Medicine and Health Sciences Commons^™

Transfer Learning Artificial Intelligence For Automated Detection Of Atrial Fibrillation In Patients Undergoing Evaluation For Suspected Obstructive Sleep Apnoea: A Feasibility Study, Nestor Gahungu, Afsin Shariar, David Playford, Christopher Judkins, Eli Gabbay

Medical Papers and Journal Articles

Background: Individuals with obstructive sleep apnoea (OSA) experience a higher burden of atrial fibrillation (AF) than the general population, and many cases of AF remain undetected. We tested the feasibility of an artificial intelligence (AI) approach to opportunistic detection of AF from single-lead electrocardiograms (ECGs) which are routinely recorded during in-laboratory polysomnographic sleep studies.

Methods: Using transfer learning, an existing ECG AI model was applied to 1839 single-lead ECG traces recorded during in-laboratory sleep studies without any training of the algorithm. Manual review of all traces was performed by two trained clinicians who were blinded to each other's review. Discrepancies …

Machine Learning Applications To Neuroimaging For Glioma Detection And Classification: An Artificial Intelligence Augmented Systematic Review, Quinlan D. Buchlak, Nazanin Esmaili, Jean-Christophe Leveque, Christine Bennett, Farrokh Farrokhi, Massimo Piccardi

Medical Papers and Journal Articles

Glioma is the most common primary intraparenchymal tumor of the brain and the 5-year survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for detecting, characterizing and monitoring brain tumors but definitive diagnosis still relies on surgical pathology. Machine learning has been applied to the analysis of MRI data in glioma research and has the potential to change clinical practice and improve patient outcomes. This systematic review synthesizes and analyzes the current state of machine learning applications to glioma MRI data and explores the use of machine learning for systematic review automation. Various datapoints were extracted …

Ethical Thinking Machines In Surgery And The Requirement For Clinical Leadership, Quinlan D. Buchlak, Nazanin Esmaili, Jean-Christophe Leveque, Christine Bennett, Massimo Piccardi, Farrokh Farrokhi

Medical Papers and Journal Articles

No abstract available.

Investigating Risk Factors And Predicting Complications In Deep Brain Stimulation Surgery With Machine Learning Algorithms, Farrokh Farrokhi, Quinlan D. Buchlak, Matt Sikora, Nazanin Esmaili, Maria Marsans, Pamela Mcleod, Jamie Mark, Emily Cox, Christine Bennett, Jonathan Carlson

Medical Papers and Journal Articles

Background: Deep brain stimulation (DBS) surgery is an option for patients experiencing medically resistant neurological symptoms. DBS complications are rare; finding significant predictors requires a large number of surgeries. Machine learning algorithms may be used to effectively predict these outcomes. The aims of this study were to (1) investigate preoperative clinical risk factors, and (2) build machine learning models to predict adverse outcomes.

Methods: This multicenter registry collected clinical and demographic characteristics of patients undergoing DBS surgery (n=501) and tabulated occurrence of complications. Logistic regression was used to evaluate risk factors. Supervised learning algorithms were trained and validated on 70% …