collaborators

10 papers

eess.IV2025

Fracture Detection and Localisation in Wrist and Hand Radiographs using Detection Transformer Variants

Aditya Bagri, Vasanthakumar Venugopal, Anandakumar D +7

Background: Accurate diagnosis of wrist and hand fractures using radiographs is essential in emergency care, but manual interpretation is slow and prone to errors. Transformer-base…

cs.CV2025

A Deep Learning-Based Ensemble System for Automated Shoulder Fracture Detection in Clinical Radiographs

Hemanth Kumar M, Karthika M, Saianiruth M +8

Background: Shoulder fractures are often underdiagnosed, especially in emergency and high-volume clinical settings. Studies report up to 10% of such fractures may be missed by radi…

eess.IV2025

Autonomous AI for Multi-Pathology Detection in Chest X-Rays: A Multi-Site Study in the Indian Healthcare System

Bargava Subramanian, Shajeev Jaikumar, Praveen Shastry +6

Study Design: The study outlines the development of an autonomous AI system for chest X-ray (CXR) interpretation, trained on a vast dataset of over 5 million X rays sourced from he…

eess.IV2025

Vision-Language Models for Acute Tuberculosis Diagnosis: A Multimodal Approach Combining Imaging and Clinical Data

Ananya Ganapthy, Praveen Shastry, Naveen Kumarasami +7

Background: This study introduces a Vision-Language Model (VLM) leveraging SIGLIP and Gemma-3b architectures for automated acute tuberculosis (TB) screening. By integrating chest X…

eess.IV2025

A Multi-Site Study on AI-Driven Pathology Detection and Osteoarthritis Grading from Knee X-Ray

Bargava Subramanian, Naveen Kumarasami, Praveen Shastry +6

Introduction: Bone health disorders like osteoarthritis and osteoporosis pose major global health challenges, often leading to delayed diagnoses due to limited diagnostic tools. Th…

eess.IV2025

AI-Driven MRI Spine Pathology Detection: A Comprehensive Deep Learning Approach for Automated Diagnosis in Diverse Clinical Settings

Bargava Subramanian, Naveen Kumarasami, Praveen Shastry +7

Study Design: This study presents the development of an autonomous AI system for MRI spine pathology detection, trained on a dataset of 2 million MRI spine scans sourced from diver…