A Novel Deep Learning Method for Nuclear Cataract Classification Based on Anterior Segment Optical Coherence Tomography Images

Zhang, Xiaoqing; Xiao, Zunjie; Higashita, Risa; Wan, Chao; Yuan, Jing; Fang, Jiansheng; Hu, Yan; Liu, Jiang

doi:10.1109/smc42975.2020.9283218

Cited by 24 publications

(11 citation statements)

References 16 publications

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“…In another example, a novel deep learning method was proposed by Zhang et al [53] to classify nuclear cataracts based on anterior segment OCT images using a convolutional neural network (CNN) model named GraNet. They used a grading block for high-level feature learning that was based on the pointwise convolutional method.…”

Section: Exploitation Of Deep Learning Approaches For Cataract Detectionmentioning

confidence: 99%

Towards a Connected Mobile Cataract Screening System: A Future Approach

et al. 2022

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Advances in computing and AI technology have promoted the development of connected health systems, indirectly influencing approaches to cataract treatment. In addition, thanks to the development of methods for cataract detection and grading using different imaging modalities, ophthalmologists can make diagnoses with significant objectivity. This paper aims to review the development and limitations of published methods for cataract detection and grading using different imaging modalities. Over the years, the proposed methods have shown significant improvement and reasonable effort towards automated cataract detection and grading systems that utilise various imaging modalities, such as optical coherence tomography (OCT), fundus, and slit-lamp images. However, more robust and fully automated cataract detection and grading systems are still needed. In addition, imaging modalities such as fundus, slit-lamps, and OCT images require medical equipment that is expensive and not portable. Therefore, the use of digital images from a smartphone as the future of cataract screening tools could be a practical and helpful solution for ophthalmologists, especially in rural areas with limited healthcare facilities.

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Section: Exploitation Of Deep Learning Approaches For Cataract Detectionmentioning

confidence: 99%

Towards a Connected Mobile Cataract Screening System: A Future Approach

et al. 2022

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“…For AS-OCT images, literature [130,134,9] proposes convolution neural network frameworks for automatic lens region segmentation based on AS-OCT images, which can help ophthalmologists localize and diagnose different types of cataract efficiently. Zhang et al [136] proposed a novel CNN model named GraNet for nuclear cataract classification on AS-OCT images but achieved poor results.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Machine Learning for Cataract Classification and Grading on Ophthalmic Imaging Modalities: A Survey

Zhang¹,

Fang²,

Hu³

et al. 2020

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Cataract is one of the leading causes of reversible visual impairment and blindness globally. Over the years, researchers have achieved significant progress in developing state-of-the-art artificial intelligence techniques for automatic cataract classification and grading, helping clinicians prevent and treat cataract in time. This paper provides a comprehensive survey of recent advances in machine learning for cataract classification and grading based on ophthalmic images. We summarize existing literature from two research directions: conventional machine learning techniques and deep learning techniques. This paper also provides insights into existing works of both merits and limitations. In addition, we discuss several challenges of automatic cataract classification and grading based on machine learning techniques and present possible solutions to these challenges for future research.

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“…Artificial intelligence (AI) is being increasingly used in medicine, and ophthalmology is one of the most active fields for its clinical application ( 15 ). Recent studies have attempted to use AI for cataract grading with various methods, including slit-lamp photography, fundus photography, and optical coherence tomography (OCT) ( 16 – 22 ), and the results suggest that AI-based cataract grading shows acceptable performance with 70–90% accuracy. However, an AI-based approach for evaluation of visual acuity in patients with cataracts is still lacking.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence for the Estimation of Visual Acuity Using Multi-Source Anterior Segment Optical Coherence Tomographic Images in Senile Cataract

et al. 2022

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PurposeTo investigate an artificial intelligence (AI) model performance using multi-source anterior segment optical coherence tomographic (OCT) images in estimating the preoperative best-corrected visual acuity (BCVA) in patients with senile cataract.DesignRetrospective, cross-instrument validation study.SubjectsA total of 2,332 anterior segment images obtained using swept-source OCT, optical biometry for intraocular lens calculation, and a femtosecond laser platform in patients with senile cataract and postoperative BCVA ≥ 0.0 logMAR were included in the training/validation dataset. A total of 1,002 images obtained using optical biometry and another femtosecond laser platform in patients who underwent cataract surgery in 2021 were used for the test dataset.MethodsAI modeling was based on an ensemble model of Inception-v4 and ResNet. The BCVA training/validation dataset was used for model training. The model performance was evaluated using the test dataset. Analysis of absolute error (AE) was performed by comparing the difference between true preoperative BCVA and estimated preoperative BCVA, as ≥0.1 logMAR (AE≥0.1) or <0.1 logMAR (AE <0.1). AE≥0.1 was classified into underestimation and overestimation groups based on the logMAR scale.Outcome MeasurementsMean absolute error (MAE), root mean square error (RMSE), mean percentage error (MPE), and correlation coefficient between true preoperative BCVA and estimated preoperative BCVA.ResultsThe test dataset MAE, RMSE, and MPE were 0.050 ± 0.130 logMAR, 0.140 ± 0.134 logMAR, and 1.3 ± 13.9%, respectively. The correlation coefficient was 0.969 (p < 0.001). The percentage of cases with AE≥0.1 was 8.4%. The incidence of postoperative BCVA > 0.1 was 21.4% in the AE≥0.1 group, of which 88.9% were in the underestimation group. The incidence of vision-impairing disease in the underestimation group was 95.7%. Preoperative corneal astigmatism and lens thickness were higher, and nucleus cataract was more severe (p < 0.001, 0.007, and 0.024, respectively) in AE≥0.1 than that in AE <0.1. The longer the axial length and the more severe the cortical/posterior subcapsular opacity, the better the estimated BCVA than the true BCVA.ConclusionsThe AI model achieved high-level visual acuity estimation in patients with senile cataract. This quantification method encompassed both visual acuity and cataract severity of OCT image, which are the main indications for cataract surgery, showing the potential to objectively evaluate cataract severity.

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A Novel Deep Learning Method for Nuclear Cataract Classification Based on Anterior Segment Optical Coherence Tomography Images

Cited by 24 publications

References 16 publications

Towards a Connected Mobile Cataract Screening System: A Future Approach

Towards a Connected Mobile Cataract Screening System: A Future Approach

Machine Learning for Cataract Classification and Grading on Ophthalmic Imaging Modalities: A Survey

Artificial Intelligence for the Estimation of Visual Acuity Using Multi-Source Anterior Segment Optical Coherence Tomographic Images in Senile Cataract

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