AIMC Topic: Adult

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Investigating the capability of deep learning models to predict age and biological sex from anterior segment ophthalmic imaging: a multi-centre retrospective study.

BMJ open
OBJECTIVE: To assess the capability of a convolutional neural network trained by transfer learning on anterior segment optical coherence tomography (AS-OCT) images, Placido-disk corneal topography images and external photographs to predict age and bi...

AI-assisted recurrent laryngeal nerve identification during endoscopic/robotic thyroid surgery based on the CMC-UNet model: a multicenter retrospective study.

Journal of robotic surgery
During endoscopic or robotic-assisted thyroid surgery, the field of view may be restricted by tissue swelling or bleeding. These Limitations make delicate surgical manipulation in the confined space more challenging. This study proposes an artificial...

Individual innovativeness levels and levels of medical artificial intelligence readiness among nursing students: a cross-sectional and correlational study.

BMC medical education
AIM: This study, aimed to determine the individual innovativeness levels of nursing students and their readiness levels for medical artificial intelligence and the relationship between these two variables.

Identifying EEG-based neurobehavioral risk markers of gaming addiction using machine learning and iowa gambling task.

Biomedical physics & engineering express
Internet Gaming Disorder (IGD), Gaming Disorder (GD), and Internet Addiction represent behavioral patterns with significant psychological and neurological consequences. Affected individuals often disengage from routine activities and exhibit distress...

Prognostic machine learning models for predicting postoperative complications following general surgery in Bandar Abbas, Iran: a study protocol.

BMJ open
INTRODUCTION: To enhance the quality of surgical care, complications need to be minimised. Consequently, comprehending the occurrence and risk elements for postoperative complications is essential. Subsequently, we will apply machine learning (ML) al...

AI-based HRCT quantification reveals DLCO and TLC as key determinants of ILD severity in connective tissue diseases.

RMD open
OBJECTIVE: Interstitial lung disease (ILD) represents the most common and severe organ manifestation observed in patients diagnosed with connective tissue diseases (CTDs). The aim of this retrospective cross-sectional study was to identify clinical r...

Development and Validation of a Predictive Model for Severe Tubular Atrophy/Interstitial Fibrosis in Patients with IgA Nephropathy: Multicenter Retrospective Study.

JMIR medical informatics
BACKGROUND: Severe tubular atrophy/interstitial fibrosis are critical pathological features associated with poor prognosis in IgA nephropathy (IgAN). The early identification of patients at high risk for severe tubular damage could guide clinical man...

Computational modeling of platelet activation signatures in response to diverse immune and hemostatic agonists.

Platelets
Platelets are increasingly recognized as key players not only in hemostasis, but also in immunity and inflammation. However, the mechanisms and markers underlying their activation remain incompletely understood. This study aimed to decipher how plate...