ChatFFA: An ophthalmic chat system for unified vision-language understanding and question answering for fundus fluorescein angiography.
iScience
; 27(7): 110021, 2024 Jul 19.
Article
en En
| MEDLINE
| ID: mdl-39055931
ABSTRACT
Existing automatic analysis of fundus fluorescein angiography (FFA) images faces limitations, including a predetermined set of possible image classifications and being confined to text-based question-answering (QA) approaches. This study aims to address these limitations by developing an end-to-end unified model that utilizes synthetic data to train a visual question-answering model for FFA images. To achieve this, we employed ChatGPT to generate 4,110,581 QA pairs for a large FFA dataset, which encompassed a total of 654,343 FFA images from 9,392 participants. We then fine-tuned the Bootstrapping Language-Image Pre-training (BLIP) framework to enable simultaneous handling of vision and language. The performance of the fine-tuned model (ChatFFA) was thoroughly evaluated through automated and manual assessments, as well as case studies based on an external validation set, demonstrating satisfactory results. In conclusion, our ChatFFA system paves the way for improved efficiency and feasibility in medical imaging analysis by leveraging generative large language models.
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Bases de datos:
MEDLINE
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En
Revista:
IScience
Año:
2024
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Article
País de afiliación:
Hong Kong