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Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears.

Nat Commun 2021 06 10;12(1):3541. Epub 2021 Jun 10.

Department of Pathology, Guangdong Provincial Women's and Children's Dispensary, Shenzhen, Guangdong Province, PR China.

Technical advancements significantly improve earlier diagnosis of cervical cancer, but accurate diagnosis is still difficult due to various factors. We develop an artificial intelligence assistive diagnostic solution, AIATBS, to improve cervical liquid-based thin-layer cell smear diagnosis according to clinical TBS criteria. We train AIATBS with >81,000 retrospective samples. Read More

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Deep Neural Message Passing With Hierarchical Layer Aggregation and Neighbor Normalization.

IEEE Trans Neural Netw Learn Syst 2021 Jun 9;PP. Epub 2021 Jun 9.

As a unified framework for graph neural networks, message passing-based neural network (MPNN) has attracted a lot of research interest and has been shown successfully in a number of domains in recent years. However, because of over-smoothing and vanishing gradients, deep MPNNs are still difficult to train. To alleviate these issues, we first introduce a deep hierarchical layer aggregation (DHLA) strategy, which utilizes a block-based layer aggregation to aggregate representations from different layers and transfers the output of the previous block to the subsequent block, so that deeper MPNNs can be easily trained. Read More

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Image Stitching Based on Semantic Planar Region Consensus.

IEEE Trans Image Process 2021 Jun 8;PP. Epub 2021 Jun 8.

Image stitching for two images without a global transformation between them is notoriously difficult. In this paper, noticing the importance of semantic planar structures under perspective geometry, we propose a new image stitching method which stitches images by allowing for the alignment of a set of matched dominant semantic planar regions. Clearly different from previous methods resorting to plane segmentation, the key to our approach is to utilize rich semantic information directly from RGB images to extract semantic planar image regions with a deep Convolutional Neural Network (CNN). Read More

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Multisource Feedback in the Trauma Context: Priorities and Perspectives.

AEM Educ Train 2021 Jul 13;5(3):e10533. Epub 2020 Oct 13.

and the Department of Emergency Medicine Queen's University Kingston Ontario Canada.

Objectives: Trauma resuscitations require competence in both clinical and nonclinical skills but these can be difficult to observe and assess. Multisource feedback (MSF) is workplace-based, involves the direct observation of learners, and can provide feedback on nonclinical skills. We sought to compare and contrast the priorities of multidisciplinary trauma team members when assessing resident trauma team captain (TTC) performance. Read More

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Detecting Attention Levels in ADHD Children with a Video Game and the Measurement of Brain Activity with a Single-Channel BCI Headset.

Sensors (Basel) 2021 May 6;21(9). Epub 2021 May 6.

Department of Experimental Psychology, Faculty of Psychology, Universidad de Sevilla, 41018 Seville, Spain.

Attentional biomarkers in attention deficit hyperactivity disorder are difficult to detect using only behavioural testing. We explored whether attention measured by a low-cost EEG system might be helpful to detect a possible disorder at its earliest stages. The GokEvolution application was designed to train attention and to provide a measure to identify attentional problems in children early on. Read More

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Transfer learning enhanced generative adversarial networks for multi-channel MRI reconstruction.

Comput Biol Med 2021 May 26;134:104504. Epub 2021 May 26.

Cardiovascular Research Centre, Royal Brompton Hospital, London, SW3 6NP, UK; National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK. Electronic address:

Deep learning based generative adversarial networks (GAN) can effectively perform image reconstruction with under-sampled MR data. In general, a large number of training samples are required to improve the reconstruction performance of a certain model. However, in real clinical applications, it is difficult to obtain tens of thousands of raw patient data to train the model since saving k-space data is not in the routine clinical flow. Read More

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Evaluating a Web-based Point-of-care Ultrasound Curriculum for the Diagnosis of Intussusception.

AEM Educ Train 2021 Jul 23;5(3):e10526. Epub 2020 Sep 23.

Department of Emergency Medicine and Pediatrics University of California San Francisco CA USA.

Objectives: Intussusception is a pediatric medical emergency that can be difficult to diagnose. Radiology-performed ultrasound is the diagnostic study of choice but may lead to delays due to lack of availability. Point-of-care ultrasound for intussusception (POCUS-I) studies have shown excellent accuracy and reduced lengths of stay, but there are limited POCUS-I training materials for pediatric emergency medicine (PEM) providers. Read More

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Training in endoscopic ear surgery using the papaya petiole.

Authors:
V Narendrakumar

J Laryngol Otol 2021 May 25:1-4. Epub 2021 May 25.

Pragathi ENT Clinic, Chrompet, Chennai, India.

Background: Endoscopic ear surgery is a game changer in the field of otology. Training in endoscopic skills is essential for ENT residents, and is especially important during the coronavirus disease 2019 lockdown period. In such difficult times, ENT residents and surgeons can undergo hands-on training using a papaya petiole, even within their homes. Read More

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Using tri-axial accelerometer loggers to identify spawning behaviours of large pelagic fish.

Mov Ecol 2021 May 24;9(1):26. Epub 2021 May 24.

College of Science and Engineering, Flinders University, Adelaide, Australia.

Background: Tri-axial accelerometers have been used to remotely describe and identify in situ behaviours of a range of animals without requiring direct observations. Datasets collected from these accelerometers (i.e. Read More

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Application and Construction of Deep Learning Networks in Medical Imaging.

IEEE Trans Radiat Plasma Med Sci 2021 Mar 13;5(2):137-159. Epub 2020 Oct 13.

Department of Radiology, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53705 USA, and also with the Department of Medical Physics, University of Wisconsin-Madison, Madison, WI 53705 USA.

Deep learning (DL) approaches are part of the machine learning (ML) subfield concerned with the development of computational models to train artificial intelligence systems. DL models are characterized by automatically extracting high-level features from the input data to learn the relationship between matching datasets. Thus, its implementation offers an advantage over common ML methods that often require the practitioner to have some domain knowledge of the input data to select the best latent representation. Read More

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Comparison of various approaches to combine logistic regression with genetic algorithms in survival prediction of hepatocellular carcinoma.

Comput Biol Med 2021 May 11;134:104431. Epub 2021 May 11.

Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Krakow, Poland; Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Gliwice, Poland. Electronic address:

Hepatocellular carcinoma (HCC) is the most common liver cancer in adults. Many different factors make it difficult to diagnose in humans.. Read More

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Challenges in the target volume definition of lung cancer radiotherapy.

Transl Lung Cancer Res 2021 Apr;10(4):1983-1998

University of Manchester, Manchester Academic Health Centre, The Christie NHS Foundation Trust, Manchester, UK.

Radiotherapy, with or without systemic treatment has an important role in the management of lung cancer. In order to deliver the treatment accurately, the clinician must precisely outline the gross tumour volume (GTV), mostly on computed tomography (CT) images. However, due to the limited contrast between tumour and non-malignant changes in the lung tissue, it can be difficult to distinguish the tumour boundaries on CT images leading to large interobserver variation and differences in interpretation. Read More

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Automated Defect Detection from Ultrasonic Images Using Deep Learning.

IEEE Trans Ultrason Ferroelectr Freq Control 2021 May 19;PP. Epub 2021 May 19.

Non-destructive evaluation (NDE) is a set of techniques used for material inspection and defect detection without causing damage to the inspected component. One of the commonly used non-destructive techniques is called ultrasonic inspection. The acquisition of ultrasonic data was mostly automated in recent years, but the analysis of the collected data is still performed manually. Read More

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The Challenges of Dyad Practice in Simulation Training of Basic Open Surgical Skills-A Mixed-Method Study.

Simul Healthc 2021 Mar 18. Epub 2021 Mar 18.

From the Copenhagen Academy for Medical Education and Simulation (D.B.Z., L.K., A.F., J.B.C., E.T.), Copenhagen Ø; University of Copenhagen, Faculty of Health and Medical Sciences (L.K.), Copenhagen N; Department of Thoracic, Cardiac and Vascular Surgery (A.F.), Odense University Hospital, Odense; and Department of Obstetrics and Gynaecology, Slagelse Sygehus (E.T.), Slagelse, Denmark.

Introduction: Simulation training at home improves access to training, but motivation can be difficult to maintain. Dyad training could keep trainees motivated. This study aimed to examine the effect of self-regulated training of basic surgical skills in pairs versus individually. Read More

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Evaluation of soft skills among Italian Healthcare Rehabilitators: A cross sectional study.

J Public Health Res 2021 May 17. Epub 2021 May 17.

Department of Public Health, University Federico II of Naples .

Background: Healthcare rehabilitator skills can be grouped into hard and soft skills. Hard skills are specific and teachable, which can be defined and measured, while soft skills are less tangible and more difficult to quantify. The aim of this study is to investigate the level of knowledge of soft skills among Italian healthcare rehabilitators, and how they were acquired. Read More

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Enhancing Reproductive Organ Segmentation in Pediatric CT via Adversarial Learning.

Proc SPIE Int Soc Opt Eng 2021 Feb 15;11596. Epub 2021 Feb 15.

Department of Electrical and Computer Engineering, Marquette University, Milwaukee, USA.

Accurately segmenting organs in abdominal computed tomography (CT) scans is crucial for clinical applications such as pre-operative planning and dose estimation. With the recent advent of deep learning algorithms, many robust frameworks have been proposed for organ segmentation in abdominal CT images. However, many of these frameworks require large amounts of training data in order to achieve high segmentation accuracy. Read More

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February 2021

Accurate feature point detection method exploiting the line structure of the projection pattern for 3D reconstruction.

Appl Opt 2021 Apr;60(11):2926-2937

The 3D imaging methods using a grid pattern can satisfy real-time applications since they are fast and accurate in decoding and capable of producing a dense 3D map. However, like the other spatial coding methods, it is difficult to achieve high accuracy as is the case for time multiplexing due to the effects of the inhomogeneity of the scene. To overcome those challenges, this paper proposes a convolutional-neural-network-based method of feature point detection by exploiting the line structure of the grid pattern projected. Read More

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Augmenting Transfer Learning with Feature Extraction Techniques for Limited Breast Imaging Datasets.

J Digit Imaging 2021 May 10. Epub 2021 May 10.

Department of Computer Science & Engineering, Anna University, Chennai-600025, Tamil Nadu, India.

Computer aided detection (CADe) and computer aided diagnostic (CADx) systems are ongoing research areas for identifying lesions among complex inner structures with different pixel intensities, and for medical image classification. There are several techniques available for breast cancer detection and diagnosis using CADe and CADx systems. However, some of these systems are not accurate enough or suffer from lack of sufficient data. Read More

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Awe/Gratitude as an Experiential Aspect of Spirituality and Its Association to Perceived Positive Changes During the COVID-19 Pandemic.

Front Psychiatry 2021 20;12:642716. Epub 2021 Apr 20.

Caritas Science and Christian Social Work, Faculty of Theology, Albert-Ludwig-University, Freiburg, Germany.

While the COVID-19 pandemic has affected the lives of almost all people worldwide, many people observed also positive changes in their attitudes and behaviors. This can be seen in the context of posttraumatic growth. These perceived changes refer to five main categories: Nature/Silence/Contemplation, Spirituality, Relationships, Reflection on life, and Digital media usage. Read More

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Using Natural Language Processing to Automatically Assess Feedback Quality: Findings From Three Surgical Residencies.

Acad Med 2021 May 4. Epub 2021 May 4.

E. Ötleş is Medical Scientist Training Program fellow, Department of Industrial and Operations Engineering, University of Michigan Medical School, Ann Arbor, Michigan. D. Kendrick is assistant professor, Department of Surgery, University of Minnesota Medical School, Minneapolis, Minnesota. Q.P. Solano is a third-year medical student, University of Michigan Medical School, Ann Arbor, Michigan. M. Schuller is senior project manager, Department of Surgery, University of Michigan Medical School, Ann Arbor, Michigan. S.L. Ahle is a resident, Department of Surgery, Yale School of Medicine, New Haven, Connecticut. M.H. Eskender is a resident, Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, Illinois. E. Carnes is research assistant, Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, Illinois. B.C. George is assistant professor and director, Center for Surgical Training and Research, Department of Surgery, University of Michigan Medical School, Ann Arbor, Michigan.

Purpose: Learning is markedly improved with high-quality feedback, yet assuring the quality of feedback is difficult to achieve at scale. Natural language processing (NLP) algorithms may be useful in this context as they can automatically classify large volumes of narrative data. However, it is unknown if NLP models can accurately evaluate surgical trainee feedback. Read More

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Classifying signals from a wearable accelerometer device to measure respiratory rate.

ERJ Open Res 2021 Apr 26;7(2). Epub 2021 Apr 26.

Centre for Speckled Computing, School of Informatics, University of Edinburgh, Edinburgh, UK.

Background: Automatic measurement of respiratory rate in general hospital patients is difficult. Patient movement degrades the signal and variation of the breathing cycle means that accurate observation for ≥60 s is needed for adequate precision.

Methods: We studied acutely ill patients recently admitted to a teaching hospital. Read More

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Towards a Platform for Robot-Assisted Minimally-Supervised Therapy of Hand Function: Design and Pilot Usability Evaluation.

Front Bioeng Biotechnol 2021 15;9:652380. Epub 2021 Apr 15.

Rehabilitation Engineering Laboratory, D-HEST, ETH Zürich, Zurich, Switzerland.

Background: Robot-assisted therapy can increase therapy dose after stroke, which is often considered insufficient in clinical practice and after discharge, especially with respect to hand function. Thus far, there has been a focus on rather complex systems that require therapist supervision. To better exploit the potential of robot-assisted therapy, we propose a platform designed for minimal therapist supervision, and present the preliminary evaluation of its immediate usability, one of the main and frequently neglected challenges for real-world application. Read More

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Multi-task Learning via Adaptation to Similar Tasks for Mortality Prediction of Diverse Rare Diseases.

AMIA Annu Symp Proc 2020 25;2020:763-772. Epub 2021 Jan 25.

Department of Computer Science, Peking University, Beijing, China.

The mortality prediction of diverse rare diseases using electronic health record (EHR) data is a crucial task for intelligent healthcare. However, data insufficiency and the clinical diversity of rare diseases make it hard for deep learning models to be trained. Mortality prediction for these patients with different diseases can be viewed as a multi-task learning problem with insufficient data but a large number of tasks. Read More

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A Clinically Practical and Interpretable Deep Model for ICU Mortality Prediction with External Validation.

AMIA Annu Symp Proc 2020 25;2020:629-637. Epub 2021 Jan 25.

PingAn Health Technology, Beijing, China.

Deep learning models are increasingly studied in the field of critical care. However, due to the lack of external validation and interpretability, it is difficult to generalize deep learning models in critical care senarios. Few works have validated the performance of the deep learning models with external datasets. Read More

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Bidirectional Mapping of Brain MRI and PET With 3D Reversible GAN for the Diagnosis of Alzheimer's Disease.

Front Neurosci 2021 9;15:646013. Epub 2021 Apr 9.

Combining multi-modality data for brain disease diagnosis such as Alzheimer's disease (AD) commonly leads to improved performance than those using a single modality. However, it is still challenging to train a multi-modality model since it is difficult in clinical practice to obtain complete data that includes all modality data. Generally speaking, it is difficult to obtain both magnetic resonance images (MRI) and positron emission tomography (PET) images of a single patient. Read More

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An Architecture for Reliable Transportation of Delicate Goods.

Sensors (Basel) 2021 Apr 9;21(8). Epub 2021 Apr 9.

Research Centre in Digitalization and Intelligent Robotics, Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal.

Adequate conditions are critical to avoiding damage or degradation of products during transportation, especially in the case of delicate goods like food products, live animals, precision machinery or art items, among others. The damages are not always readily identified: sometimes they are only detected several days or weeks after the merchandise has been delivered. Moreover, it may be hard to assess if the problems resulted from the transport conditions, and it may be even harder to prove it, making it difficult to determine and assign responsibilities. Read More

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Gastric polyp detection in gastroscopic images using deep neural network.

PLoS One 2021 28;16(4):e0250632. Epub 2021 Apr 28.

School of Automation, Southeast University, Nanjing, China.

This paper presents the research results of detecting gastric polyps with deep learning object detection method in gastroscopic images. Gastric polyps have various sizes. The difficulty of polyp detection is that small polyps are difficult to detect from the background. Read More

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[Early diagnosis of Alzheimer's disease based on three-dimensional convolutional neural networks ensemble model combined with genetic algorithm].

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2021 Feb;38(1):47-55

Faculty of Computer, Guangdong University of Technology, Guangzhou 510006, P.R.China.

The pathogenesis of Alzheimer's disease (AD), a common neurodegenerative disease, is still unknown. It is difficult to determine the atrophy areas, especially for patients with mild cognitive impairment (MCI) at different stages of AD, which results in a low diagnostic rate. Therefore, an early diagnosis model of AD based on 3-dimensional convolutional neural network (3DCNN) and genetic algorithm (GA) was proposed. Read More

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February 2021

Social Media Bridges the Training Gap Between Match Day and Internship With ACGME Milestone-based Clinical Case Curriculum.

AEM Educ Train 2021 Apr 6;5(2):e10503. Epub 2020 Aug 6.

Department of Emergency Medicine St. John's Riverside Hospital Yonkers NY USA.

Objectives: The objective was to bridge the relative educational gap for newly matched emergency medicine preinterns between Match Day and the start of internship by implementing an Accreditation Council for Graduate Medical Education Milestone (ACGME)-based virtual case curriculum over the social media platform Slack.

Methods: We designed a Milestone-based curriculum of 10 emergency department clinical cases and used Slack to implement it. An instructor was appointed for each participating institution to lead the discussion and encourage collaboration among preinterns. Read More

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Visual interpretability in 3D brain tumor segmentation network.

Comput Biol Med 2021 Jun 19;133:104410. Epub 2021 Apr 19.

Medical Imaging and Diagnostics Lab, National Centre of Artificial Intelligence (NCAI), Pakistan; Department of Computer Science, COMSATS University Islamabad (CUI), Islamabad, 45550, Pakistan. Electronic address:

Medical image segmentation is a complex yet one of the most essential tasks for diagnostic procedures such as brain tumor detection. Several 3D Convolutional Neural Network (CNN) architectures have achieved remarkable results in brain tumor segmentation. However, due to the black-box nature of CNNs, the integration of such models to make decisions about diagnosis and treatment is high-risk in the domain of healthcare. Read More

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