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Applying deep learning to recognize the properties of vitreous opacity in ophthalmic ultrasound images

Eye volume 38, pages 380–385 (2024)Cite this article

Subjects

Abstract

Background

To explore the feasibility of artificial intelligence technology based on deep learning to automatically recognize the properties of vitreous opacities in ophthalmic ultrasound images.

Methods

A total of 2000 greyscale Doppler ultrasound images containing non-pathological eye and three typical vitreous opacities confirmed as physiological vitreous opacity (VO), asteroid hyalosis (AH), and vitreous haemorrhage (VH) were selected and labelled for each lesion type. Five residual networks (ResNet) and two GoogLeNet models were trained to recognize vitreous lesions. Seventy-five percent of the images were randomly selected as the training set, and the remaining 25% were selected as the test set. The accuracy and parameters were recorded and compared among these seven different deep learning (DL) models. The precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC) values for recognizing vitreous lesions were calculated for the most accurate DL model.

Results

These seven DL models had significant differences in terms of their accuracy and parameters. GoogLeNet Inception V1 achieved the highest accuracy (95.5%) and minor parameters (10315580) in vitreous lesion recognition. GoogLeNet Inception V1 achieved precision values of 0.94, 0.94, 0.96, and 0.96, recall values of 0.94, 0.93, 0.97 and 0.98, and F1 scores of 0.94, 0.93, 0.96 and 0.97 for normal, VO, AH, and VH recognition, respectively. The AUC values for these four vitreous lesion types were 0.99, 1.0, 0.99, and 0.99, respectively.

Conclusions

GoogLeNet Inception V1 has shown promising results in ophthalmic ultrasound image recognition. With increasing ultrasound image data, a wide variety of confidential information on eye diseases can be detected automatically by artificial intelligence technology based on deep learning.

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Fig. 1: Greyscale ultrasound images of normal and vitreous opacities.
Fig. 2
Fig. 3
Fig. 4: GoogLeNet inception V1 has shown promising results in ophthalmic ultrasound image recognition.

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Data availability

Original data are in the possession of the corresponding author and are available on request.

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Funding

This study was funded by Scientific Research Funding for Education Department of Liaoning Province (Grant No. ZD2020002).

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Authors and Affiliations

  1. Department of Ophthalmology, The Fourth Affiliated Hospital of China Medical University, Eye Hospital of China Medical University, The Key Laboratory of Lens in Liaoning Province, Shenyang, China

    Li Feng, Hui Qiu & Mingyu Shi

  2. Shenyang Ligong University, Shenyang, China

    Yanzhu Zhang

  3. Hebei Eye Hospital, Xingtai, China

    Wei Wei

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Contributions

LF and MS were involved in the conception and conduct of the study, LF, HQ and MS were drafted this manuscript, and YZ, WW and MS were involved in review the manuscript. All authors read and approved the final version.

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Correspondence to Mingyu Shi.

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Feng, L., Zhang, Y., Wei, W. et al. Applying deep learning to recognize the properties of vitreous opacity in ophthalmic ultrasound images. Eye 38, 380–385 (2024). https://doi.org/10.1038/s41433-023-02705-7

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