AIMC Topic: Retrospective Studies

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Machine learning for outcome predictions of patients with trauma during emergency department care.

BMJ health & care informatics
OBJECTIVES: To develop and evaluate a machine learning model for predicting patient with trauma mortality within the US emergency departments.

[Application of digital medical technology in hepatopancreatobiliary surgery:20 years' retrospective review and prospect].

Zhonghua wai ke za zhi [Chinese journal of surgery]
Digital medicine has played a vital role in promoting the development of hepatobiliary and pancreatic surgery of China.The multidisciplinary integration of medical science and technology innovates research and development,and practice in clinical dia...

Upstaging and Survival Outcomes for Non-Muscle Invasive Bladder Cancer After Radical Cystectomy: Results from the International Robotic Cystectomy Consortium.

Journal of endourology
We sought to describe the incidence, risk factors, and survival outcomes associated with pathologic upstaging from non-muscle invasive bladder cancer (NMIBC) to muscle invasive bladder cancer (MIBC) after robot-assisted radical cystectomy (RARC). W...

Using Natural Language Processing to Automatically Assess Feedback Quality: Findings From 3 Surgical Residencies.

Academic medicine : journal of the Association of American Medical Colleges
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 la...

A Novel Deep Learning Based Computer-Aided Diagnosis System Improves the Accuracy and Efficiency of Radiologists in Reading Biparametric Magnetic Resonance Images of the Prostate: Results of a Multireader, Multicase Study.

Investigative radiology
OBJECTIVE: The aim of this study was to evaluate the effect of a deep learning based computer-aided diagnosis (DL-CAD) system on radiologists' interpretation accuracy and efficiency in reading biparametric prostate magnetic resonance imaging scans.

Corneal Edema Visualization With Optical Coherence Tomography Using Deep Learning: Proof of Concept.

Cornea
PURPOSE: Optical coherence tomography (OCT) is essential for the diagnosis and follow-up of corneal edema, but assessment can be challenging in minimal or localized edema. The objective was to develop and validate a novel automated tool to detect and...

Histopathological Diagnosis System for Gastritis Using Deep Learning Algorithm.

Chinese medical sciences journal = Chung-kuo i hsueh k'o hsueh tsa chih
Objective To develope a deep learning algorithm for pathological classification of chronic gastritis and assess its performance using whole-slide images (WSIs). Methods We retrospectively collected 1,250 gastric biopsy specimens (1,128 gastritis, 122...

[Da Vinci robot-assisted pylorus and vagus nerve-preserving partial gastrectomy for gastric cancer].

Zhonghua wei chang wai ke za zhi = Chinese journal of gastrointestinal surgery
To investigate the safety and feasibility of Da Vinci robot-assisted pylorus and vagus nerve-preserving partial gastrectomy for gastric cancer. In this study, descriptive case series method was used to retrospectively analyze the data of 3 patients...

[Clinical significance of the deep learning algorithm based on contrast-enhanced CT in the differential diagnosis of gastric gastrointestinal stromal tumors with a diameter ≤ 5 cm].

Zhonghua wei chang wai ke za zhi = Chinese journal of gastrointestinal surgery
Contrast-enhanced CT is an important method of preoperative diagnosis and evaluation for the malignant potential of gastric submucosal tumor (SMT). It has a high diagnostic accuracy rate in differentiating gastric gastrointestinal stromal tumor (GIS...

Diagnosis of focal liver lesions with deep learning-based multi-channel analysis of hepatocyte-specific contrast-enhanced magnetic resonance imaging.

World journal of gastroenterology
BACKGROUND: The nature of input data is an essential factor when training neural networks. Research concerning magnetic resonance imaging (MRI)-based diagnosis of liver tumors using deep learning has been rapidly advancing. Still, evidence to support...