prostate biopsy still remains an open issue. The aim of this study was to evaluate if the prostate target biopsy (TB) outcomes could be predicted by using artificial intelligence approach based on a set of clinical pre-biopsy.

Methods: Pre-biopsy characteristics in terms of PSA, PSA density, digital rectal examination (DRE), previous prostate biopsies, number of suspicious lesions at mp-MRI, lesion volume, lesion location, and Pi-Rads score were extracted from our prospectively maintained TB database from March 2014 to December 2019. Our approach is based on Fuzzy logic and associative rules mining, with the aim to predict TB outcomes.

Results: A total of 1448 patients were included. Using the Frequent-Pattern growth algorithm we extracted 875 rules and used to build the fuzzy classifier. 963 subjects were classified whereas for the remaining 484 subjects were not classified since no rules matched with their input variables. Analyzing the classified subjects we obtained a specificity of 59.2% and sensitivity of 90.8% with a negative and the positive predictive values of 81.3% and 76.6%, respectively. In particular, focusing on ISUP ≥ 3 PCa, our model is able to correctly predict the biopsy outcomes in 98.1% of the cases.

Conclusions: In this study we demonstrated that the possibility to look at several pre-biopsy variables simultaneously with artificial intelligence algorithms can improve the prediction of TB outcomes, outclassing the performance of PSA, its derivates and MRI alone.
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http://dx.doi.org/10.1038/s41391-021-00441-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8413110PMC
September 2021

Simulation of the Impact on the Workload of the Enlargement of the Clinical Staff of a Specialistic Reference Center.

Stud Health Technol Inform 2021 May;281:605-609

Department of Electronics and Telecommunications, Politecnico di Torino, Italy.

Quality of care and patient satisfaction are important aspects of high standard care. If clinical staff is subject to an elevated workload there is a possible decrease of both. This justifies the development of tools to quantify the workload and to find organizational changes that will normalize it. We have previously developed a simulation system to quantify the workload of the staff working in a regional reference center for the treatment of bleeding and hemorrhagic disorders. The goal of this new work is to simulate, through an agent-based model, the impact of adding a physician to the staff. Ten sets of initial parameters were defined to simulate ten typical weeks. Results show that the introduction of the new physician together with a second ambulatory room can reduce the workload of all the staff to the expected 8-hour. In this situation, in which the staff workload does not exceed the daily capacity, we may suppose that an increase in the quality of care and patient satisfaction will be possible.
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http://dx.doi.org/10.3233/SHTI210242DOI Listing
May 2021

Implementing telemedicine for the management of benign urologic conditions: a single centre experience in Italy.

World J Urol 2021 Aug 1;39(8):3109-3115. Epub 2021 Jan 1.

Division of Urology, Department of Oncology, School of Medicine, San Luigi Hospital, University of Turin, Regione Gonzole 10, 10043, Orbassano, Turin, Italy.

Purpose: To assess the use of telemedicine with phone-call visits as a practical tool to follow-up with patients affected by urological benign diseases, whose clinic visits had been cancelled during the acute phase of the COVID-19 pandemic.

Methods: Patients were contacted via phone-call and a specific questionnaire was administered to evaluate the health status of these patients and to identify those who needed an "in-person" ambulatory visit due to the worsening of their condition. Secondarily, the patients' perception of a potential shift towards a "telemedicine" approach to the management of their condition and to indirectly evaluate their desire to return to "in-person" clinic visits.

Results: 607 were contacted by phone-call. 87.5% (531/607) of the cases showed stability of the symptoms so no clinic in-person or emergency visits were needed. 81.5% (495/607) of patients were more concerned about the risk of contagion than their urological condition. The median score for phone visit comprehensibility and ease of communication of exams was 5/5; whilst patients' perception of phone visits' usefulness was scored 4/5. 53% (322/607) of the interviewees didn't own the basic supports required to be able to perform a real telemedicine consult according to the required standards.

Conclusion: Telemedicine approach limits the number of unnecessary accesses to medical facilities and represents an important tool for the limitation of the risk of transmission of infectious diseases, such as COVID-19. However, infrastructures, health workers and patients should reach out to a computerization process to allow a wider diffusion of more advanced forms of telemedicine, such as televisit.
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http://dx.doi.org/10.1007/s00345-020-03536-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7775638PMC
August 2021

A Convolutional Neural Network based system for Colorectal cancer segmentation on MRI images.

Annu Int Conf IEEE Eng Med Biol Soc 2020 07;2020:1675-1678

The aim of the study is to present a new Convolutional Neural Network (CNN) based system for the automatic segmentation of the colorectal cancer. The algorithm implemented consists of several steps: a pre-processing to normalize and highlights the tumoral area, the classification based on CNNs, and a post-processing aimed at reducing false positive elements. The classification is performed using three CNNs: each of them classifies the same regions of interest acquired from three different MR sequences. The final segmentation mask is obtained by a majority voting. Performances were evaluated using a semi-automatic segmentation revised by an experienced radiologist as reference standard. The system obtained Dice Similarity Coefficient (DSC) of 0.60, Precision (Pr) of 0.76 and Recall (Re) of 0.55 on the testing set. After applying the leave-one-out validation, we obtained a median DSC=0.58, Pr=0.74, Re=0.54. The promising results obtained by this system, if validated on a larger dataset, could strongly improve personalized medicine.
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http://dx.doi.org/10.1109/EMBC44109.2020.9175804DOI Listing
July 2020

An innovative radiomics approach to predict response to chemotherapy of liver metastases based on CT images.

Annu Int Conf IEEE Eng Med Biol Soc 2020 07;2020:1339-1342

Liver metastases (mts) from colorectal cancer (CRC) can have different responses to chemotherapy in the same patient. The aim of this study is to develop and validate a machine learning algorithm to predict response of individual liver mts. 22 radiomic features (RF) were computed on pretreatment portal CT scans following a manual segmentation of mts. RFs were extracted from 7x7 Region of Interests (ROIs) that moved across the image by step of 2 pixels. Liver mts were classified as non-responder (R-) if their largest diameter increased more than 3 mm after 3 months of treatment and responder (R+), otherwise. Features selection (FS) was performed by a genetic algorithm and classification by a Support Vector Machine (SVM) classifier. Sensitivity, specificity, negative (NPV) and positive (PPV) predictive values were evaluated for all lesions in the training and validation sets, separately. On the training set, we obtained sensitivity of 86%, specificity of 67%, PPV of 89% and NPV of 61%, while, on the validation set, we reached a sensitivity of 73%, specificity of 47%, PPV of 64% and NPV of 57%. Specificity was biased by the low number of R- lesions on the validation set. The promising results obtained in the validation dataset should be extended to a larger cohort of patient to further validate our method.Clinical Relevance- to personalize treatment of patients with metastastic colorectal cancer, based on the likelihood of response to chemotherapy of each liver metastasis.
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http://dx.doi.org/10.1109/EMBC44109.2020.9176627DOI Listing
July 2020

Surface Electromyography Applied to Gait Analysis: How to Improve Its Impact in Clinics?

Front Neurol 2020 4;11:994. Epub 2020 Sep 4.

PoliToBIOMedLab, Politecnico di Torino, Turin, Italy.

Surface electromyography (sEMG) is the main non-invasive tool used to record the electrical activity of muscles during dynamic tasks. In clinical gait analysis, a number of techniques have been developed to obtain and interpret the muscle activation patterns of patients showing altered locomotion. However, the body of knowledge described in these studies is very seldom translated into routine clinical practice. The aim of this work is to analyze critically the key factors limiting the extensive use of these powerful techniques among clinicians. A thorough understanding of these limiting factors will provide an important opportunity to overcome limitations through specific actions, and advance toward an evidence-based approach to rehabilitation based on objective findings and measurements.
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http://dx.doi.org/10.3389/fneur.2020.00994DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7502709PMC
September 2020

Radiomics predicts response of individual HER2-amplified colorectal cancer liver metastases in patients treated with HER2-targeted therapy.

Int J Cancer 2020 12 14;147(11):3215-3223. Epub 2020 Sep 14.

Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy.

The aim of our study was to develop and validate a machine learning algorithm to predict response of individual HER2-amplified colorectal cancer liver metastases (lmCRC) undergoing dual HER2-targeted therapy. Twenty-four radiomics features were extracted after 3D manual segmentation of 141 lmCRC on pretreatment portal CT scans of a cohort including 38 HER2-amplified patients; feature selection was then performed using genetic algorithms. lmCRC were classified as nonresponders (R-), if their largest diameter increased more than 10% at a CT scan performed after 3 months of treatment, responders (R+) otherwise. Sensitivity, specificity, negative (NPV) and positive (PPV) predictive values in correctly classifying individual lesion and overall patient response were assessed on a training dataset and then validated on a second dataset using a Gaussian naïve Bayesian classifier. Per-lesion sensitivity, specificity, NPV and PPV were 89%, 85%, 93%, 78% and 90%, 42%, 73%, 71% respectively in the testing and validation datasets. Per-patient sensitivity and specificity were 92% and 86%. Heterogeneous response was observed in 9 of 38 patients (24%). Five of nine patients were carriers of nonresponder lesions correctly classified as such by our radiomics signature, including four of seven harboring only one nonresponder lesion. The developed method has been proven effective in predicting behavior of individual metastases to targeted treatment in a cohort of HER2 amplified patients. The model accurately detects responder lesions and identifies nonresponder lesions in patients with heterogeneous response, potentially paving the way to multimodal treatment in selected patients. Further validation will be needed to confirm our findings.
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http://dx.doi.org/10.1002/ijc.33271DOI Listing
December 2020

Agent-Based Modeling and Simulation of Care Delivery for Patients with Thrombotic and Bleeding Disorders.

Stud Health Technol Inform 2020 Jun;270:1193-1194

Department of Electronics and Telecommunications, Politecnico di Torino, Italy.

The quality of patients care delivery is thought to be strongly affected by the physicians' workload. In this study we present an Agent-Based model of the processes during a typical working day. We simulated the current scenario and a possible scenario concerning the introduction of a second ambulatory as a potential improvement in the center organization. Our results validated the reliability of the model and showed that the introduction of a second ambulatory averagely reduces the daily physician' workload.
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http://dx.doi.org/10.3233/SHTI200358DOI Listing
June 2020

Automatic genetic planning for volumetric modulated arc therapy: A large multi-centre validation for prostate cancer.

Radiother Oncol 2020 07 21;148:126-132. Epub 2020 Apr 21.

Department of Radiation Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.

Purpose: The first clinical genetic autoplanning algorithm (Genetic Planning Solution, GPS) was validated in ten radiotherapy centres for prostate cancer VMAT by comparison with manual planning (Manual).

Methods: Although there were large differences among centres in planning protocol, GPS was tuned with the data of a single centre and then applied everywhere without any centre-specific fine-tuning. For each centre, ten Manual plans were compared with autoGPS plans, considering dosimetric plan parameters and the Clinical Blind Score (CBS) resulting from blind clinician plan comparisons. AutoGPS plans were used as is, i.e. there was no patient-specific fine-tuning.

Results: For nine centres, all ten plans were clinically acceptable. In the remaining centre, only one plan was acceptable. For the 91% acceptable plans, differences between Manual and AutoGPS in target coverage were negligible. OAR doses were significantly lower in AutoGPS plans (p < 0.05); rectum D and D were reduced by 8.1% and 17.9%, bladder D and D by 5.9% and 10.3%. According to clinicians, 69% of the acceptable AutoGPS plans were superior to the corresponding Manual plan. In case of preferred Manual plans (31%), perceived advantages compared to autoGPS were minor. QA measurements demonstrated that autoGPS plans were deliverable. A quick configuration adjustment in the centre with unacceptable plans rendered 100% of plans acceptable.

Conclusion: A novel, clinically applied genetic autoplanning algorithm was validated in 10 centres for in total 100 prostate cancer patients. High quality plans could be generated at different centres without centre-specific algorithm tuning.
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http://dx.doi.org/10.1016/j.radonc.2020.04.020DOI Listing
July 2020

Applying Data Warehousing to a Phase III Clinical Trial From the Fondazione Italiana Linfomi Ensures Superior Data Quality and Improved Assessment of Clinical Outcomes.

JCO Clin Cancer Inform 2019 10;3:1-15

Division of Hematology, Azienda Ospedaliera SS Antonio e Biagio e Cesare Arrigo, Alessandria, Italy.

Purpose: Data collection in clinical trials is becoming complex, with a huge number of variables that need to be recorded, verified, and analyzed to effectively measure clinical outcomes. In this study, we used data warehouse (DW) concepts to achieve this goal. A DW was developed to accommodate data from a large clinical trial, including all the characteristics collected. We present the results related to baseline variables with the following objectives: developing a data quality (DQ) control strategy and improving outcome analysis according to the clinical trial primary end points.

Methods: Data were retrieved from the electronic case reporting forms (eCRFs) of the phase III, multicenter MCL0208 trial (ClinicalTrials.gov identifier: NCT02354313) of the Fondazione Italiana Linfomi for younger patients with untreated mantle cell lymphoma (MCL). The DW was created with a relational database management system. Recommended DQ dimensions were observed to monitor the activity of each site to handle DQ management during patient follow-up. The DQ management was applied to clinically relevant parameters that predicted progression-free survival to assess its impact.

Results: The DW encompassed 16 tables, which included 226 variables for 300 patients and 199,500 items of data. The tool allowed cross-comparison analysis and detected some incongruities in eCRFs, prompting queries to clinical centers. This had an impact on clinical end points, as the DQ control strategy was able to improve the prognostic stratification according to single parameters, such as tumor infiltration by flow cytometry, and even using established prognosticators, such as the MCL International Prognostic Index.

Conclusion: The DW is a powerful tool to organize results from large phase III clinical trials and to effectively improve DQ through the application of effective engineered tools.
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http://dx.doi.org/10.1200/CCI.19.00049DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6873907PMC
October 2019

Comparison of Different Sets of Features for Human Activity Recognition by Wearable Sensors.

Sensors (Basel) 2018 Nov 29;18(12). Epub 2018 Nov 29.

Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy.

Human Activity Recognition (HAR) refers to an emerging area of interest for medical, military, and security applications. However, the identification of the features to be used for activity classification and recognition is still an open point. The aim of this study was to compare two different feature sets for HAR. Particularly, we compared a set including time, frequency, and time-frequency domain features widely used in literature () with a set of time-domain features derived by considering the physical meaning of the acquired signals (). The comparison of the two sets were based on the performances obtained using four machine learning classifiers. Sixty-one healthy subjects were asked to perform seven different daily activities wearing a MIMU-based device. Each signal was segmented using a 5-s window and for each window, 222 and 221 variables were extracted for the and respectively. Each set was reduced using a Genetic Algorithm (GA) simultaneously performing feature selection and classifier optimization. Our results showed that Support Vector Machine achieved the highest performances using both sets (97.1% and 96.7% for and respectively). However, allows to better understand alterations of the biomechanical behavior in more complex situations, such as when applied to pathological subjects.
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http://dx.doi.org/10.3390/s18124189DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6308535PMC
November 2018

Modelling and analysis of four telemedicine Italian experiences.

Annu Int Conf IEEE Eng Med Biol Soc 2017 Jul;2017:2634-2637

In the last 10 years the European population aged 65 years and over grew of 2.3%, with Italy having the highest share of elderly persons in the total population. OPLON (OPportunities for active and healthy LONgevity) is a project supported by the Italian Ministry of Education, Universities, and Research aiming to identify and prevent frailty and to improve the life quality of elderly subjects. The main goal of OPLON is to develop a "Care&Cure" model for the management of subjects with different morbidities and co-morbidities, adaptable to the subject's risk level and to the regional contexts. In this study we analyzed four Italian telemedicine experiences addressed to chronic, geriatric or partially self-sufficient subjects. Each of them was exhaustively described by means of three process modelling tools (synopsis, workflow and swimlane activity diagrams). Starting from this analysis, we defined a general model of tele-monitoring and tele-assistance of frail and pre-frail people with different needs and pathologies. The proposed model was characterized by three macro processes (enrollment, assessment and assistance) and four groups of actors (patient, general practitioner/specialist physician, multidisciplinary team, and healthcare professionals). Combining this model with a detailed analysis of regulations and legislations in force both at local and national level, it will be possible to design the complete and efficient "Care&Cure" model.
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http://dx.doi.org/10.1109/EMBC.2017.8037398DOI Listing
July 2017

Estimation of joint position error.

Annu Int Conf IEEE Eng Med Biol Soc 2017 Jul;2017:2474-2477

Joint position error (JPE) is frequently used to assess proprioception in rehabilitation and sport science. During position-reposition tests the subject is asked to replicate a specific target angle (e.g. 30° of knee flexion) for a specific number of times. The aim of this study is to find an effective method to estimate JPE from the joint kinematic signal. Forty healthy subjects were tested to assess knee joint position sense. Three different methods of JPE estimation are described and compared using a hierarchical clustering approach. Overall, the 3 methods showed a high degree of similarity, ranging from 88% to 100%. We concluded that it is preferable to use the more user-independent method, in which the operator does not have to manually place "critical" markers.
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http://dx.doi.org/10.1109/EMBC.2017.8037358DOI Listing
July 2017

Data quality improvement of a multicenter clinical trial dataset.

Annu Int Conf IEEE Eng Med Biol Soc 2017 Jul;2017:1190-1193

Medical datasets are usually affected by several problems, such as missing values, inconsistencies, redundancies, that can influence the data mining process and the extraction of useful knowledge. For these reasons, a preprocessing phase should be performed for improving the overall quality of data and, consequently, of the information that may be discovered from them. In this study we applied five steps of data preprocessing to improve the quality of a large dataset derived from a multicenter clinical trial. Our dataset included 298 patients enrolled in a prospective, multicenter, clinical trial, characterized by 22 input variables and one class variable (MIPI value). In particular, data coming from different medical centers were firstly integrated to obtain a homogeneous dataset. The latter was normalized to scale all variables into smaller and similar intervals. Then, all missing values were estimated by means of an imputation step. The complete dataset was finally discretized and reduced to remove redundant variables and decrease the amount of data to be managed. The improvement of data quality after each step was evaluated by means of the patients' classification accuracy using the KNN classifier. Our results showed that the proposed pipeline produced an increment of more than 20% of the classification performances. Moreover, the highest growth of accuracy was obtained after missing value imputation, whereas the discretization and feature selection steps allowed for a significant reduction of variables to be managed, without any deterioration of the information contained in data.
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http://dx.doi.org/10.1109/EMBC.2017.8037043DOI Listing
July 2017

Muscle contractions in cyclic movements: Optimization of CIMAP algorithm.

Annu Int Conf IEEE Eng Med Biol Soc 2017 Jul;2017:58-61

During cyclic movements, the number of muscle activations and their timing are different from cycle to cycle. In a previous study, the CIMAP algorithm was proposed for grouping cycles showing similar EMG activation intervals, using dendrogram clustering. Even if the algorithm demonstrated good performances on a healthy population, the intra-cluster variability decreased when applied to datasets from pathological subjects. In this work we propose an optimized version of the CIMAP, comparing the performances of 8 different combinations of parameters used for the dendrogram construction. The cut-off point is also modified. The new and the original version of the algorithm are compared, in terms of intra-cluster variability, considering a population of 60 subjects, both healthy and pathological. The results show that the new CIMAP allows for obtaining clusters with lower variability with respect to the original version of the algorithm (p <; 0.001).
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http://dx.doi.org/10.1109/EMBC.2017.8036762DOI Listing
July 2017

Frequency-of-occurrence of myoelectric patterns to evaluate gait motor control strategies after hip replacement surgery.

Annu Int Conf IEEE Eng Med Biol Soc 2016 Aug;2016:387-390

Gait alterations are observed even years after hip replacement surgery. Such long-lasting alterations may arise from a global change of the motor control strategies. The aim of this work is to investigate the changes in gait motor control strategies of patients after hip replacement surgery by analyzing the frequency of occurrence (OF) of myoelectric activation patterns. We studied five lower limb muscles during gait, in hip prosthesis patients and controls. We found that patients adopt a motor control strategy that tends to prefer "simplified" myoelectric patterns, showing a smaller number of activations within the gait cycle. This altered motor control was present both on the prosthesis and the sound side, and did not improve during the 12-month follow-up. The reduced number of activations is even more evident in lateral hamstrings and gluteus medius, which are the muscles more affected by hip replacement surgery. Furthermore, this study demonstrated that the OF is a sensitive parameter able to discover subtle changes in motor control strategies. Its use can be extended to other studies involving motor coordination, motor learning and motor control adaptations.
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http://dx.doi.org/10.1109/EMBC.2016.7590721DOI Listing
August 2016

Comprehensive framework for preventive maintenance priority of medical equipment.

Annu Int Conf IEEE Eng Med Biol Soc 2015 Aug;2015:1227-30

Throughout the medical equipment life cycle, preventive maintenance is considered one of the most important stages that should be managed properly. However, the need for better management and control by giving a reasonable prioritization for preventive maintenance becomes essential. The purpose of this study is to develop a comprehensive framework for preventive maintenance priority of medical equipment using Quality Function Deployment (QFD) and Fuzzy Logic (FL). The quality function deployment is proposed in order to identify the most important criteria that could impact preventive maintenance priority decision; meanwhile the role of the fuzzy logic is to generate a priority index of the list of equipment considering those criteria. The model validation was carried out on 140 pieces of medical equipment belonging to two hospitals. In application, we propose to classify the priority index into five classes. The results indicate that the strong correlation existence between risk-based criteria and preventive maintenance priority decision.
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http://dx.doi.org/10.1109/EMBC.2015.7318588DOI Listing
August 2015

Preventive maintenance prioritization index of medical equipment using quality function deployment.

IEEE J Biomed Health Inform 2015 May 10;19(3):1029-35. Epub 2014 Jul 10.

Preventive maintenance is a core function of clinical engineering, and it is essential to guarantee the correct functioning of the equipment. The management and control of maintenance activities are equally important to perform maintenance. As the variety of medical equipment increases, accordingly the size of maintenance activities increases, the need for better management and control become essential. This paper aims to develop a new model for preventive maintenance priority of medical equipment using quality function deployment as a new concept in maintenance of medical equipment. We developed a three-domain framework model consisting of requirement, function, and concept. The requirement domain is the house of quality matrix. The second domain is the design matrix. Finally, the concept domain generates a prioritization index for preventive maintenance considering the weights of critical criteria. According to the final scores of those criteria, the prioritization action of medical equipment is carried out. Our model proposes five levels of priority for preventive maintenance. The model was tested on 200 pieces of medical equipment belonging to 17 different departments of two hospitals in Piedmont province, Italy. The dataset includes 70 different types of equipment. The results show a high correlation between risk-based criteria and the prioritization list.
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http://dx.doi.org/10.1109/JBHI.2014.2337895DOI Listing
May 2015

Segmentation and classification of gait cycles.

IEEE Trans Neural Syst Rehabil Eng 2014 Sep 26;22(5):946-52. Epub 2013 Nov 26.

Gait abnormalities can be studied by means of instrumented gait analysis. Foot-switches are useful to study the foot-floor contact and for timing the gait phases in many gait disorders, provided that a reliable foot-switch signal may be collected. Considering long walks allows reducing the intra-subject variability, but requires automatic and user-independent methods to analyze a large number of gait cycles. The aim of this work is to describe and validate an algorithm for the segmentation of the foot-switch signal and the classification of the gait cycles. The performance of the algorithm was assessed comparing its results against the manual segmentation and classification performed by a gait analysis expert on the same signal. The performance was found to be equal to 100% for healthy subjects and over 98% for pathological subjects. The algorithm allows determining the atypical cycles (cycles that do not match the standard sequence of gait phases) for many different kinds of pathological gait, since it is not based on pathology-specific templates.
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http://dx.doi.org/10.1109/TNSRE.2013.2291907DOI Listing
September 2014

Fuzzy logic applied to a Patient Classification System.

Annu Int Conf IEEE Eng Med Biol Soc 2013 ;2013:1310-3

The optimization of the clinical staff resources is a very complicated task that can be supported by a set of tools called Patient Classification Systems (PCS). These methods allow the evaluation of the correct number of nurses and healthcare workers needed in order to guarantee an appropriate care level.
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http://dx.doi.org/10.1109/EMBC.2013.6609749DOI Listing
August 2015

A multi-agent system for monitoring patient flow.

Stud Health Technol Inform 2013 ;192:944

Biolab, Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, Torino, Italy.

Patient flow within a healthcare facility may follow different and, sometimes, complicated paths. Each path phase is associated with the documentation of the activities carried out during it and may require the consultation of clinical guidelines, medical literature and the use of specific software and decision aid systems. In this study we present the design of a Patient Flow Management System (PFMS) based on Multi Agent Systems (MAS) methodology. System requirements were identified by means of process modeling tools and a MAS consisting of six agents was designed and is under construction. Its main goal is to support both the medical staff during the health care process and the hospital managers in assuring that all the required documentation is completed and available. Moreover, such a tool can be used for the assessment and comparison of different clinical pathways, in order to identify possible improvementsand the optimum patient flow.
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April 2015

Feature selection applied to ultrasound carotid images segmentation.

Annu Int Conf IEEE Eng Med Biol Soc 2011 ;2011:5161-4

BioLab, Department of Electronics, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy.

The automated tracing of the carotid layers on ultrasound images is complicated by noise, different morphology and pathology of the carotid artery. In this study we benchmarked four methods for feature selection on a set of variables extracted from ultrasound carotid images. The main goal was to select those parameters containing the highest amount of information useful to classify the pixels in the carotid regions they belong to. Six different classes of pixels were identified: lumen, lumen-intima interface, intima-media complex, media-adventitia interface, adventitia and adventitia far boundary. The performances of QuickReduct Algorithm (QRA), Entropy-Based Algorithm (EBR), Improved QuickReduct Algorithm (IQRA) and Genetic Algorithm (GA) were compared using Artificial Neural Networks (ANNs). All methods returned subsets with a high dependency degree, even if the average classification accuracy was about 50%. Among all classes, the best results were obtained for the lumen. Overall, the four methods for feature selection assessed in this study return comparable results. Despite the need for accuracy improvement, this study could be useful to build a pre-classifier stage for the optimization of segmentation performance in ultrasound automated carotid segmentation.
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http://dx.doi.org/10.1109/IEMBS.2011.6091278DOI Listing
June 2012

A multi agent system model for evaluating quality service of Clinical Engineering Department.

Annu Int Conf IEEE Eng Med Biol Soc 2011 ;2011:1209-12

Department, Politecnico di Torino, Torino, Italy.

Biomedical technology is strategically important to the operational effectiveness of healthcare facilities. As a consequence, clinical engineers have become an essential figure in hospital environment: their role in maintenance, support, evaluation, integration, assessment of new, advanced and complex technologies in point of view of patient safety and cost reduction is become inalienable. For this reason, nations have begun to establish Clinical Engineering Department, but, unfortunately, in a very diversified and fragmented way. So, a tool able to evaluate and improve the quality of current services is needed. Hence, this work builds a model that acts as a reference tool in order to assess the quality of an existing Clinical Engineering Department, underlining its defaulting aspects and suggesting improvements.
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http://dx.doi.org/10.1109/IEMBS.2011.6090284DOI Listing
June 2012

Role of fuzzy pre-classifier for high performance LI/MA segmentation in B-mode longitudinal carotid ultrasound images.

Annu Int Conf IEEE Eng Med Biol Soc 2010 ;2010:4719-22

BioLab, Department of Electronics, Politecnico di Torino, Italy.

The automated segmentation of the carotid artery wall from ultrasound images is required for an accurate measurement of the artery intima-media thickness. Segmentation accuracy of automated techniques is usually limited by noise and artifacts. In 2005, the authors developed a methodology called CULEX whose performance was noise sensitive. The final stage of CULEX segmentation was fuzzy clustering of the pixels, to detect the lumen-intima (LI) and media-adventitia (MA) carotid wall interfaces. In this paper, we show the effect of a fuzzy Mamdani-type pre-classifier used to improve the segmentation performance. Thanks to the Mamdami fuzzy pre-classifier, we optimized the de-fuzzyfication threshold, increasing the LI and MA performance by 62% and 3.5%, respectively. The obtained segmentation errors (55.6 ± 69.4 microm for LI and 34.4 ± 24.4 microm for MA), validated against human tracings and on a 200-images dataset containing a mixture of healthy and plaque vessels.
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http://dx.doi.org/10.1109/IEMBS.2010.5626390DOI Listing
March 2011

A self-organizing map based morphological analysis of oral glucose tolerance test curves in women with gestational diabetes mellitus.

Stud Health Technol Inform 2010 ;160(Pt 2):1145-9

Dipartimento di Meccanica, Politecnico di Torino, Italy.

Gestational diabetes mellitus (GDM) makes women at risk of type 2 diabetes during their life. In order to predict this later abnormal glucose intolerance, several antepartum and postpartum predictors have been identified. In this study we conjecture that future evolution is predictable from morphology of the oral glucose tolerance test (OGTT) curves at baseline. To test our hypothesis, as a first step we evaluated the association between the curve morphologies of normal and diabetic patient condition at baseline. In particular, we analysed glucose and insulin curves of a group of women with a history of GDM. A Self-organizing map (SOM) was proposed to evaluate shape differences among control, normal, impaired glucose tolerance and diabetic curves shape. We compared our results with the currently applied clinical classification. We found that morphology contains information about the current status of the patient, because the SOM analysis clearly allows to discriminate subjects belonging to healthy or diabetic group. Moreover, SOMs highlighted additional information that could be used for prognostic purposes.
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April 2011

A model for simulation of Clinical Engineering Department activities.

Annu Int Conf IEEE Eng Med Biol Soc 2008 ;2008:5109-12

Dipartimento di Elettronica Politecnico di Torino, Italy.

Clinical Engineering (CE) Departments are in charge of healthcare technology management in healthcare facilities. The workload is proportional to the number of activities, the number and complexity of biomedical instrumentation, and the technology intensity of the facility. Clinical engineers and Biomedical equipment technicians work together in order to perform the different activities and to obtain customer satisfaction. This paper describes a model that can be used to estimate the number of engineers and technicians required to start a new CE Department. The estimation is obtained by means of simulation. Starting by several inputs that describe the facility and the quantity and characteristics of the instruments the model is able to provide the number of Clinical engineers and Biomedical equipment technicians.
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http://dx.doi.org/10.1109/IEMBS.2008.4650363DOI Listing
May 2009

AHP for the acquisition of biomedical instrumentation.

Annu Int Conf IEEE Eng Med Biol Soc 2007 ;2007:3581-4

Dipartimento di Elettronica, Politecnico di Torino, Torino, corso Duca degli Abruzzi 24, Italy.

Health technology management consists of several decision processes including the acquisition of new technology. The purchasing of a new device requires the selection of one among several products taking into account different criteria. When the technology is characterized by large amount of parameters the choice becomes problematical and a support tool is needed. In 2003 Sloane et al. published a study in which they demonstrated the potentialities of the Analytic Hierarchy Process (AHP) to support the selection of a biomedical instrumentation. The work presented here describes the application of AHP to support the quality assessment for the selection of pacemakers and implantable defibrillators and shows that the method is indeed very appropriate for that task.
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http://dx.doi.org/10.1109/IEMBS.2007.4353105DOI Listing
March 2008

Fuzzy Classifier based on Muscle Fatigue Parameters.

Conf Proc IEEE Eng Med Biol Soc 2005;2005:2421-4

Politecnico di Torino, Torino, 10129, Italy (e-mail:

This paper presents the development of a decision aid tool based on a fuzzy classifier. The goal was to obtain a system that could support a physician who have to make decisions about how to deal with the progression of the disease of a child affected by Duchenne Muscular Dystrophy. First, we used an outranking multicriteria method to select among the possible parameters of muscle fatigue evaluation the subset that provide more reliable information, than we used the selected parameters as membership functions for the fuzzy classifier. The output of the fuzzy classifier consisted of three classes: 1-"close to normal results", 2-"results compatible with moderate pathological conditions", and 3-"results congruent with severe pathological conditions". A first test of the classifier was performed using the data of the twenty examinations of six children and it provided good results. We believe that these results are relevant to the clinical applications and they can be easily extended to different pathologies.
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http://dx.doi.org/10.1109/IEMBS.2005.1616957DOI Listing
October 2012

Do we have the data to take informed decisions in Healthcare Technology Management (HTM) related issues? A Conceptual HTM-IS Framework.

Conf Proc IEEE Eng Med Biol Soc 2005;2005:7099-102

HTM Programme Director at the University of Cape Town, South Africa (e-mail:

This paper considers the data and information needs for a broad range of environments, focusing on information-poor environments that may be well resourced in other respects. A generic framework for the establishment of a comprehensive healthcare technology management information system (HTMIS) is suggested.
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http://dx.doi.org/10.1109/IEMBS.2005.1616142DOI Listing
October 2012
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