AIMC Topic: Female

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Effectiveness of Robotic Intervention on Improving Social Development and Participation of Children with Autism Spectrum Disorder - A Randomised Controlled Trial.

Journal of autism and developmental disorders
Evidence-based robotic intervention programmes for children with autism spectrum disorder (ASD) have been limited. As yet, there is insufficient evidence to inform therapists, teachers, and service providers on effectiveness of robotic intervention t...

Deep learning-based osteochondritis dissecans detection in ultrasound images with humeral capitellum localization.

International journal of computer assisted radiology and surgery
PURPOSE: Osteochondritis dissecans (OCD) of the humeral capitellum is a common cause of elbow disorders, particularly among young throwing athletes. Conservative treatment is the preferred treatment for managing OCD, and early intervention significan...

Deep learning algorithm for fully automated measurement of sagittal balance in adult spinal deformity.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
AIM: Deep learning (DL) algorithms can be used for automated analysis of medical imaging. The aim of this study was to assess the accuracy of an innovative, fully automated DL algorithm for analysis of sagittal balance in adult spinal deformity (ASD)...

MI-DenseCFNet: deep learning-based multimodal diagnosis models for Aureus and Aspergillus pneumonia.

European radiology
OBJECTIVE: To build and merge a diagnostic model called multi-input DenseNet fused with clinical features (MI-DenseCFNet) for discriminating between Staphylococcus aureus pneumonia (SAP) and Aspergillus pneumonia (ASP) and to evaluate the significant...

Trust in Machine Learning Driven Clinical Decision Support Tools Among Otolaryngologists.

The Laryngoscope
BACKGROUND: Machine learning driven clinical decision support tools (ML-CDST) are on the verge of being integrated into clinical settings, including in Otolaryngology-Head & Neck Surgery. In this study, we investigated whether such CDST may influence...

Predicting Penicillin Allergy: A United States Multicenter Retrospective Study.

The journal of allergy and clinical immunology. In practice
BACKGROUND: Using the reaction history in logistic regression and machine learning (ML) models to predict penicillin allergy has been reported based on non-US data.

A Machine Learning Approach to Predict Post-stroke Fatigue. The Nor-COAST study.

Archives of physical medicine and rehabilitation
OBJECTIVE: This study aimed to predict fatigue 18 months post-stroke by utilizing comprehensive data from the acute and sub-acute phases after stroke in a machine-learning set-up.

Prediction of Disease-Free Survival in Breast Cancer using Deep Learning with Ultrasound and Mammography: A Multicenter Study.

Clinical breast cancer
BACKGROUND: Breast cancer is a leading cause of cancer morbility and mortality in women. The possibility of overtreatment or inappropriate treatment exists, and methods for evaluating prognosis need to be improved.

Prior Clinico-Radiological Features Informed Multi-Modal MR Images Convolution Neural Network: A novel deep learning framework for prediction of lymphovascular invasion in breast cancer.

Cancer medicine
BACKGROUND: Current methods utilizing preoperative magnetic resonance imaging (MRI)-based radiomics for assessing lymphovascular invasion (LVI) in patients with early-stage breast cancer lack precision, limiting the options for surgical planning.