AIMC Topic: Diagnosis, Computer-Assisted

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SpeckleGAN: a generative adversarial network with an adaptive speckle layer to augment limited training data for ultrasound image processing.

International journal of computer assisted radiology and surgery Jun 18, 2020
PURPOSE: In the field of medical image analysis, deep learning methods gained huge attention over the last years. This can be explained by their often improved performance compared to classic explicit algorithms. In order to work well, they need larg...
Software Algorithms Databases, Factual Computer Simulation Diagnosis, Computer-Assisted Humans Neural Networks, Computer Deep Learning Ultrasonography Image Processing, Computer-Assisted Normal Distribution
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Histologic tissue components provide major cues for machine learning-based prostate cancer detection and grading on prostatectomy specimens.

Scientific reports Jun 18, 2020
Automatically detecting and grading cancerous regions on radical prostatectomy (RP) sections facilitates graphical and quantitative pathology reporting, potentially benefitting post-surgery prognosis, recurrence prediction, and treatment planning aft...
Histological Techniques Humans Prostatic Neoplasms Machine Learning Neoplasm Grading Neoplasm Recurrence, Local Algorithms Prostatectomy Male Diagnosis, Computer-Assisted
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Lower Adenoma Miss Rate of Computer-Aided Detection-Assisted Colonoscopy vs Routine White-Light Colonoscopy in a Prospective Tandem Study.

Gastroenterology Jun 17, 2020
BACKGROUND AND AIMS: Up to 30% of adenomas might be missed during screening colonoscopy-these could be polyps that appear on-screen but are not recognized by endoscopists or polyps that are in locations that do not appear on the screen at all. Comput...
Adenomatous Polyps Missed Diagnosis Humans Prospective Studies Female Reproducibility of Results Aged Young Adult Adult Male Middle Aged Adolescent Predictive Value of Tests Colonic Neoplasms Colonic Polyps Image Interpretation, Computer-Assisted Colonoscopy Diagnosis, Computer-Assisted Deep Learning China
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Multi-input deep learning architecture for predicting breast tumor response to chemotherapy using quantitative MR images.

International journal of computer assisted radiology and surgery Jun 16, 2020
PURPOSE: Neoadjuvant chemotherapy (NAC) aims to minimize the tumor size before surgery. Predicting response to NAC could reduce toxicity and delays to effective intervention. Computational analysis of dynamic contrast-enhanced magnetic resonance imag...
Contrast Media Magnetic Resonance Imaging Area Under Curve Chemotherapy, Adjuvant Deep Learning Humans ROC Curve Breast Neoplasms Neural Networks, Computer Image Processing, Computer-Assisted Middle Aged Retrospective Studies Treatment Outcome Antineoplastic Agents Female Neoadjuvant Therapy Diagnosis, Computer-Assisted Adult
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Computing schizophrenia: ethical challenges for machine learning in psychiatry.

Psychological medicine Jun 15, 2020
Recent advances in machine learning (ML) promise far-reaching improvements across medical care, not least within psychiatry. While to date no psychiatric application of ML constitutes standard clinical practice, it seems crucial to get ahead of these...
Humans Machine Learning Algorithms Psychiatry Bioethics Diagnosis, Computer-Assisted Schizophrenia
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Deep learning in interstitial lung disease-how long until daily practice.

European radiology Jun 14, 2020
Interstitial lung diseases are a diverse group of disorders that involve inflammation and fibrosis of interstitium, with clinical, radiological, and pathological overlapping features. These are an important cause of morbidity and mortality among lung...
Inflammation Idiopathic Pulmonary Fibrosis Lung Diseases, Interstitial Pattern Recognition, Automated Humans Neural Networks, Computer Algorithms Tomography, X-Ray Computed Reproducibility of Results Lung Diagnosis, Computer-Assisted Deep Learning
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Machine Learning of 12-Lead QRS Waveforms to Identify Cardiac Resynchronization Therapy Patients With Differential Outcomes.

Circulation. Arrhythmia and electrophysiology Jun 14, 2020
BACKGROUND: Cardiac resynchronization therapy (CRT) improves heart failure outcomes but has significant nonresponse rates, highlighting limitations in ECG selection criteria: QRS duration (QRSd) ≥150 ms and subjective labeling of left bundle branch b...
Bundle-Branch Block Signal Processing, Computer-Assisted Aged Heart Failure Male Treatment Outcome Cardiac Resynchronization Therapy Humans Ventricular Function, Left Risk Assessment Unsupervised Machine Learning Heart-Assist Devices Retrospective Studies Stroke Volume Recovery of Function Heart Transplantation Middle Aged Female Predictive Value of Tests Risk Factors Disease Progression Diagnosis, Computer-Assisted Time Factors Electrocardiography
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Preprocedure Application of Machine Learning and Mechanistic Simulations Predicts Likelihood of Paroxysmal Atrial Fibrillation Recurrence Following Pulmonary Vein Isolation.

Circulation. Arrhythmia and electrophysiology Jun 14, 2020
BACKGROUND: Pulmonary vein isolation (PVI) is an effective treatment strategy for patients with atrial fibrillation (AF), but many experience AF recurrence and require repeat ablation procedures. The goal of this study was to develop and evaluate a m...
Female Reproducibility of Results Risk Factors Aged Male Humans Machine Learning Retrospective Studies Heart Rate Catheter Ablation Proof of Concept Study Contrast Media Middle Aged Organometallic Compounds Recurrence Magnetic Resonance Imaging Models, Cardiovascular Risk Assessment Action Potentials Patient-Specific Modeling Predictive Value of Tests Diagnosis, Computer-Assisted Pulmonary Veins Atrial Fibrillation Treatment Outcome Meglumine
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Convolutional neural network for detection and classification of seizures in clinical data.

Medical & biological engineering & computing Jun 12, 2020
Epileptic seizure detection and classification in clinical electroencephalogram data still is a challenge, and only low sensitivity with a high rate of false positives has been achieved with commercially available seizure detection tools, which usual...
Databases, Factual Diagnosis, Computer-Assisted Deep Learning Electroencephalography Seizures Humans Neural Networks, Computer Data Analysis Algorithms Signal Processing, Computer-Assisted
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BIRADS features-oriented semi-supervised deep learning for breast ultrasound computer-aided diagnosis.

Physics in medicine and biology Jun 12, 2020
We propose a novel BIRADS-SSDL network that integrates clinically-approved breast lesion characteristics (BIRADS features) into task-oriented semi-supervised deep learning (SSDL) for accurate diagnosis of ultrasound (US) images with a small training ...
Deep Learning Image Processing, Computer-Assisted Ultrasonography, Mammary Breast Normal Distribution Humans Female Breast Neoplasms Diagnosis, Computer-Assisted
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