AIMC Topic: Humans

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Evaluation of a deep learning segmentation tool to help detect spinal cord lesions from combined T2 and STIR acquisitions in people with multiple sclerosis.

European radiology
OBJECTIVE: To develop a deep learning (DL) model for the detection of spinal cord (SC) multiple sclerosis (MS) lesions from both sagittal T2 and short tau inversion recovery (STIR) sequences and to investigate whether such a model could improve the p...

Machine Learning Feasibility in Cochlear Implant Speech Perception Outcomes-Moving Beyond Single Biomarkers for Cochlear Implant Performance Prediction.

Ear and hearing
OBJECTIVES: Machine learning (ML) is an emerging discipline centered around complex pattern matching and large data-based prediction modeling and can improve precision medicine healthcare. Cochlear implants (CI) are highly effective, however, outcome...

DUAL VALIDATION ANALYSIS OF SERUM CYP3A4 IN PREDICTING NEC IN PRETERM INFANTS.

Shock (Augusta, Ga.)
Objective: Necrotizing enterocolitis (NEC) is a life-threatening condition in premature infants, where timely diagnosis and intervention are crucial. This study investigated the potential of serum CYP3A4 as an early predictive biomarker for NEC and d...

Deep learning model for detecting cystoid fluid collections on optical coherence tomography in X-linked retinoschisis patients.

Acta ophthalmologica
PURPOSE: To validate a deep learning (DL) framework for detecting and quantifying cystoid fluid collections (CFC) on spectral-domain optical coherence tomography (SD-OCT) in X-linked retinoschisis (XLRS) patients.

The performance of ChatGPT and ERNIE Bot in surgical resident examinations.

International journal of medical informatics
STUDY PURPOSE: To assess the application of these two large language models (LLMs) for surgical resident examinations and to compare the performance of these LLMs with that of human residents.

Predicting hospital admissions, ICU utilization, and prolonged length of stay among febrile pediatric emergency department patients using incomplete and imbalanced electronic health record (EHR) data strategies.

International journal of medical informatics
OBJECTIVE: Determine the efficacy of commonly used approaches to handling missing and/or imbalanced Electronic Health Record (EHR) data on the performance of predictive models targeting risk of admission, intensive care unit (ICU) use, or prolonged l...

Predicting genes associated with ossification of the posterior longitudinal ligament using graph attention network.

Methods (San Diego, Calif.)
Ossification of the posterior longitudinal ligament is a degenerative disease that severely impacts the spine, with a complex pathogenesis involving the interplay of multiple genes. This study utilizes a combination of graph neural networks and deep ...

Gap-App: A sex-distinct AI-based predictor for pancreatic ductal adenocarcinoma survival as a web application open to patients and physicians.

Cancer letters
In this study, using RNA-Seq gene expression data and advanced machine learning techniques, we identified distinct gene expression profiles between male and female pancreatic ductal adenocarcinoma (PDAC) patients. Building on this insight, we develop...

FreqYOLO: A uterine disease detection network based on local and global frequency feature learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Leiomyomas (LM) and adenomyosis (AM) are common gynecological diseases with high incidence rates and an increasing trend of affecting younger women. Accurate detection and differentiation of LM and AM in ultrasound images are crucial for selecting ap...