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TET2 gene mutation status associated with poor prognosis of transition zone prostate cancer: a retrospective cohort study based on whole exome sequencing and machine learning models.

Frontiers in endocrinology
BACKGROUND: Prostate cancer (PCa) in the transition zone (TZ) is uncommon and often poses challenges for early diagnosis, but its genomic determinants and therapeutic vulnerabilities remain poorly characterized.

A retrospective study using machine learning to develop predictive model to identify rotavirus-associated acute gastroenteritis in children.

PeerJ
BACKGROUND: Rotavirus is the leading cause of severe dehydrating diarrhea in children under 5 years worldwide. Timely diagnosis is critical, but access to confirmatory testing is limited in hospital settings. Machine learning (ML) models have shown p...

Using the Minnesota Multiphasic Personality Inventory-2 restructured form to predict functioning after treatment for borderline personality disorder: A machine learning approach.

Psychological assessment
Insight into predictors of functioning after treatment for borderline personality disorder (BPD) is limited, despite growing recognition that more focus on other aspects of recovery, especially psychosocial functioning, is warranted. The present stud...

c-Triadem: A constrained, explainable deep learning model to identify novel biomarkers in Alzheimer's disease.

PloS one
Alzheimer's disease (AD) is a neurodegenerative disorder that requires early diagnosis for effective management. However, issues with currently available diagnostic biomarkers preclude early diagnosis, necessitating the development of alternative bio...

AADNet: Exploring EEG Spatiotemporal Information for Fast and Accurate Orientation and Timbre Detection of Auditory Attention Based on a Cue-Masked Paradigm.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Auditory attention decoding from electroencephalogram (EEG) could infer to which source the user is attending in noisy environments. Decoding algorithms and experimental paradigm designs are crucial for the development of technology in practical appl...

Radiation oncology patients' perceptions of artificial intelligence and machine learning in cancer care: A multi-centre cross-sectional study.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
AIM: The use of artificial intelligence (AI) and machine learning (ML) is increasingly widespread in radiation oncology. However, patient engagement to date has been poor. Respect for persons in the healthcare setting and the principle of informed co...

The role of patient outcomes in shaping moral responsibility in AI-supported decision making.

Radiography (London, England : 1995)
INTRODUCTION: Integrating decision support mechanisms utilising artificial intelligence (AI) into medical radiation practice introduces unique challenges to accountability for patient care outcomes. AI systems, often seen as "black boxes," can obscur...

Principles for enhancing trust in artificial intelligence systems among medical imaging professionals in Ghana: A nationwide cross-sectional study.

Radiography (London, England : 1995)
INTRODUCTION: To realise the full potential of artificial intelligence (AI) systems in medical imaging, it is crucial to address challenges, such as cyberterrorism to foster trust and acceptance. This study aimed to determine the principles that enha...

Machine learning models for improving the diagnosing efficiency of skeletal class I and III in German orthodontic patients.

Scientific reports
The precise and efficient diagnosis of an individual's skeletal class is necessary in orthodontics to ensure correct and stable treatment planning. However, it is difficult to efficiently determine the true skeletal class due to several correlations ...

Analysis of the Relationship Between and Cytokine Gene Expression in Hematological Malignancy: Leveraging Explained Artificial Intelligence and Machine Learning for Small Dataset Insights.

International journal of medical sciences
This study measures expression of () and related cytokine genes in bone marrow mononuclear cells in patients with hematological malignancies, analyzing the relationship between them with an integrated framework of statistical analyses, machine learn...