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Deep learning visual analysis in laparoscopic surgery: a systematic review and diagnostic test accuracy meta-analysis.

Surgical endoscopy
BACKGROUND: In the past decade, deep learning has revolutionized medical image processing. This technique may advance laparoscopic surgery. Study objective was to evaluate whether deep learning networks accurately analyze videos of laparoscopic proce...

Retraining Convolutional Neural Networks for Specialized Cardiovascular Imaging Tasks: Lessons from Tetralogy of Fallot.

Pediatric cardiology
Ventricular contouring of cardiac magnetic resonance imaging is the gold standard for volumetric analysis for repaired tetralogy of Fallot (rTOF), but can be time-consuming and subject to variability. A convolutional neural network (CNN) ventricular ...

Development of Electronic Health Record-Based Prediction Models for 30-Day Readmission Risk Among Patients Hospitalized for Acute Myocardial Infarction.

JAMA network open
IMPORTANCE: In the US, more than 600 000 adults will experience an acute myocardial infarction (AMI) each year, and up to 20% of the patients will be rehospitalized within 30 days. This study highlights the need for consideration of calibration in th...

Effect of Machine Learning on Dispatcher Recognition of Out-of-Hospital Cardiac Arrest During Calls to Emergency Medical Services: A Randomized Clinical Trial.

JAMA network open
IMPORTANCE: Emergency medical dispatchers fail to identify approximately 25% of cases of out-of-hospital cardiac arrest (OHCA), resulting in lost opportunities to save lives by initiating cardiopulmonary resuscitation.

Noninvasive Prediction of Occult Peritoneal Metastasis in Gastric Cancer Using Deep Learning.

JAMA network open
IMPORTANCE: Occult peritoneal metastasis frequently occurs in patients with advanced gastric cancer and is poorly diagnosed with currently available tools. Because the presence of peritoneal metastasis precludes the possibility of curative surgery, t...

Predicting hearing recovery following treatment of idiopathic sudden sensorineural hearing loss with machine learning models.

American journal of otolaryngology
PURPOSE: Idiopathic sudden sensorineural hearing loss (ISSHL) is an emergency otological disease, and its definite prognostic factors remain unclear. This study applied machine learning methods to develop a new ISSHL prognosis prediction model.

Assessing PD-L1 expression in non-small cell lung cancer and predicting responses to immune checkpoint inhibitors using deep learning on computed tomography images.

Theranostics
This study aimed to use computed tomography (CT) images to assess PD-L1 expression in non-small cell lung cancer (NSCLC) and predict response to immunotherapy. We retrospectively analyzed a PD-L1 expression dataset that consisted of 939 consecutive...