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Convolutional neural networks to identify malformations of cortical development: A feasibility study.

Seizure 2021 May 31;91:81-90. Epub 2021 May 31.

Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, MA USA.

Objective: To develop and test a deep learning model to automatically detect malformations of cortical development (MCD).

Methods: We trained a deep learning model to distinguish between diffuse cortical malformation (CM), periventricular nodular heterotopia (PVNH), and normal magnetic resonance imaging (MRI). We trained 4 different convolutional neural network (CNN) architectures. Read More

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The usefulness of ultrasound before induction of labor.

Am J Obstet Gynecol MFM 2021 Jun 12:100423. Epub 2021 Jun 12.

Obstetric Unit, Department of Medical and Surgical Sciences, University of Bologna and IRCCS Azienda Ospedaliero-Universitaria S.Orsola-Malpighi, Bologna, Italy.

The indications for induction of labor have been consistently on the rise. These indications are mainly medical (maternal or fetal), social or indications that are related to convenience or maternal preferences. With the increase in prevalence of these indications, the incidence rates of induction of labor are expected to continue to rise. Read More

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A multicenter study to develop a non-invasive radiomic model to identify urinary infection stone in vivo using machine-learning.

Kidney Int 2021 Jun 12. Epub 2021 Jun 12.

Department of Pharmacy, the First People's Hospital of Kashi Prefecture; Affiliated Kashi Hospital of Sun Yat-Sen University, Kashi, People's Republic of China. Electronic address:

Urolithiasis is a common urological disease, and treatment strategy options vary between different stone types. However, accurate detection of stone composition can only be performed in vitro. The management of infection stones is particularly challenging with yet no effective approach to pre-operatively identify infection stones from non-infection stones. Read More

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Anatomically aided PET image reconstruction using deep neural networks.

Med Phys 2021 Jun 15. Epub 2021 Jun 15.

Department of Biomedical Engineering, University of California, Davis, CA, USA.

Purpose: The developments of PET/CT and PET/MR scanners provide opportunities for improving PET image quality by using anatomical information. In this paper, we propose a novel co-learning 3D convolutional neural network (CNN) to extract modality-specific features from PET/CT image pairs and integrate complementary features into an iterative reconstruction framework to improve PET image reconstruction.

Methods: We used a pre-trained deep neural network to represent PET images. Read More

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Unraveling the Interplay of Image Formation, Data Representation and Learning in CT-based COPD Phenotyping Automation: The Need for a Meta-Strategy.

Med Phys 2021 Jun 15. Epub 2021 Jun 15.

Siemens Healthineers, CT R&D Image Analytics, Forchheim, Germany.

Purpose: In the literature on automated phenotyping of Chronic Obstructive Pulmonary Disease (COPD), there is a multitude of isolated classical machine learning and deep learning techniques, mostly investigating individual phenotypes, with small study cohorts and heterogeneous meta-parameters, e.g. different scan protocols or segmented regions. Read More

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A Knowledge Distillation Ensemble Framework for Predicting Short and Long-term Hospitalisation Outcomes from Electronic Health Records Data.

IEEE J Biomed Health Inform 2021 Jun 15;PP. Epub 2021 Jun 15.

The ability to perform accurate prognosis of patients is crucial for proactive clinical decision making, informed resource management and personalised care. Existing outcome prediction models suffer from a low recall of infrequent positive outcomes. We present a highly-scalable and robust machine learning framework to automatically predict adversity represented by mortality and ICU admission from time-series vital signs and laboratory results obtained within the first 24 hours of hospital admission. Read More

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Initial transperineal prostate biopsy experience at a high-volume center.

Can J Urol 2021 Jun;28(3):10692-10698

Department of Urology, Lahey Hospital and Medical Center, Burlington, Massachusetts, USA.

Introduction: Transperineal prostate biopsy (TPBx) allows for prostate cancer detection with fewer infectious complications when compared to transrectal prostate biopsy (TRUSBx). We evaluated the initial experience of a single physician with no prior TPBx exposure, compared to TRUSBx and MRI/US fusion biopsy (MRIBx) performed by experienced physicians.

Materials And Methods: All consecutive patients undergoing prostate biopsy (June 2019-March 2020) were included. Read More

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A comparative of a single novice surgeon's direct anterior approach and posterior approach learning curves in total hip arthroplasty: a retrospective cohort study.

Eur J Orthop Surg Traumatol 2021 Jun 15. Epub 2021 Jun 15.

Hip Surgery Unit, Orthopaedic Surgery Department, Vall d'Hebron University Hospital, Universitat Autónoma de Barcelona Departament de Cirurgia, Pg. Vall d'Hebron 119-129, 08035, Barcelona, Spain.

Introduction: The purpose of the present study was to compare a novice surgeon's learning curves with the direct anterior approach and posterior approach in total hip arthroplasty.

Methods: A consecutive series of 376 total hip arthroplasties performed from November 2014 to September 2019 in a level-one healthcare center by a single surgeon (V.B) were retrospectively studied. Read More

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Can robotic technology mitigate the learning curve of total hip arthroplasty?

Bone Jt Open 2021 Jun;2(6):365-370

Department of Orthopaedic Surgery, Ochsner Clinic Foundation, New Orleans, Louisiana, USA.

Aims: Traditionally, acetabular component insertion during total hip arthroplasty (THA) is visually assisted in the posterior approach and fluoroscopically assisted in the anterior approach. The present study examined the accuracy of a new surgeon during anterior (NSA) and posterior (NSP) THA using robotic arm-assisted technology compared to two experienced surgeons using traditional methods.

Methods: Prospectively collected data was reviewed for 120 patients at two institutions. Read More

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Clinically applicable artificial intelligence algorithm for the diagnosis, evaluation, and monitoring of acute retinal necrosis.

J Zhejiang Univ Sci B 2021 Jun;22(6):504-511

Eye Center, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China.

The prompt detection and proper evaluation of necrotic retinal region are especially important for the diagnosis and treatment of acute retinal necrosis (ARN). The potential application of artificial intelligence (AI) algorithms in these areas of clinical research has not been reported previously. The present study aims to create a computational algorithm for the automated detection and evaluation of retinal necrosis from retinal fundus photographs. Read More

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Identification of transdiagnostic psychiatric disorder subtypes using unsupervised learning.

Neuropsychopharmacology 2021 Jun 14. Epub 2021 Jun 14.

Max Planck Institute of Psychiatry, Munich, Germany.

Psychiatric disorders show heterogeneous symptoms and trajectories, with current nosology not accurately reflecting their molecular etiology and the variability and symptomatic overlap within and between diagnostic classes. This heterogeneity impedes timely and targeted treatment. Our study aimed to identify psychiatric patient clusters that share clinical and genetic features and may profit from similar therapies. Read More

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The learning curve of laparoscopic ablation of liver tumors: A technically demanding procedure requiring dedicated training.

Eur J Surg Oncol 2021 May 20. Epub 2021 May 20.

Division of HPB, Minimally Invasive and Robotic Surgery, Department of Clinical Medicine and Surgery, Federico II University, Naples, Italy. Electronic address:

Background: Laparoscopic ablation (LA) of liver tumors is an increasingly performed procedure. However, LA is technically demanding, with inherent difficulties making LA more complex than percutaneous and open surgery ablations. This study aimed to characterize the learning curve (LC) of LAs. Read More

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A new approach for interpretability and reliability in clinical risk prediction: Acute coronary syndrome scenario.

Artif Intell Med 2021 Jul 13;117:102113. Epub 2021 May 13.

Cardiology Department, Leiria Hospital Centre, Leiria, Portugal. Electronic address:

Introduction: The risk prediction of the occurrence of a clinical event is often based on conventional statistical procedures, through the implementation of risk score models. Recently, approaches based on more complex machine learning (ML) methods have been developed. Despite the latter usually have a better predictive performance, they obtain little approval from the physicians, as they lack interpretability and, therefore, clinical confidence. Read More

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Improving clinical outcome predictions using convolution over medical entities with multimodal learning.

Artif Intell Med 2021 Jul 13;117:102112. Epub 2021 May 13.

Department of Computer Engineering, TOBB University of Economics and Technology, Ankara, Turkey. Electronic address:

Early prediction of mortality and length of stay (LOS) of a patient is vital for saving a patient's life and management of hospital resources. Availability of Electronic Health Records (EHR) makes a huge impact on the healthcare domain and there are several works on predicting clinical problems. However, many studies did not benefit from the clinical notes because of the sparse, and high dimensional nature. Read More

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Prediction of weaning from mechanical ventilation using Convolutional Neural Networks.

Artif Intell Med 2021 Jul 5;117:102087. Epub 2021 May 5.

Department of Computer Science, University of York, York, UK.

Weaning from mechanical ventilation covers the process of liberating the patient from mechanical support and removing the associated endotracheal tube. The management of weaning from mechanical ventilation comprises a significant proportion of the care of critically ill intubated patients in Intensive Care Units (ICUs). Both prolonged dependence on mechanical ventilation and premature extubation expose patients to an increased risk of complications and increased health care costs. Read More

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GPS driving: a digital biomarker for preclinical Alzheimer disease.

Alzheimers Res Ther 2021 Jun 14;13(1):115. Epub 2021 Jun 14.

Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA.

Background: Alzheimer disease (AD) is the most common cause of dementia. Preclinical AD is the period during which early AD brain changes are present but cognitive symptoms have not yet manifest. The presence of AD brain changes can be ascertained by molecular biomarkers obtained via imaging and lumbar puncture. Read More

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Using deep learning to identify recent positive selection in malaria parasite sequence data.

Malar J 2021 Jun 14;20(1):270. Epub 2021 Jun 14.

London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.

Background: Malaria, caused by Plasmodium parasites, is a major global public health problem. To assist an understanding of malaria pathogenesis, including drug resistance, there is a need for the timely detection of underlying genetic mutations and their spread. With the increasing use of whole-genome sequencing (WGS) of Plasmodium DNA, the potential of deep learning models to detect loci under recent positive selection, historically signals of drug resistance, was evaluated. Read More

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Machine Learning Model for Predicting Postoperative Survival of Patients with Colorectal Cancer.

Cancer Res Treat 2021 Jun 15. Epub 2021 Jun 15.

Department of Surgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.

Purpose: Machine learning (ML) is a strong candidate for making accurate predictions, as we can use large amount of data with powerful computational algorithms. We developed a ML based model to predict survival of patients with colorectal cancer (CRC) using data from 2 independent datasets.

Materials And Methods: A total of 364,316 and 1,572 CRC patients were included from the Surveillance, Epidemiology, and End results (SEER) and a Korean dataset, respectively. Read More

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Target identification among known drugs by deep learning from heterogeneous networks.

Chem Sci 2020 Jan 13;11(7):1775-1797. Epub 2020 Jan 13.

Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic 9500 Euclid Avenue Cleveland OH 44106 USA +1-216-6361609 +1-216-4447654.

Without foreknowledge of the complete drug target information, development of promising and affordable approaches for effective treatment of human diseases is challenging. Here, we develop deepDTnet, a deep learning methodology for new target identification and drug repurposing in a heterogeneous drug-gene-disease network embedding 15 types of chemical, genomic, phenotypic, and cellular network profiles. Trained on 732 U. Read More

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January 2020

Automated classification of coronary atherosclerotic plaque in optical frequency domain imaging based on deep learning.

Atherosclerosis 2021 Jun 7;328:100-105. Epub 2021 Jun 7.

Division of Cardiology, Department of Medicine II, Kansai Medical University, Hirakata, Japan.

Background And Aims: We developed a deep learning (DL) model for automated atherosclerotic plaque categorization using optical frequency domain imaging (OFDI) and performed quantitative and visual evaluations.

Methods: A total of 1103 histological cross-sections from 45 autopsy hearts were examined to compare the ex vivo OFDI scans. The images were segmented and annotated considering four histological categories: pathological intimal thickening (PIT), fibrous cap atheroma (FA), fibrocalcific plaque (FC), and healed erosion/rupture (HER). Read More

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Evaluation of Computer-Aided Nodule Assessment and Risk Yield (CANARY) in Korean patients for prediction of invasiveness of ground-glass opacity nodule.

PLoS One 2021 14;16(6):e0253204. Epub 2021 Jun 14.

Department of Thoracic and Cardiovascular Surgery, Yonsei University College of Medicine, Seoul, Korea.

Differentiating the invasiveness of ground-glass nodules (GGN) is clinically important, and several institutions have attempted to develop their own solutions by using computed tomography images. The purpose of this study is to evaluate Computer-Aided Analysis of Risk Yield (CANARY), a validated virtual biopsy and risk-stratification machine-learning tool for lung adenocarcinomas, in a Korean patient population. To this end, a total of 380 GGNs from 360 patients who underwent pulmonary resection in a single institution were reviewed. Read More

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Detection of myocardial ischemia by intracoronary ECG using convolutional neural networks.

PLoS One 2021 14;16(6):e0253200. Epub 2021 Jun 14.

Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.

Introduction: The electrocardiogram (ECG) is a valuable tool for the diagnosis of myocardial ischemia as it presents distinctive ischemic patterns. Deep learning methods such as convolutional neural networks (CNN) are employed to extract data-derived features and to recognize natural patterns. Hence, CNN enable an unbiased view on well-known clinical phenomenon, e. Read More

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Dictionary learning constrained direct parametric estimation in dynamic myocardial perfusion PET.

IEEE Trans Med Imaging 2021 Jun 14;PP. Epub 2021 Jun 14.

In myocardial perfusion imaging with dynamic positron emission tomography (PET), direct parametric reconstruction from the projection data allows accurate modeling of the Poisson noise in the projection domain to provide more reliable estimate of the parametric images. In this study, we propose to incorporate a superior denoiser to efficiently suppress the unfavorable noise propagation during the direct reconstruction. The dictionary learning (DL) based sparse representation serves as a regularization term to constrain the intermediate K1 estimation. Read More

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Developing an Explainable Machine Learning-Based Personalised Dementia Risk Prediction Model: A Transfer Learning Approach With Ensemble Learning Algorithms.

Front Big Data 2021 26;4:613047. Epub 2021 May 26.

Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh Medical School, Edinburgh, United Kingdom.

Alzheimer's disease (AD) has its onset many decades before dementia develops, and work is ongoing to characterise individuals at risk of decline on the basis of early detection through biomarker and cognitive testing as well as the presence/absence of identified risk factors. Risk prediction models for AD based on various computational approaches, including machine learning, are being developed with promising results. However, these approaches have been criticised as they are unable to generalise due to over-reliance on one data source, poor internal and external validations, and lack of understanding of prediction models, thereby limiting the clinical utility of these prediction models. Read More

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Preoperative Prediction of Meningioma Consistency Machine Learning-Based Radiomics.

Front Oncol 2021 26;11:657288. Epub 2021 May 26.

Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.

Objectives: The aim of this study was to establish and validate a radiomics nomogram for predicting meningiomas consistency, which could facilitate individualized operation schemes-making.

Methods: A total of 172 patients was enrolled in the study (train cohort: 120 cases, test cohort: 52 cases). Tumor consistency was classified as soft or firm according to Zada's consistency grading system. Read More

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Uncontrolled Confounders May Lead to False or Overvalued Radiomics Signature: A Proof of Concept Using Survival Analysis in a Multicenter Cohort of Kidney Cancer.

Front Oncol 2021 27;11:638185. Epub 2021 May 27.

Department of Radiology, Columbia University Irving Medical Center, New York, NY, United States.

Purpose: We aimed to explore potential confounders of prognostic radiomics signature predicting survival outcomes in clear cell renal cell carcinoma (ccRCC) patients and demonstrate how to control for them.

Materials And Methods: Preoperative contrast enhanced abdominal CT scan of ccRCC patients along with pathological grade/stage, gene mutation status, and survival outcomes were retrieved from The Cancer Imaging Archive (TCIA)/The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) database, a publicly available dataset. A semi-automatic segmentation method was applied to segment ccRCC tumors, and 1,160 radiomics features were extracted from each segmented tumor on the CT images. Read More

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Prognostic Value of Pre-Treatment CT Radiomics and Clinical Factors for the Overall Survival of Advanced (IIIB-IV) Lung Adenocarcinoma Patients.

Front Oncol 2021 28;11:628982. Epub 2021 May 28.

GE Healthcare, Beijing, China.

Purpose: The purpose of this study was to investigate the prognostic value of pre-treatment CT radiomics and clinical factors for the overall survival (OS) of advanced (IIIB-IV) lung adenocarcinoma patients.

Methods: This study involved 165 patients with advanced lung adenocarcinoma. The Lasso-Cox regression model was used for feature selection and radiomics signature building. Read More

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Automatic detection of retinopathy with optical coherence tomography images via a semi-supervised deep learning method.

Biomed Opt Express 2021 May 13;12(5):2684-2702. Epub 2021 Apr 13.

School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore.

Automatic detection of retinopathy via computer vision techniques is of great importance for clinical applications. However, traditional deep learning based methods in computer vision require a large amount of labeled data, which are expensive and may not be available in clinical applications. To mitigate this issue, in this paper, we propose a semi-supervised deep learning method built upon pre-trained VGG-16 and virtual adversarial training (VAT) for the detection of retinopathy with optical coherence tomography (OCT) images. Read More

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Sequential Pattern Mining to Predict Medical In-Hospital Mortality from Administrative Data: Application to Acute Coronary Syndrome.

J Healthc Eng 2021 25;2021:5531807. Epub 2021 May 25.

UPRES EA 2415-Clinical Research University Institute, Montpellier University, Montpellier 34 093, France.

Prediction of a medical outcome based on a trajectory of care has generated a lot of interest in medical research. In sequence prediction modeling, models based on machine learning (ML) techniques have proven their efficiency compared to other models. In addition, reducing model complexity is a challenge. Read More

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A Genomic-Clinicopathologic Nomogram for the Prediction of Lymph Node Invasion in Prostate Cancer.

J Oncol 2021 26;2021:5554708. Epub 2021 May 26.

Department of Urology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China.

Background: Lymph node status is important for treatment decision making in prostate cancer (PCa). We aimed to develop a genomic-clinicopathologic nomogram for the prediction of lymph node invasion (LNI) in PCa.

Methods: Differentially expressed genes between LNI and non-LNI PCa samples were identified in the Cancer Genome Atlas database. Read More

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