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Related Experiment Video

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Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

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A knowledge-enhanced transform-based multimodal classifier for microbial keratitis identification.

Jianfeng Wu¹, Zhouhang Yuan², Zhengqing Fang²

¹School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, 31002, China.

Scientific Reports

|June 2, 2023

Summary

This study introduces a novel deep learning model for diagnosing bacterial keratitis (BK) and fungal keratitis (FK) using slit-lamp images and treatment texts. The model achieved high accuracy, outperforming benchmarks for improved infectious keratitis diagnosis.

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Microbial keratitis is an urgent eye condition requiring prompt diagnosis to prevent vision loss.
Distinguishing between bacterial and fungal keratitis using only images is challenging due to similar visual characteristics.

Purpose of the Study:

To develop a deep learning model for differentiating bacterial keratitis (BK) and fungal keratitis (FK).
To leverage multimodal data, including slit-lamp images and treatment texts, for enhanced diagnostic accuracy.

Main Methods:

A knowledge-enhanced transform-based multimodal classifier was developed.
The model utilized 704 slit-lamp images from 352 patients, divided into training, validation, and testing sets.
Performance was evaluated using accuracy, specificity, sensitivity, and AUC.

Main Results:

The model achieved a top accuracy of 93% in the testing set.
Sensitivity was 0.97, specificity was 0.92, and AUC was 0.94, surpassing the benchmark accuracy of 0.86.
Diagnostic accuracies for BK ranged from 81-92%, and for FK from 89-97%.

Conclusions:

This study presents the first model integrating disease changes and medication interventions for infectious keratitis diagnosis.
The developed multimodal classifier demonstrates state-of-the-art performance, outperforming existing benchmark models.