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A novel artificial intelligence model for diagnosing Acanthamoeba keratitis through confocal microscopy.

Omar Shareef¹, Mohammad Soleimani², Elmer Tu³

¹School of Engineering and Applied Sciences, Harvard College, Cambridge, MA, 02138, USA; Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, 02114, USA.

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Summary

An artificial intelligence (AI) model was developed to diagnose Acanthamoeba keratitis (AK) using Heidelberg Retinal Tomograph 3 (HRT 3) in vivo confocal microscopy (IVCM) images. The AI model achieved 76% accuracy, demonstrating promise for early AK diagnosis.

Keywords:

Acanthamoeba keratitis Confocal microscopy Convolutional neural network Diagnosis Machine-learning

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Acanthamoeba keratitis (AK) is a serious eye infection requiring prompt diagnosis.
In vivo confocal microscopy (IVCM) is a valuable tool for diagnosing AK.
Standardized AI-driven diagnostic tools are needed to improve AK detection.

Purpose of the Study:

To develop and evaluate an artificial intelligence (AI) model for diagnosing Acanthamoeba keratitis (AK).
The AI model utilizes images from the Heidelberg Retinal Tomograph 3 (HRT 3) in vivo confocal microscopy (IVCM).

Main Methods:

A retrospective cohort study used HRT 3 IVCM images from culture-confirmed AK patients.
Cornea specialists independently labeled images as AK or nonspecific finding (NSF).
A convolutional neural network (CNN) was developed using Python and TensorFlow for image classification.

Main Results:

The study analyzed 3312 IVCM images from 17 AK patients.
After expert consensus, 2782 images were used for model training and validation.
The AI model achieved 76% accuracy, sensitivity, and specificity, with 78% precision.

Conclusions:

An AI model was successfully developed for diagnosing AK using HRT-based IVCM images.
The model demonstrated good diagnostic performance, indicating potential for clinical use.
This AI tool shows promise in enhancing early detection and management of Acanthamoeba keratitis.