AIMC Topic: Female

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GWO+RuleFit: rule-based explainable machine-learning combined with heuristics to predict mid-treatment FDG PET response to chemoradiation for locally advanced non-small cell lung cancer.

Physics in medicine and biology
Vital rules learned from fluorodeoxyglucose positron emission tomography (FDG-PET) radiomics of tumor subregional response can provide clinical decision support for precise treatment adaptation. We combined a rule-based machine learning (ML) model (R...

Trust criteria for artificial intelligence in health: normative and epistemic considerations.

Journal of medical ethics
Rapid advancements in artificial intelligence and machine learning (AI/ML) in healthcare raise pressing questions about how much users should trust AI/ML systems, particularly for high stakes clinical decision-making. Ensuring that user trust is prop...

Developing a privacy-preserving deep learning model for glaucoma detection: a multicentre study with federated learning.

The British journal of ophthalmology
BACKGROUND: Deep learning (DL) is promising to detect glaucoma. However, patients' privacy and data security are major concerns when pooling all data for model development. We developed a privacy-preserving DL model using the federated learning (FL) ...

Prediction of visual field progression with serial optic disc photographs using deep learning.

The British journal of ophthalmology
AIM: We tested the hypothesis that visual field (VF) progression can be predicted with a deep learning model based on longitudinal pairs of optic disc photographs (ODP) acquired at earlier time points during follow-up.

Computational approaches for clinical, genomic and proteomic markers of response to glucagon-like peptide-1 therapy in type-2 diabetes mellitus: An exploratory analysis with machine learning algorithms.

Diabetes & metabolic syndrome
INTRODUCTION: In 2021, the International Diabetes Federation reported that 537 million people worldwide are living with diabetes. While glucagon-like peptide-1 agonists provide significant benefits in diabetes management, approximately 40% of patient...

A stacking ensemble model for predicting the occurrence of carotid atherosclerosis.

Frontiers in endocrinology
BACKGROUND: Carotid atherosclerosis (CAS) is a significant risk factor for cardio-cerebrovascular events. The objective of this study is to employ stacking ensemble machine learning techniques to enhance the prediction of CAS occurrence, incorporatin...

Deep Learning-Enabled Vasculometry Depicts Phased Lesion Patterns in High Myopia Progression.

Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)
PURPOSE: To investigate the potential phases in myopic retinal vascular alterations for further elucidating the mechanisms underlying the progression of high myopia (HM).

Risk prediction of kalaemia disturbance and acute kidney injury after total knee arthroplasty: use of a machine learning algorithm.

Orthopaedics & traumatology, surgery & research : OTSR
INTRODUCTION: Total knee arthroplasty (TKA) is a procedure associated with risks of electrolyte and kidney function disorders, which are rare but can lead to serious complications if not correctly identified. A routine check-up is very often carried ...

Deep Learning for Predicting the Difficulty Level of Removing the Impacted Mandibular Third Molar.

International dental journal
BACKGROUND: Preoperative assessment of the impacted mandibular third molar (LM3) in a panoramic radiograph is important in surgical planning. The aim of this study was to develop and evaluate a computer-aided visualisation-based deep learning (DL) sy...

The acceptance of the potential use of social robots for children with autism spectrum disorder by Indonesian occupational therapists: a mixed methods study.

Disability and rehabilitation. Assistive technology
PURPOSE: Social robots have shown positive effects in treating children with autism spectrum disorder. The development of social robots in Indonesia has enabled their potential use in occupational therapy. This study aimed to investigate the factors ...