AIMC Topic: Humans

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Integration of bulk/scRNA-seq and multiple machine learning algorithms identifies PIM1 as a biomarker associated with cuproptosis and ferroptosis in abdominal aortic aneurysm.

Frontiers in immunology
BACKGROUND: Abdominal aortic aneurysm (AAA) is a serious life-threatening vascular disease, and its ferroptosis/cuproptosis markers have not yet been characterized. This study was aiming to identify markers associated with ferroptosis/cuproptosis in ...

AI frontiers in emergency care: the next evolution of nursing interventions.

Frontiers in public health
This scoping review explores the utilization of artificial intelligence in emergency nursing, assessing its impact, potential benefits, and the obstacles faced in its adoption. It covers the scope of AI from advanced triage protocols to continuous mo...

BUSClean: Open-source software for breast ultrasound image pre-processing and knowledge extraction for medical AI.

PloS one
Development of artificial intelligence (AI) for medical imaging demands curation and cleaning of large-scale clinical datasets comprising hundreds of thousands of images. Some modalities, such as mammography, contain highly standardized imaging. In c...

ResNet-Vision Transformer based MRI-endoscopy fusion model for predicting treatment response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: A multicenter study.

Chinese medical journal
BACKGROUND: Neoadjuvant chemoradiotherapy followed by radical surgery has been a common practice for patients with locally advanced rectal cancer, but the response rate varies among patients. This study aimed to develop a ResNet-Vision Transformer ba...

Machine learning derived physical activity in preschool children with developmental coordination disorder.

Developmental medicine and child neurology
AIM: To compare the device-measured physical activity behaviours of preschool children with typical motor development to those with probable developmental coordination disorder (pDCD) and at risk for developmental coordination disorder (DCDr).

Assessment of image quality on the diagnostic performance of clinicians and deep learning models: Cross-sectional comparative reader study.

Journal of the European Academy of Dermatology and Venereology : JEADV
BACKGROUND: Skin cancer is a prevalent and clinically significant condition, with early and accurate diagnosis being crucial for improved patient outcomes. Dermoscopy and artificial intelligence (AI) hold promise in enhancing diagnostic accuracy. How...

Rhabdomyosarcoma Classification: Refining Our Understanding.

Surgical pathology clinics
Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma in children, representing approximately 40% of all pediatric soft tissue sarcomas and 3% of all pediatric cancers. RMSs are a heterogeneous group of neoplasms with variations in morphology...

Digital Pathology-based Artificial Intelligence Biomarker Validation in Metastatic Prostate Cancer.

European urology oncology
BACKGROUND AND OBJECTIVE: Owing to the expansion of treatment options for metastatic hormone-sensitive prostate cancer (mHSPC) and an appreciation of clinical subgroups with differential prognosis and treatment responses, prognostic and predictive bi...

The role of deep learning in diagnostic imaging of spondyloarthropathies: a systematic review.

European radiology
AIM: Diagnostic imaging is an integral part of identifying spondyloarthropathies (SpA), yet the interpretation of these images can be challenging. This review evaluated the use of deep learning models to enhance the diagnostic accuracy of SpA imaging...

Evaluating the efficacy of AI systems in diabetic retinopathy detection: A comparative analysis of Mona DR and IDx-DR.

Acta ophthalmologica
PURPOSE: To compare two artificial intelligence (AI)-based Automated Diabetic Retinopathy Image Assessment (ARIA) softwares in terms of concordance with specialist human graders and referable diabetic retinopathy (DR) diagnostic capacity.