AIMC Topic: Sensitivity and Specificity

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Applying machine learning to predict bowel preparation adequacy in elderly patients for colonoscopy: development and validation of a web-based prediction tool.

Annals of medicine
BACKGROUND: Adequate bowel preparation is crucial for effective colonoscopy, especially in elderly patients who face a high risk of inadequate preparation. This study develops and validates a machine learning model to predict bowel preparation adequa...

GPT-4o and Specialized AI in Breast Ultrasound Imaging: A Comparative Study on Accuracy, Agreement, Limitations, and Diagnostic Potential.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
OBJECTIVES: This study aimed to evaluate the ability of ChatGPT and Breast Ultrasound Helper, a special ChatGPT-based subprogram trained on ultrasound image analysis, to analyze and differentiate benign and malignant breast lesions on ultrasound imag...

A Systematic Review of the Clinical Impact of Implementing Artificial Intelligence in Upper Aerodigestive Tract Endoscopy.

Head & neck
BACKGROUND: Endoscopy is essential in upper aerodigestive tract (UADT) examination, particularly in the early detection of laryngopharyngeal lesions. However, UADT endoscopy remains operator-dependent and lacks standardized quality metrics. Recent ad...

Evaluation of an AI facial recognition system for Turner Syndrome screening and facial complexity: a prospective cohort.

International journal of medical informatics
PURPOSE: Artificial intelligence-based facial recognition (AI-FR) is promising in diagnosis of diseases with distinct facial features. Our team has retrospectively constructed an AI-FR system for Turner Syndrome (TS) based on 1295 facial photographs ...

Stroke Sensitivity Calculation in Medical Emergency Calls and Factors Associated With Stroke Suspicion: A Retrospective Registry-Based Study.

Annals of emergency medicine
STUDY OBJECTIVE: Sensitivity for stroke detection in emergency medical communication centers (EMCCs) varies widely. Few studies offer detailed insights into the out-of-hospital pathways of patients with stroke. This study explored the ability of EMCC...

Zero-shot large language model application for surgical site infection auditing.

Infection, disease & health
INTRODUCTION: Artificial intelligence, in particular large language models (LLM), may be able to assist with monitoring for surgical site infections (SSI).

Development of a deep-learning algorithm for etiological classification of subarachnoid hemorrhage using non-contrast CT scans.

European radiology
OBJECTIVES: This study aims to develop a deep learning algorithm for differentiating aneurysmal subarachnoid hemorrhage (aSAH) from non-aneurysmal subarachnoid hemorrhage (naSAH) using non-contrast computed tomography (NCCT) scans.

Preoperative radiomics models using CT and MRI for microsatellite instability in colorectal cancer: a systematic review and meta-analysis.

Abdominal radiology (New York)
OBJECTIVE: Microsatellite instability (MSI) is a novel predictive biomarker for chemotherapy and immunotherapy response, as well as prognostic indicator in colorectal cancer (CRC). The current standard for MSI identification is polymerase chain react...

External validation of an RSNA 2023 Abdominal Trauma AI Challenge high performing machine learning model in the detection and grading of splenic injuries on CT.

Abdominal radiology (New York)
PURPOSE: This study aims to validate the performance of an award-winning machine learning (ML) model from the Radiological Society of North America (RSNA) 2023 Abdominal Trauma AI Challenge in detecting splenic injuries on CT scans using a large, geo...

The accuracy of Machine learning in the prediction and diagnosis of diabetic kidney Disease: A systematic review and Meta-Analysis.

International journal of medical informatics
PURPOSE: Machine learning (ML) has gained attention in diabetes management, particularly for predicting and diagnosing diabetic kidney disease (DKD). However, systematic evidence on its performance remains limited. This study evaluates the predictive...