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Our research underpins 5C Network's Generalised Medical AI framework and Hybrid Intelligence approach to radiology.

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

2025-04-01
Artificial Intelligence

A comprehensive multi-site study evaluating autonomous AI systems for multi-pathology detection in chest X-rays within the Indian healthcare system. This research demonstrates the effectiveness of AI-driven diagnostic tools in diverse clinical settings across India, addressing the critical need for scalable radiology solutions in resource-constrained environments.

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

2025-03-31
Clinical

This study presents a novel vision-language model approach for acute tuberculosis diagnosis, combining chest X-ray imaging with clinical data for enhanced diagnostic accuracy. The multimodal framework demonstrates significant improvements in TB detection rates, particularly in early-stage identification and differential diagnosis scenarios.

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

2025-03-30
Medical Imaging

A comprehensive deep learning framework for automated MRI spine pathology detection, designed to work effectively across diverse clinical settings and patient populations. The system incorporates advanced neural network architectures and attention mechanisms to identify subtle spinal abnormalities with high sensitivity and specificity.

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Advancing Chronic Tuberculosis Diagnostics Using Vision-Language Models: A Multimodal Framework for Precision Analysis

2025-03-29
Deep Learning

This research advances chronic tuberculosis diagnostics through innovative vision-language models, creating a multimodal framework for precision analysis. The approach combines advanced computer vision techniques with natural language processing to improve diagnostic accuracy for chronic TB cases, addressing a critical gap in current diagnostic methodologies.

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

2025-03-28
Computer Vision

A comprehensive multi-site study evaluating AI-driven pathology detection and osteoarthritis grading from knee X-ray images. The research demonstrates the effectiveness of automated systems in identifying joint pathologies and grading osteoarthritis severity, providing valuable tools for orthopedic diagnosis and treatment planning.

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3D Convolutional Neural Networks for Improved Detection of Intracranial Bleeding in CT Imaging

2025-03-27
Machine Learning

This study presents advanced 3D convolutional neural networks for improved detection of intracranial bleeding in CT imaging. The research focuses on emergency radiology applications, where rapid and accurate detection of intracranial hemorrhage is critical for patient outcomes and treatment decisions.

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AI and Deep Learning for Automated Segmentation and Quantitative Measurement of Spinal Structures in MRI

2025-03-26
Clinical

A comprehensive study on AI and deep learning applications for automated segmentation and quantitative measurement of spinal structures in MRI. The research provides tools for precise anatomical analysis, supporting clinical decision-making in spinal surgery and treatment planning.

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Deep Learning-Based Automated Workflow for Accurate Segmentation and Measurement of Abdominal Organs in CT Scans

2025-03-25
Artificial Intelligence

This research presents a deep learning-based automated workflow for accurate segmentation and measurement of abdominal organs in CT scans. The system provides comprehensive organ analysis capabilities, supporting diagnostic workflows and treatment planning in abdominal imaging.

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

2025-07-31
Medical Imaging

A deep learning-based ensemble system for automated shoulder fracture detection in clinical radiographs. This research addresses the critical need for rapid and accurate fracture identification in emergency and orthopedic settings, providing valuable decision support tools for clinicians.

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