Ophthalmology Science (Jul 2024)
A User-friendly Approach for the Diagnosis of Diabetic Retinopathy Using ChatGPT and Automated Machine Learning
Abstract
Purpose: To assess the capabilities of Chat Generative Pre-trained Transformer (ChatGPT) and Vertex AI in executing code-free preprocessing, training machine learning (ML) models, and analyzing the data. Design: Evaluation of diagnostic test or technology. Participants: ChatGPT and Vetrex AI as publicly available large language model and ML platform, respectively. Methods: ChatGPT was employed to improve the resolution of fundus photography images from the Methods to Evaluate Segmentation and Indexing Techniques in the field of Retinal Ophthalmology (Messidor-2) open-source dataset using the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique by Fiji software. Subsequently, Vertex AI, an automated ML (AutoML) platform, was utilized to develop 2 classification models. The first model served as a binary classifier for detecting the presence of diabetic retinopathy (DR), while the second determined its severity. Finally, ChatGPT was used to provide scripts for R and Python programming languages for data analysis and was also directly employed in analyzing the data in a code-free method. Main Outcome Measures: Evaluating the utility of ChatGPT in generating scripts for preprocessing images using Fiji and analyzing data across Python and R and assessing its potential in analyzing data through a code-free method. Investigating the capabilities of Vertex AI to train image classification models for detection of DR and its severity. Results: Two ML models were trained using 1740 images from the Messidor-2 database. The first model, designed to detect the severity of DR, achieved an area under the precision-recall curve (AUPRC) of 0.81, with a precision rate of 81.81% and recall of 72.83%. The second model, tailored for the detection of the presence of DR, recorded a precision and recall of 84.48% with an AUPRC of 0.90. Conclusions: ChatGPT and Vertex AI have the potential to enable physicians without coding expertise to preprocess images, analyze data, and train ML models. Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
