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An artificial intelligence approach to classify pathogenic fungal genera of fungal keratitis using corneal confocal microscopy images

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Abstract

Purpose

Fungal keratitis is a common cause of blindness worldwide. Timely identification of the causative fungal genera is essential for clinical management. In vivo confocal microscopy (IVCM) provides useful information on pathogenic genera. This study attempted to apply deep learning (DL) to establish an automated method to identify pathogenic fungal genera using IVCM images.

Methods

Deep learning networks were trained, validated, and tested using a data set of 3364 IVCM images that collected from 100 eyes of 100 patients with culture-proven filamentous fungal keratitis. Two transfer learning approaches were investigated: one was a combined framework that extracted features by a DL network and adopted decision tree (DT) as a classifier; another was a complete supervised DL model which used DL-based fully connected layers to implement the classification.

Results

The DL classifier model revealed better performance compared with the DT classifier model in an independent testing set. The DL classifier model showed an area under the receiver operating characteristic curves (AUC) of 0.887 with an accuracy of 0.817, sensitivity of 0.791, specificity of 0.831, G-mean of 0.811, and F1 score of 0.749 in identifying Fusarium, and achieved an AUC of 0.827 with an accuracy of 0.757, sensitivity of 0.756, specificity of 0.759, G-mean of 0.757, and F1 score of 0.716 in identifying Aspergillus.

Conclusion

The DL model can classify Fusarium and Aspergillus by learning effective features in IVCM images automatically. The automated IVCM image analysis suggests a noninvasive identification of Fusarium and Aspergillus with clear potential application in early diagnosis and management of fungal keratitis.

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Abbreviations

IVCM:

In vivo confocal microscopy

AI:

Artificial intelligence

DL:

Deep learning

Grad-CAM:

Gradient-weighted class activation mapping

DT:

Decision tree

PCA:

Principal component analysis

LightGBM:

Light gradient boosting machine

G-mean:

Geometric mean

ROC:

Receiver operating characteristic

AUC:

Area under the ROC curve

CI:

Confidence interval

DET:

Detection error tradeoff

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Funding

This study was supported by Guangxi Science and Technology Base and Talent Special Fund (Grant numbers [GuikeAD22035011]), Guangxi Clinical Ophthalmic Research Center (Grant numbers [GuikeAD19245193]), Guangxi Promotion of Appropriate Health Technologies Project (Grant numbers [S2019084]), and Guangxi Zhuang Autonomous Region Health Committee's Self-financing Project (Grant numbers [Z20201322]).

Author information

Author notes

  1. Ningning Tang, Guangyi Huang, Daizai Lei and Li Jiang have contributed equally to this work.

Authors and Affiliations

  1. Department of Ophthalmology, the People′s Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, People’s Republic of China

    Ningning Tang, Guangyi Huang, Daizai Lei, Li Jiang, Qi Chen, Wenjing He, Fen Tang, Yiyi Hong, Jian Lv, Yuanjun Qin, Yunru Lin, Qianqian Lan, Yikun Qin, Min Li, Fan Xu & Peng Lu

  2. Guangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin University of Electronic Technology, Guilin, 541004, People’s Republic of China

    Rushi Lan & Xipeng Pan

  3. Department of Radiology, Guangdong Academy of Medical Sciences, Guangdong Provincial People’s Hospital, Guangzhou, 510080, People’s Republic of China

    Xipeng Pan

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  1. Ningning Tang
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Contributions

The first draft of the manuscript was written by N.T., and all authors commented on previous versions of the manuscript. G.H., D.L., and L.J. performed the analyses. Q.C., W.H., F.T., Y.H., J.L., Y.Q., Y.L., and Q.L. contributed to data collection and measurements. Y.Q., R.L., and X.P. contributed to algorithm optimization. M.L. and P.L. were involved quality management. F.X. conceived the research, provided overall supervision, and undertook the responsibility of submitting the manuscript for publication. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Fan Xu or Peng Lu.

Ethics declarations

Competing interests

The authors have no relevant financial or non-financial interests to disclose.

Ethics approval

This study was approved by the ethics committee of the People’s Hospital of Guangxi Zhuang Autonomous Region, China. The approval number is KY-SY-2020–1.

Consent to participate

Informed consent was granted exemption by ethics committee of the People’s Hospital of Guangxi Zhuang Autonomous Region. All IVCM images applied in the study were completely anonymized. The submission does not include information that may identify the participant.

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Tang, N., Huang, G., Lei, D. et al. An artificial intelligence approach to classify pathogenic fungal genera of fungal keratitis using corneal confocal microscopy images. Int Ophthalmol 43, 2203–2214 (2023). https://doi.org/10.1007/s10792-022-02616-8

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