Tech Healthcare SolutionsTransforming Healthcare Through Predictive Maintenance

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Medical Imaging Analysis

AI assisted Radiology workflow for early disease detection fostered AI models to train on millions of abnormalities with high precision. This resulted in reduced diagnostic time by 50% and improved early detection of disease like cancer.

Digital Twins in Healthcare

We’ve developed a breakthrough solution to address the limitations of traditional treatments for complex and rare diseases. By creating a digital twin of each patient an AI-powered replica built from real-time health data we enable truly personalized care. This approach allows for treatment plans that are precisely tailored to the individual, improving outcomes and minimizing side effects.

Rare Disease Identification with AI

Rare diseases often remain undiagnosed for years, largely due to their complexity and limited awareness. Our solution uses natural language processing and image analysis to evaluate patient symptoms and diagnostics against a comprehensive database of rare diseases. This dramatically shortens the time to diagnosis from years to just weeks.

Blockchain for Medical Data Sharing

Hospitals often face difficulties in sharing patient data for research due to strict privacy regulations and security concerns. To address this, we developed a blockchain-based system that enables secure, transparent, and tamper-proof data sharing. This solution enhances collaboration between healthcare institutions while safeguarding patient privacy and ensuring the integrity of sensitive health information.

AI In Complex Surgical Workflows

Precision is vital in surgeries involving the brain or spine, yet achieving it remains a significant challenge. We’ve developed AI-powered navigation systems that leverage real-time imaging and predictive analysis to guide surgeons with greater accuracy. This innovation significantly reduces surgical errors and enhances precision in even the most complex procedures.

AI Driven Drug Discovery

Developing drugs for rare diseases is both expensive and time-consuming. Our solution uses AI models to simulate how potential compounds interact with disease markers, streamlining the discovery process. This approach accelerates drug development, reducing the time to market by 40%.

Predictive Maintenance with AI

Unexpected equipment failures can significantly disrupt patient care and delay hospital operations. To address this, we use IoT and AI for predictive maintenance, allowing for real-time monitoring of critical hospital equipment. This proactive approach reduces downtime by 70% and extends the lifespan of the equipment, ensuring more reliable functionality and ultimately enhancing the quality of patient care.

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Used Cases- AI Medical Diagnosis with ResNet50

Brain Tumor

Brain tumors such as glioma, meningioma, and pituitary tumors can be life-threatening if not detected early. To improve early diagnosis, we use the ResNet50 deep learning model to analyse brain MRI scans with high accuracy. MRI images are resized to 200×200 pixels and enhanced using data augmentation techniques like rotation and flipping to improve model performance and reduce overfitting. Transfer learning with pre-trained weights helps the model generalize better across datasets. As a result, the system effectively classifies scans into categories including glioma, meningioma, pituitary tumor, and no tumor. This approach offers a fast, reliable tool for early detection and supports clinical decision-making.

Blood Cancer Classification

The model (using ResNet50 architecture) efficiently classifies blood cancer types into benign, malignant pre-B, malignant pro-B and malignant early pre-B conditions. It aids clinicians in the early detection of blood cancers, providing and automated tool to assist in diagnosis. The system shows promising results in differentiating between various forms of blood cancer, supporting timely and accurate treatment decisions.

Eye Disease Classification

Eye diseases such as cataracts, diabetic retinopathy and glaucoma are significant contributors to vision impairment worldwide. Early detection of these conditions is crucial for effective management and treatment. Thus, we utilize ResNet50, a deep convolutional neural network, for feature extraction and classification of eye conditions. The model successfully classifies images with a high degree of accuracy.

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