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Full-Text Articles in Medicine and Health Sciences

Machine Learning Guided Postnatal Gestational Age Assessment Using New-Born Screening Metabolomic Data In South Asia And Sub-Saharan Africa, Sunil Sazawal, Kelli K. Ryckman, Sayan Das, Muhammad Imran Nisar, Usma Mehmood, Amina Barkat, Farah Khalid, Muhammad Ilyas Muhammad Ilyas, Ambreen Nizar, Fyezah Jehan Sep 2021

Machine Learning Guided Postnatal Gestational Age Assessment Using New-Born Screening Metabolomic Data In South Asia And Sub-Saharan Africa, Sunil Sazawal, Kelli K. Ryckman, Sayan Das, Muhammad Imran Nisar, Usma Mehmood, Amina Barkat, Farah Khalid, Muhammad Ilyas Muhammad Ilyas, Ambreen Nizar, Fyezah Jehan

Department of Paediatrics and Child Health

Background: Babies born early and/or small for gestational age in Low and Middle-income countries (LMICs) contribute substantially to global neonatal and infant mortality. Tracking this metric is critical at a population level for informed policy, advocacy, resources allocation and program evaluation and at an individual level for targeted care. Early prenatal ultrasound examination is not available in these settings, gestational age (GA) is estimated using new-born assessment, last menstrual period (LMP) recalls and birth weight, which are unreliable. Algorithms in developed settings, using metabolic screen data, provided GA estimates within 1-2 weeks of ultrasonography-based GA. We sought to leverage machine …


Deep Learning Applications In Neuro-Oncology, Adnan Khan, Hamza Ibad, Kaleem Ahmed, Zahra Hoodbhoy, Muhammad Shahzad Shamim Aug 2021

Deep Learning Applications In Neuro-Oncology, Adnan Khan, Hamza Ibad, Kaleem Ahmed, Zahra Hoodbhoy, Muhammad Shahzad Shamim

Medical College Documents

Deep learning (DL) is a relatively newer subdomain of machine learning (ML) with incredible potential for certain applications in the medical field. Given recent advances in its use in neuro-oncology, its role in diagnosing, prognosticating, and managing the care of cancer patients has been the subject of many research studies. The gamut of studies has shown that the landscape of algorithmic methods is constantly improving with each iteration from its inception. With the increase in the availability of high-quality data, more training sets will allow for higher fidelity models. However, logistical and ethical concerns over a prospective trial comparing prognostic …


Diagnostic Accuracy Of Machine Learning Models To Identify Congenital Heart Disease: A Meta-Analysis, Zahra Hoodbhoy, Uswa Jiwani, Saima Sattar, Rehana A. Salam, Babar Hasan, Jai K. Das Jul 2021

Diagnostic Accuracy Of Machine Learning Models To Identify Congenital Heart Disease: A Meta-Analysis, Zahra Hoodbhoy, Uswa Jiwani, Saima Sattar, Rehana A. Salam, Babar Hasan, Jai K. Das

Department of Paediatrics and Child Health

Background: With the dearth of trained care providers to diagnose congenital heart disease (CHD) and a surge in machine learning (ML) models, this review aims to estimate the diagnostic accuracy of such models for detecting CHD.
Methods: A comprehensive literature search in the PubMed, CINAHL, Wiley Cochrane Library, and Web of Science databases was performed. Studies that reported the diagnostic ability of ML for the detection of CHD compared to the reference standard were included. Risk of bias assessment was performed using Quality Assessment for Diagnostic Accuracy Studies-2 tool. The sensitivity and specificity results from the studies were used to …