Radiology, a niche specialty of medicine, has evolved from the mere radiograph of a hand to organ anatomical visualization, moving to molecular and functional imaging. It has been at the forefront of the intersection of health and technology. The feather in the cap of this specialty is digitization and assisted learning offered by Artificial intelligence and Deep Machine Learning for personalized care in oncology. AI refers to machines and systems' ability to acquire and apply knowledge to perform a variety of cognitive tasks (e.g., sensing, processing oral language, reasoning, learning, and making decisions), basically mimicking human behavior. The potential for AI in the era of precision oncology is tremendous, with important diagnostic, prognostic, and management implications.
The workshop is a guiding step for novice and amateur radiologists to embark righteously on a journey of technology based on AI. The sessions introduces participants to Machine learning, Deep learning and Convolutional Neural Network. It builds upon the basics of Radiomics and elucidates the associated ethical dimensions of AI in healthcare.
The emerging application of NGS technology under clinical settings provide an unprecedented opportunity to classify a patient based on their tumor’s molecular changes with improved accuracy to design the right targeted therapy. However, the sheer magnitude of the number of variables in these data pose a formidable computational challenge to analyze the abundant genomic data and derive clinical sense. Also, integrating the heterogeneous information in multiple clinical datasets and genomic datasets presents an arduous challenge that impedes its universal application. This workshop is focussed to explore the utility of an artificial intelligence-based decision support system developed at ACTREC, Tata Memorial Centre—ClinOme--that utilizes both the clinical features and the genomic profile of a cancer patient to assist the physician in integrating information about a specific patient. The module is designed as a primer for the clinicians to introduce the complexity of the NGS dataset and its automated analysis to derive a clinically relevant information with no pre-requisite of computational familiarity. This 1 hour workshop is drafted to develop an easier grasp and understanding of the basics split under the following headings:
This virtual pre-conference molecular diagnostic workshop is designed to give participants a run-through from the basic genomics diving into the high-end next generation sequencing (NGS) testing. The didactic lectures include basic workflow of NGS testing, HGVS nomenclature, reporting guidelines and recommendations, followed by a comprehensive review of the bioinformatic data and downstream analysis. The topics covered are aimed to focus on the current trends, practical challenges, concerns and solutions adopted in the fields of Molecular Pathology in clinical settings. This workshop is aimed towards trainee pathologists, academic pathologists, oncologists, researchers and scientists.
This workshop will provide the participants with the understanding of the nuts and bolts required to setup, validate and use digital pathology (DP) in clinical practice. The latter part of the workshop will focus on introducing the deep learning and machine learning concepts required for computational pathology, primarily from the pathologist perspective. It will also focus on the strengths and limitations of DP for primary diagnosis, AI algorithms, generalizability and the problems a pathologist should be aware of prior to adopting it in clinical practice. This cutting-edge workshop is a prelude to the main conference and will be of immense relevance to the attendees.