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Predicting Neoplastic Polyp in Patients With Gallbladder Polyps Using Interpretable Machine Learning Models: Retrospective Cohort Study.

Cancer medicine
OBJECTIVE: Gallbladder polyps (GBPs) are increasingly prevalent, with the majority being benign; however, neoplastic polyps carry a risk of malignant transformation, highlighting the importance of accurate differentiation. This study aimed to develop...

ChatGPT's competence in responding to urological emergencies.

Ulusal travma ve acil cerrahi dergisi = Turkish journal of trauma & emergency surgery : TJTES
BACKGROUND: In recent years, artificial intelligence (AI) applications have been increasingly used as sources of medical information, alongside their applications in many other fields. This study is the first to evaluate ChatGPT's performance in addr...

Applying Artificial Intelligence to Quantify Body Composition on Abdominal CTs and Better Predict Kidney Transplantation Wait-List Mortality.

Journal of the American College of Radiology : JACR
BACKGROUND: Prekidney transplant evaluation routinely includes abdominal CT for presurgical vascular assessment. A wealth of body composition data are available from these CT examinations, but they remain an underused source of data, often missing fr...

Opportunistic Detection of Hepatocellular Carcinoma Using Noncontrast CT and Deep Learning Artificial Intelligence.

Journal of the American College of Radiology : JACR
OBJECTIVE: Hepatocellular carcinoma (HCC) poses a heavy global disease burden; early diagnosis is critical to improve outcomes. Opportunistic screening-the use of imaging data acquired for other clinical indications for disease detection-as well as t...

A deep-learning retinal aging biomarker for cognitive decline and incident dementia.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: The utility of retinal photography-derived aging biomarkers for predicting cognitive decline remains under-explored.

Developing and validating a prediction tool for cerebral amyloid angiopathy neuropathological severity.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Cerebral amyloid angiopathy (CAA) is a cerebrovascular condition, the severity of which can only be determined post mortem. Here, we developed machine learning models, the Florey CAA Score (FCAAS), to predict CAA severity (none/mild/mod...

Two-Stage Deep Learning Model for Adrenal Nodule Detection on CT Images: A Retrospective Study.

Radiology
Background The detection and classification of adrenal nodules are crucial for their management. Purpose To develop and test a deep learning model to automatically depict adrenal nodules on abdominal CT images and to simulate triaging performance in ...

Multidisciplinary clinician perceptions on utility of a machine learning tool (ALERT) to predict 6-month mortality and improve end-of-life outcomes for advanced cancer patients.

Cancer medicine
BACKGROUND: There are significant disparities in outcomes at the end-of-life (EOL) for minoritized patients with advanced cancer, with most dying without a documented serious illness conversation (SIC). This study aims to assess clinician perceptions...

Quantifying Facial Gestures Using Deep Learning in a New World Monkey.

American journal of primatology
Facial gestures are a crucial component of primate multimodal communication. However, current methodologies for extracting facial data from video recordings are labor-intensive and prone to human subjectivity. Although automatic tools for this task a...

Utilising routinely collected clinical data through time series deep learning to improve identification of bacterial bloodstream infections: a retrospective cohort study.

The Lancet. Digital health
BACKGROUND: Blood cultures are the gold standard for diagnosing bacterial bloodstream infections, but test results are only available 24-48 h after sampling. We aimed to develop and evaluate models using health-care data to predict bloodstream infect...