Use case of no code machine learning tools for medical image classification.

Abstract

Abstract AI tools are making paradigm chanegs in the field of medical imaging. Currently, the development of AI tools and their validation for clinical use is heterogenously distributed with the end-users (i.e the physians or radiologists) adopting the software solution. As we are all progressing towards democratization of AI, no code tools offer a versatile and convenient; but largely under-utilized method for medical imaging tasks. Purpose: As a proof-of-concept study, we attempted to evaluate whether no-code machine learning (ML) tools like teachable machine could perform a basic medical image classification task. Methods: We selected 85 cases from our imaging database whose planar whole body Iodine-131 diagnostic scans were labelled into 2 classes as “No evidence of disease” (NED) and “abnormal for training and testing the model. Results: The model generated could accurately classify all NED cases (100%) and abnormal cases with 93% accuracy. Conclusion: We propose that no-code ML tools can perform simple medical image tasks easily. Validation on multiple source larger datasets may allow early adoption of this technology by imaging specialists.