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Daily Archives: April 25, 2026

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Artificial Intelligence In Achromatopsia: A Comprehensive Review Of Diagnosis, Genetic Insights And Emerging Therapeutic Strategies

Authors: Dr. A Senthilkumar, Dr. Tintu George, Dr. Ginne M James

Abstract: Achromatopsia is a rare inherited retinal disorder characterized by the absence of cone photoreceptor function, resulting in color blindness, photophobia, nystagmus, and reduced visual acuity. Recent advances in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), have transformed ophthalmic diagnostics and opened new avenues for early detection and treatment planning. This review paper presents a comprehensive analysis of achromatopsia, focusing on its clinical features, genetic basis, diagnostic approaches and therapeutic developments, with a strong emphasis on AI-driven methodologies. The paper also explores the integration of AI in retinal imaging, genotype–phenotype correlation, and gene therapy optimization. Finally, challenges, limitations and future research directions are discussed.

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EV Battery Management System With Charge Monitoring

Authors: N. Santosh Kumar, U. Reshma, V. Sandhya, S. Santhi vardhan

Abstract: This project presents an innovative Battery Management System (BMS) for electric vehicles, leveraging Arduino UNO to ensure optimal battery performance, safety, and longevity. The system is designed to monitor critical parameters such as voltage, current, temperature, and state of charge (SOC), while also assessing the state of health (SOH) of the battery. The BMS supports both fast and slow charging modes, intelligently managing charging processes to prevent overcharging and thermal runaway. User-friendly interfaces, including real-time data displays, offer intuitive insights into battery status, empowering users with actionable information. This project combines cost-effective hardware with comprehensive safety and performance monitoring, making it a significant step toward safer and more efficient electric vehicle batteries. Real-time SOC/SOH evaluation, and versatile charging management, which collectively advance the capabilities of conventional battery management solutions.

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