The ocular surface · 2024
A novel artificial intelligence model for diagnosing Acanthamoeba keratitis through confocal microscopy.
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Shareef Omar, Soleimani Mohammad, Tu Elmer, Jacobs Deborah S, Ciolino Joseph B, Rahdar Amir, Cheraqpour Kasra, Ashraf Mohammadali, Habib Nabiha B, Greenfield Jason, Yousefi Siamak, Djalilian Ali R, Saeed Hajirah N
Аннотация
PURPOSE: To develop an artificial intelligence (AI) model to diagnose Acanthamoeba keratitis (AK) based on in vivo confocal microscopy (IVCM) images extracted from the Heidelberg Retinal Tomograph 3 (HRT 3).
METHODS: This retrospective cohort study utilized HRT 3 IVCM images from patients who had received a culture-confirmed diagnosis of AK between 2013 and 2021 at Massachusetts Eye and Ear. Two cornea specialists independently labeled the images as AK or nonspecific finding (NSF) in a blind manner. Deep learning tasks were then conducted through Python and TensorFlow. Distinguishing between AK and NSF was designed as the task and completed through a devised convolutional neural network.
RESULTS: A dataset of 3312 confocal images from 17 patients with a culture-confirmed diagnosis of AK was used in this study. The inter-rater agreement for identifying the presence or absence of AK in IVCM images was 84 %, corresponding to a total of 2782 images on which both observers agreed and which were included in the model. 1242 and 1265 images of AK and NSF, respectively, were utilized in the training and validation sets, and 173 and 102 images of AK and NSF, respectively, were utilized in the evaluation set. Our model had an accuracy, sensitivity, and specificity of 76 % each, and a precision of 78 %.
CONCLUSIONS: We developed an HRT-based IVCM AI model for AK diagnosis utilizing culture-confirmed cases of AK. We achieved good accuracy in diagnosing AK and our model holds significant promise in the clinical application of AI in improving early AK diagnosis.
METHODS: This retrospective cohort study utilized HRT 3 IVCM images from patients who had received a culture-confirmed diagnosis of AK between 2013 and 2021 at Massachusetts Eye and Ear. Two cornea specialists independently labeled the images as AK or nonspecific finding (NSF) in a blind manner. Deep learning tasks were then conducted through Python and TensorFlow. Distinguishing between AK and NSF was designed as the task and completed through a devised convolutional neural network.
RESULTS: A dataset of 3312 confocal images from 17 patients with a culture-confirmed diagnosis of AK was used in this study. The inter-rater agreement for identifying the presence or absence of AK in IVCM images was 84 %, corresponding to a total of 2782 images on which both observers agreed and which were included in the model. 1242 and 1265 images of AK and NSF, respectively, were utilized in the training and validation sets, and 173 and 102 images of AK and NSF, respectively, were utilized in the evaluation set. Our model had an accuracy, sensitivity, and specificity of 76 % each, and a precision of 78 %.
CONCLUSIONS: We developed an HRT-based IVCM AI model for AK diagnosis utilizing culture-confirmed cases of AK. We achieved good accuracy in diagnosing AK and our model holds significant promise in the clinical application of AI in improving early AK diagnosis.