Imagine a world where medical diagnosis is faster, more accurate, and more effective. This is the vision of the Department of Biotechnology (DBT), which is launching an ambitious initiative to create a network of image biobanks backed by artificial intelligence (AI) algorithms. The goal is to improve medical screenings, precision medicine, and assess patient risk factors better.
Unlocking the Potential of Image Biobanks
Image biobanks are organised databases of medical images and associated imaging biomarkers of radiology and beyond. The DBT is calling on eligible research institutions, private institutions, and non-governmental organisations to set up Image Informatic Centres for the collection, storage, and retrieval of diagnostic images and associated clinical data. The initiative is seeking innovative proposals to create imaging biobanks that focus on curating high-quality, annotated image databases.
Powering AI Algorithms with High-Quality Datasets
The dataset generated by these image biobanks will serve as the cornerstone for building powerful AI algorithms. The DBT is prioritising projects that combine diverse clinical expertise to improve image metadata and develop sophisticated AI tools for image analysis. The ultimate goal is to create a network of image biobanks that can be integrated with existing bio-banking networks.
Driving Breakthroughs in Medical Diagnosis
The scope of the initiative includes making the data available to the research community through a user-friendly online interface. The data will be used to develop algorithms for disease prediction, diagnosis, and prognosis using the collected data and publishing the results. The DBT envisions a future where AI-powered image analysis enables doctors to diagnose diseases more accurately and quickly, reducing the need for invasive procedures and improving patient outcomes.
New Frontiers in Medical Imaging
The image biobanks will also explore new frontiers in medical imaging, including the analysis of imaging data from normal images/tissues or triaging cases that otherwise need the expertise of an experienced radiologist/pathologist. Additionally, the initiative will consider proposals that develop data banks with direct relevance for medical applications, such as biological samples, electron microscopy, plant leaves, grain quality check, and other multi-scale biological systems.
Conclusion
The DBT’s image biobank initiative has the potential to revolutionise medical diagnosis and transform the way doctors approach patient care. With the power of AI backing, this endeavour can unlock new insights and breakthroughs in precision medicine, disease diagnosis, and patient risk factor assessment. As the DBT invites proposals from researchers and institutions, the possibilities for innovation and collaboration are endless.
Originally published on https://www.pharmabiz.com/NewsDetails.aspx?aid=180271&sid=1
