AIMC Topic: Adult

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Estimating Body Weight From Measurements From Different Single-Slice Computed Tomography Levels: An Evaluation of Total Cross-Sectional Body Area Measurements and Deep Learning.

Journal of computer assisted tomography
OBJECTIVE: This study aimed to evaluate the correlation between the estimated body weight obtained from 2 easy-to-perform methods and the actual body weight at different computed tomography (CT) levels and determine the best reference site for estima...

Patient and dermatologists' perspectives on augmented intelligence for melanoma screening: A prospective study.

Journal of the European Academy of Dermatology and Venereology : JEADV
BACKGROUND: Artificial intelligence (AI) shows promising potential to enhance human decision-making as synergistic augmented intelligence (AuI), but requires critical evaluation for skin cancer screening in a real-world setting.

Placental differences between severe fetal growth restriction and hypertensive disorders of pregnancy requiring early preterm delivery: morphometric analysis of the villous tree supported by artificial intelligence.

American journal of obstetrics and gynecology
BACKGROUND: The great obstetrical syndromes of fetal growth restriction and hypertensive disorders of pregnancy can occur individually or be interrelated. Placental pathologic findings often overlap between these conditions, regardless of whether 1 o...

Automated Machine Learning versus Expert-Designed Models in Ocular Toxoplasmosis: Detection and Lesion Localization Using Fundus Images.

Ocular immunology and inflammation
PURPOSE: Automated machine learning (AutoML) allows clinicians without coding experience to build their own deep learning (DL) models. This study assesses the performance of AutoML in detecting and localizing ocular toxoplasmosis (OT) lesions in fund...

Sleep-phasic heart rate variability predicts stress severity: Building a machine learning-based stress prediction model.

Stress and health : journal of the International Society for the Investigation of Stress
We propose a novel approach for predicting stress severity by measuring sleep phasic heart rate variability (HRV) using a smart device. This device can potentially be applied for stress self-screening in large populations. Using a Holter electrocardi...

Exploring subtypes of multiple sclerosis through unsupervised machine learning of automated fiber quantification.

Japanese journal of radiology
PURPOSE: This study aimed to subtype multiple sclerosis (MS) patients using unsupervised machine learning on white matter (WM) fiber tracts and investigate the implications for cognitive function and disability outcomes.

Effects of Various Heavy Metal Exposures on Insulin Resistance in Non-diabetic Populations: Interpretability Analysis from Machine Learning Modeling Perspective.

Biological trace element research
Increasing and compelling evidence has been proved that heavy metal exposure is involved in the development of insulin resistance (IR). We trained an interpretable predictive machine learning (ML) model for IR in the non-diabetic populations based on...

Residual facial erythema in atopic dermatitis patients treated with dupilumab stratified by machine learning.

Journal of the European Academy of Dermatology and Venereology : JEADV
BACKGROUND: Persistent facial erythema represents a significant complication in atopic dermatitis (AD) patients undergoing treatment with dupilumab. Stratifying patients based on the erythema course is crucial for elucidating heterogeneous phenotypes...

An advanced machine learning method for simultaneous breast cancer risk prediction and risk ranking in Chinese population: A prospective cohort and modeling study.

Chinese medical journal
BACKGROUND: Breast cancer (BC) risk-stratification tools for Asian women that are highly accurate and can provide improved interpretation ability are lacking. We aimed to develop risk-stratification models to predict long- and short-term BC risk amon...

Multicentre validation of a machine learning model for predicting respiratory failure after noncardiac surgery.

British journal of anaesthesia
BACKGROUND: Postoperative respiratory failure is a serious complication that could benefit from early accurate identification of high-risk patients. We developed and validated a machine learning model to predict postoperative respiratory failure, def...