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IN-202541033134-A - AI-DRIVEN IMAGE RECOGNITION SYSTEM AND METHOD FOR ENHANCED MEDICAL DIAGNOSIS

IN202541033134AIN 202541033134 AIN202541033134 AIN 202541033134AIN-202541033134-A

Abstract

[033] The present invention relates to an AI-powered medical image recognition system designed to enhance diagnostic accuracy and efficiency through advanced deep learning techniques. The system integrates an image acquisition module for collecting medical images from various modalities, a preprocessing unit for quality enhancement, an AI-based image analysis engine for anomaly detection and classification, a diagnosis interpretation module providing explainable AI insights, and a secure cloud-based storage unit for seamless data access. By leveraging convolutional neural networks (CNNs) and transformer-based models, the system enables automated detection of diseases while maintaining transparency through visual annotations and confidence scores. The clinician interface allows interactive validation and feedback, ensuring continuous model improvement. The system is interoperable with hospital infrastructure, supports telemedicine applications, and complies with healthcare regulations, making it a scalable and reliable solution for AI-assisted medical diagnostics across multiple specialties. Accompanied Drawing [FIGS. 1-2]

Inventors

  • DR P KARTHIKEYAN
  • DR A SHOBANADEVI
  • DR S ARTHEESWARI
  • ARUNPRASAD NANAZGHAN SURESH
  • ER TATIRAJU V RAJANI KANTH
  • MANJUNATHAN ALAGARSAMY

Dates

Publication Date
20250425
Application Date
20250403
Priority Date
20250403

Claims (10)

  1. Claims:1. An AI-powered medical image recognition system for automated diagnosis, comprising an image acquisition module configured to collect medical images from various imaging modalities, a preprocessing unit for enhancing image quality, an AI-based image analysis engine employing deep learning techniques to detect abnormalities, a diagnosis interpretation module providing explainable AI-based insights, and a cloud-based storage unit for secure access and retrieval of diagnostic results.
  2. 2. The system of claim 1, wherein the image acquisition module is integrated with hospital picture archiving and communication systems (PACS) and electronic health record (EHR) systems to ensure seamless data retrieval and interoperability with existing healthcare infrastructure.
  3. 3. The system of claim 1, wherein the preprocessing unit applies noise reduction, contrast enhancement, artifact removal, and resolution normalization techniques to improve image quality before AI-based analysis.
  4. 4. The system of claim 1, wherein the AI-based image analysis engine utilizes convolutional neural networks (CNNs), transformer-based architectures, and hybrid AI techniques to extract features, segment regions of interest, classify detected abnormalities, and assess disease severity.
  5. 5. The system of claim 1, wherein the diagnosis interpretation module provides visual annotations, confidence scores, and contextual explanations using explainable AI (XAI) techniques to enhance transparency and aid clinicians in validating AI-generated findings.
  6. 6. The system of claim 1, wherein the cloud-based storage unit employs end-to-end encryption, multi-factor authentication, and regulatory compliance measures such as HIPAA and GDPR to ensure the secure storage and accessibility of medical images and diagnostic reports.
  7. 7. The system of claim 1, wherein a clinician interface is provided, allowing medical professionals to review AI-generated diagnostic findings, interact with annotated images, validate results, and provide feedback for continuous improvement of the AI model.
  8. 8. The system of claim 1, wherein the AI-based image recognition system is configured to integrate with telemedicine platforms, enabling remote diagnosis, expert consultations, and second-opinion evaluations from geographically distributed healthcare professionals.
  9. 9. The system of claim 1, wherein the AI-powered image analysis engine continuously improves its diagnostic accuracy by leveraging a feedback loop that incorporates corrections and expert annotations from clinicians into future model training iterations.
  10. 10. The system of claim 1, wherein the AI-based medical image recognition system is adaptable to multiple medical imaging applications, including but not limited to oncology, radiology, cardiology, orthopedics, neurology, and pathology, ensuring comprehensive diagnostic capabilities across different medical specialties.

Description

Complete SpecificationDescription:[001] The present invention pertains to the field of artificial intelligence and medical imaging technology, focusing on the application of AI-driven image recognition systems in the healthcare domain. It specifically addresses the need for enhanced accuracy and efficiency in medical diagnostics by leveraging deep learning algorithms to analyze medical images, detect abnormalities, and assist healthcare professionals in making precise clinical decisions. By integrating AI with existing imaging modalities such as MRI, CT scans, X-rays, and ultrasound, the invention aims to reduce diagnostic errors, streamline workflows, and support remote and automated medical assessments.BACKGROUND OF THE INVENTION[002] Medical imaging plays a crucial role in the early detection, diagnosis, and treatment of various diseases and medical conditions. Traditional diagnostic methods rely heavily on radiologists and medical experts to manually interpret images obtained from imaging modalities such as X-rays, magnetic resonance imaging (MRI), computed tomography (CT) scans, ultrasound, and positron emission tomography (PET) scans. While these conventional methods have been effective, they are inherently prone to human errors, misinterpretation, and inter-observer variability. Additionally, the increasing volume of medical imaging data due to advancements in medical technology has placed significant strain on healthcare professionals, leading to potential delays in diagnosis and treatment.[003] Artificial intelligence (AI) and deep learning technologies have emerged as transformative tools in the field of medical imaging. AI-based image recognition models, particularly those utilizing convolutional neural networks (CNNs) and transformer architectures, have demonstrated exceptional capabilities in analyzing complex medical images. These models can identify patterns, detect abnormalities, and classify diseases with high accuracy, often exceeding human performance in specific diagnostic tasks. However, despite these advancements, existing AI-based solutions still face challenges related to data generalization, model interpretability, and real-world clinical integration.[004] One of the major limitations of conventional medical imaging interpretation is the dependency on expert radiologists and pathologists. In many parts of the world, there is a shortage of trained medical professionals, leading to delays in diagnosis and treatment, particularly in rural and underdeveloped regions. AI-powered image recognition systems can address this issue by automating the initial stages of medical image analysis, providing rapid preliminary assessments, and flagging potentially serious conditions for further review by specialists. This can significantly reduce the burden on healthcare professionals and improve patient outcomes.[005] Another challenge in medical image analysis is the presence of noise, artifacts, and variations in image quality due to differences in imaging equipment, operator expertise, and patient conditions. AI-driven preprocessing techniques can help enhance image quality by removing noise, adjusting contrast, and standardizing image parameters, ensuring consistent and reliable diagnostic results. Furthermore, AI algorithms can learn from large-scale medical datasets, enabling them to recognize subtle disease patterns that may be overlooked by human observers.[006] Despite the benefits of AI in medical diagnosis, there are concerns regarding the reliability, transparency, and regulatory compliance of AI-based diagnostic systems. Many existing AI models function as "black boxes," providing predictions without clear explanations of their decision-making process. This lack of interpretability has raised skepticism among medical professionals regarding the trustworthiness of AI-generated diagnoses. To address this issue, explainable AI (XAI) techniques are being incorporated into AI-driven medical imaging systems, allowing clinicians to understand the reasoning behind AI predictions through visual explanations, confidence scores, and annotated images.[007] Data security and patient privacy are also critical concerns in AI-powered medical diagnostics. Medical imaging data contains sensitive patient information that must be protected in accordance with regulatory standards such as the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR). AI-based diagnostic systems must incorporate robust security measures, including data encryption, anonymization, and secure cloud storage, to ensure compliance with privacy regulations and maintain patient confidentiality.[008] The integration of AI into medical imaging workflows requires seamless interoperability with existing healthcare infrastructure, including hospital information systems (HIS), picture archiving and communication systems (PACS), and electronic health records (EHR). AI-powered systems must b