AI Medical Compendium Topic

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Identifying most important predictors for suicidal thoughts and behaviours among healthcare workers active during the Spain COVID-19 pandemic: a machine-learning approach.

Epidemiology and psychiatric sciences
AIMS: Studies conducted during the COVID-19 pandemic found high occurrence of suicidal thoughts and behaviours (STBs) among healthcare workers (HCWs). The current study aimed to (1) develop a machine learning-based prediction model for future STBs us...

Hierarchical diagnosis of breast phyllodes tumors enabled by deep learning of ultrasound images: a retrospective multi-center study.

Cancer imaging : the official publication of the International Cancer Imaging Society
OBJECTIVE: Phyllodes tumors (PTs) are rare breast tumors with high recurrence rates, current methods relying on post-resection pathology often delay detection and require further surgery. We propose a deep-learning-based Phyllodes Tumors Hierarchical...

Hearing vocals to recognize schizophrenia: speech discriminant analysis with fusion of emotions and features based on deep learning.

BMC psychiatry
BACKGROUND AND OBJECTIVE: Accurate detection of schizophrenia poses a grand challenge as a complex and heterogeneous mental disorder. Current diagnostic criteria rely primarily on clinical symptoms, which may not fully capture individual differences ...

Enhancing clinical decision-making in closed pelvic fractures with machine learning models.

Biomolecules & biomedicine
Closed pelvic fractures can lead to severe complications, including hemodynamic instability (HI) and mortality. Accurate prediction of these risks is crucial for effective clinical management. This study aimed to utilize various machine learning (ML)...

Deep learning segmentation of periarterial and perivenous capillary-free zones in optical coherence tomography angiography.

Journal of biomedical optics
SIGNIFICANCE: Automated segmentation of periarterial and perivenous capillary-free zones (CFZs) in optical coherence tomography angiography (OCTA) can significantly improve early detection and monitoring of diabetic retinopathy (DR), a leading cause ...

Developing the DigIQ: A measure of digital competence.

PloS one
Digital competence reflects the skills and knowledge needed to thrive in the digital world. Despite the criticality of this concept, the field lacks a robust and valid measure of digital competence that is suitable across the life span. Addressing th...

Using Natural Language Processing and Machine Learning to classify the status of kidney allograft in Electronic Medical Records written in Spanish.

PloS one
INTRODUCTION: Accurate identification of graft loss in Electronic Medical Records of kidney transplant recipients is essential but challenging due to inconsistent and not mandatory International Classification of Diseases (ICD) codes. We developed an...

Radiomic analysis based on machine learning of multi-sequences MR to assess early treatment response in locally advanced nasopharyngeal carcinoma.

Science progress
ObjectiveThe prediction of early response in locally advanced nasopharyngeal carcinoma (LA-NPC) after concurrent chemoradiotherapy (CCRT) is important for determining the need for timely consolidation therapy. We developed a radiomic analysis of mult...

Evaluating the impact of AI-generated educational content on patient understanding and anxiety in endodontics and restorative dentistry: a comparative study.

BMC oral health
BACKGROUND: Effective patient education is critical in enhancing treatment outcomes and reducing anxiety in dental procedures. This study compares the effectiveness of AI-generated educational materials with traditional methods in improving patient c...