Artificial Intelligence / Machine Learning in Healthcare
Key Proposal Details
NDHM based Federated Apps for AI based epidemiological predictions for COVID-19, Antimicrobial Resistance, Tuberculosis, and Maternal and Child Health. Tie-ups with mohalla clinics to identify spikes in illnesses.
Molecular surveillance. Sequencing of sewage leading to identification and mapping of pathogens. Proposals for point-of-care pore-based sequencers for faster diagnostics that include variant identification and where possible drug resistance mapping.
AI algorithms for optimizing supply chain & resource allocation. Optimization, reinforcement learning and Deep Q Networks based open sourced platform.
Cancer Omics Biomarkers. Using AI to identify biomarker panels for cancer (genes, proteins, metabolites, mutations, copy number). qRT-PCR/CRISPR/microfluidic-based diagnostics for early cancer detection. At least 1 technology transfer.
COVID-19 Interaction database. All genetic variations of covid S-protein against all known antibodies. Database for the mode of action incurred by the genetic variations of S-protein against ACE2 human receptor.
Machine learning models for Diagnosis, Staging, and Prognosis of Rare Neuromuscular Disorders
Medical image processing. Cancer diagnosis, monitoring, Imaging biomarkers, Grading, metastasis monitoring and prognostication. Magnetic Resonance Imaging protocol designing. Automation of pathology imaging and microbial typing using microscopy images. 3D cell microscopy and its applications in healthcare. Workshop in medical image processing with artificial intelligence and clinical applications.
Rehabilitative technology development. Indigenous implants for spinal surgery rehabilitation
Precision medicine. Developing polygenic risk score models for precision medicine, disease stratification, and patient triaging using genomic data. (2) Precision oncology for a better understanding of India specific issues to develop better detection, diagnosis, and therapeutic intervention.
Mental Health. Management using AI model. Understanding the correlation and causation of mental problems, develop risk stratification algorithms, Developing cognitive manipulation techniques to mitigate these problems, Developing AI models for rehabilitation of people at risk.
Content creation for training, skilling. Education modules for training school/college students and teachers in AI/ML for healthcare