Dataset Information

Multimodal NASH prognosis using 3D imaging flow cytometry and artificial intelligence to characterize liver cells.

ABSTRACT: To improve the understanding of the complex biological process underlying the development of non-alcoholic steatohepatitis (NASH), 3D imaging flow cytometry (3D-IFC) with transmission and side-scattered images were used to characterize hepatic stellate cell (HSC) and liver endothelial cell (LEC) morphology at single-cell resolution. In this study, HSC and LEC were obtained from biopsy-proven NASH subjects with early-stage NASH (F2-F3) and healthy controls. Here, we applied single-cell imaging and 3D digital reconstructions of healthy and diseased cells to analyze a spatially resolved set of morphometric cellular and texture parameters that showed regression with disease progression. By developing a customized autoencoder convolutional neural network (CNN) based on label-free cell transmission and side scattering images obtained from a 3D imaging flow cytometer, we demonstrated key regulated cell types involved in the development of NASH and cell classification performance superior to conventional machine learning methods.

SUBMITTER: Subramanian R

PROVIDER: S-EPMC9249889 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Multimodal NASH prognosis using 3D imaging flow cytometry and artificial intelligence to characterize liver cells.

Subramanian Ramkumar R Tang Rui R Zhang Zunming Z Joshi Vaidehi V Miner Jeffrey N JN Lo Yu-Hwa YH

Scientific reports 20220701 1

To improve the understanding of the complex biological process underlying the development of non-alcoholic steatohepatitis (NASH), 3D imaging flow cytometry (3D-IFC) with transmission and side-scattered images were used to characterize hepatic stellate cell (HSC) and liver endothelial cell (LEC) morphology at single-cell resolution. In this study, HSC and LEC were obtained from biopsy-proven NASH subjects with early-stage NASH (F2-F3) and healthy controls. Here, we applied single-cell imaging an ...[more]

PMID: 35778474

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Project description:ImportanceMultimodal artificial intelligence (AI) chatbots can process complex medical image and text-based information that may improve their accuracy as a clinical diagnostic and management tool compared with unimodal, text-only AI chatbots. However, the difference in medical accuracy of multimodal and text-only chatbots in addressing questions about clinical oncology cases remains to be tested.ObjectiveTo evaluate the utility of prompt engineering (zero-shot chain-of-thought) and compare the competency of multimodal and unimodal AI chatbots to generate medically accurate responses to questions about clinical oncology cases.Design, setting, and participantsThis cross-sectional study benchmarked the medical accuracy of multiple-choice and free-text responses generated by AI chatbots in response to 79 questions about clinical oncology cases with images.ExposuresA unique set of 79 clinical oncology cases from JAMA Network Learning accessed on April 2, 2024, was posed to 10 AI chatbots.Main outcomes and measuresThe primary outcome was medical accuracy evaluated by the number of correct responses by each AI chatbot. Multiple-choice responses were marked as correct based on the ground-truth, correct answer. Free-text responses were rated by a team of oncology specialists in duplicate and marked as correct based on consensus or resolved by a review of a third oncology specialist.ResultsThis study evaluated 10 chatbots, including 3 multimodal and 7 unimodal chatbots. On the multiple-choice evaluation, the top-performing chatbot was chatbot 10 (57 of 79 [72.15%]), followed by the multimodal chatbot 2 (56 of 79 [70.89%]) and chatbot 5 (54 of 79 [68.35%]). On the free-text evaluation, the top-performing chatbots were chatbot 5, chatbot 7, and the multimodal chatbot 2 (30 of 79 [37.97%]), followed by chatbot 10 (29 of 79 [36.71%]) and chatbot 8 and the multimodal chatbot 3 (25 of 79 [31.65%]). The accuracy of multimodal chatbots decreased when tested on cases with multiple images compared with questions with single images. Nine out of 10 chatbots, including all 3 multimodal chatbots, demonstrated decreased accuracy of their free-text responses compared with multiple-choice responses to questions about cancer cases.Conclusions and relevanceIn this cross-sectional study of chatbot accuracy tested on clinical oncology cases, multimodal chatbots were not consistently more accurate than unimodal chatbots. These results suggest that further research is required to optimize multimodal chatbots to make more use of information from images to improve oncology-specific medical accuracy and reliability.

Dataset Information

Multimodal NASH prognosis using 3D imaging flow cytometry and artificial intelligence to characterize liver cells.

Publications

Multimodal NASH prognosis using 3D imaging flow cytometry and artificial intelligence to characterize liver cells.

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