Sami Qadri, an MD PhD student from the University of Helsinki, is part of a research group studying nonalcoholic fatty liver disease (NAFLD). The group has recently started using AI to assist them in analyzing liver biopsies.
Read Sami's interview below.
"I work in Professor Hannele Yki-Järvinen’s research group. Our main focus of investigation is nonalcoholic fatty liver disease (NAFLD), which has emerged as the most common liver disease worldwide.
My personal studies aim to shine a light on the effects of particular genetic determinants on NAFLD and the mechanisms by which NAFLD arises in these genetically predisposed subjects. A part of this work entails harnessing AI to analyze liver biopsies in completely new ways worldwide."
"Most importantly, Aiforia enables segmentation of both normal and pathological structures in liver histology in a way that allows us to accurately quantitate these features in multiple different ways. The traditional method of analyzing liver histology, i.e., visual assessment by pathologists, is rather problematic from the standpoint of medical research.
First, pathologists can at best give us semi-quantitative assessments with regard to the amount or extent of pathology that is present. Aiforia grants us access to truly quantitative measures with continuous metrics instead of arbitrary grading.
Second, we know that the ‘internal calibration’ among pathologists may differ, leading to marked observer-related variability in assessments. For research purposes, these inconsistencies are naturally problematic. Lastly, with Aiforia, we will gain access to completely new kinds of metrics, like quantifying the shape or size of individual lesions or their spatial relationships."
"It was difficult to weigh my expectations as I had never worked with AI before. After seeing the platform’s capabilities, however, I was thoroughly excited from the onset. I am glad to say that all of those expectations have been met and even exceeded!"
"I knew about the basics of AI in general but very little in practice. One misconception I had was the thought that it would take lots of training material for an AI model to be effective. This turned out to be incorrect, as Aiforia’s image segmentation becomes surprisingly specific with just a few annotated examples. Of course, for a robust AI model, you will need variability, but a rough working model can be devised in a matter of minutes."
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