arXiv subjects

Sohail Khalid

Publications and source records attributed to Sohail Khalid.

4 recordsLinked to original sources

arXiv2026

AI-Driven Clinical Decision Support System for Enhanced Diabetes Diagnosis and Management

Identifying type 2 diabetes mellitus can be challenging, particularly for primary care physicians. Clinical decision support systems incorporating artificial intelligence (AI-CDSS) can assist medical professionals in diagnosing type 2 diabetes with high accuracy. This study aimed to assess an AI-CDSS specifically developed for the diagnosis of type 2 diabetes by employing a hybrid approach that integrates expert-driven insights with machine learning techniques. The AI-CDSS was developed (training dataset: n = 650) and tested (test dataset: n = 648) using a dataset of 1298 patients with and without type 2 diabetes. To generate predictions, the algorithm utilized key features such as body mass index, plasma fasting glucose, and hemoglobin A1C. Furthermore, a clinical pilot study involving 105 patients was conducted to assess the diagnostic accuracy of the system in comparison to non-endocrinology specialists. The AI-CDSS showed a high degree of accuracy, with 99.8% accuracy in predicting diabetes, 99.3% in predicting prediabetes, 99.2% in identifying at-risk individuals, and 98.8% in predicting no diabetes. The test dataset revealed a 98.8% agreement between endocrinology specialists and the AI-CDSS. Type 2 diabetes was identified in 45% of 105 individuals in the pilot study. Compared with diabetes specialists, the AI-CDSS scored a 98.5% concordance rate, greatly exceeding that of nonendocrinology specialists, who had an 85% agreement rate. These findings indicate that the AI-CDSS has the potential to be a useful tool for accurately identifying type 2 diabetes, especially in situations in which diabetes specialists are not readily available.

cs.LG↗

arXiv2025

Advancements in Hematology Analyzers: Next-Generation Technologies for Precision Diagnostics and Personalized Medicine

Hematology analyzers are essential diagnostic and monitoring tools for detecting blood diseases. Although contemporary analyzers produce only basic insights, they are often not as detailed as required under the personalized medicine paradigm. Next-Generation Hematology Analyzers (NGHAs) are revolutionary newcomers in the field, with significant advantages over regular hematology analyzers. They provide deeper insights into cellular morphology, function, and genetic profiles. This detailed information opens up possibilities for tailor-made diagnostic and therapeutic approaches in precision medicine. This review presents some revolutionary technologies that have changed hematology analyzers and provides an overview of their limitations, basic functions, and influence on clinical practice. It focuses on the integration of state-of-the-art technologies, such as microfluidics, advanced optics, artificial intelligence, flow cytometry, and digital imaging, empowering NGHAs to improve diagnostic accuracy, rapidly detect diseases, and support flexible, targeted therapy. Hints regarding point-of-care hematology testing are also provided to discuss its implications for transforming healthcare patterns. This review highlights the data management, standardization, regulatory, and ethical challenges associated with these technologies. A review tracking the current state-of-the-art and trends for the future is provided to show how these advancements may reconfigure hematology analyzer design and act as a stepping stone for future therapeutic reforms.

q-bio.OT↗

arXiv2025

Artificial magnetic conductor backed dual-mode sectoral cylindrical DRA for off-body biomedical telemetry

This research investigates the potential of a sectoral Cylindrical Dielectric Resonator Antenna (CDRA) for biomedical telemetry. CDRAs are known for their low loss, ruggedness, and stability, but their limited bandwidth and size make them unsuitable for wearable devices. The research addresses these limitations by proposing a dual mode antenna that operates in EH110 and TE210 modes. The sectoral CDRA is a quarter segment with Perfect Electric Conductor boundaries, reducing its size by a factor of four. Mathematical derivations of the field components for both modes are derived to support the design. To minimize specific absorption rate (SAR), an Artificial Magnetic Conductor (AMC) surface is applied to the antennas backside, enhancing compatibility with the transverse electric modes. The antenna achieves a bandwidth of 0.7 GHz (5.2-5.9 GHz), suitable for biomedical applications, with a measured peak gain of 7.9 dBi and a SAR of 1.24 W/kg when applied to a human arm.

eess.SY↗

arXiv2025

Power Transformer Health Index and Life Span Assessment: A Comprehensive Review of Conventional and Machine Learning based Approaches

Power transformers play a critical role within the electrical power system, making their health assessment and the prediction of their remaining lifespan paramount for the purpose of ensuring efficient operation and facilitating effective maintenance planning. This paper undertakes a comprehensive examination of existent literature, with a primary focus on both conventional and cutting-edge techniques employed within this domain. The merits and demerits of recent methodologies and techniques are subjected to meticulous scrutiny and explication. Furthermore, this paper expounds upon intelligent fault diagnosis methodologies and delves into the most widely utilized intelligent algorithms for the assessment of transformer conditions. Diverse Artificial Intelligence (AI) approaches, including Artificial Neural Networks (ANN) and Convolutional Neural Network (CNN), Support Vector Machine (SVM), Random Forest (RF), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), are elucidated offering pragmatic solutions for enhancing the performance of transformer fault diagnosis. The amalgamation of multiple AI methodologies and the exploration of timeseries analysis further contribute to the augmentation of diagnostic precision and the early detection of faults in transformers. By furnishing a comprehensive panorama of AI applications in the field of transformer fault diagnosis, this study lays the groundwork for future research endeavors and the progression of this critical area of study.

cs.LG↗