Graded diagnosis of nuclear cataracts utilizing precision localization and deep learning algorithms

Dexun Jiang; Jianglong Hao; Yuanlong Chen; Xingling Li; Jie Liu

doi:10.1117/12.3026437

9 February 2024 Graded diagnosis of nuclear cataracts utilizing precision localization and deep learning algorithms

Dexun Jiang, Jianglong Hao, Yuanlong Chen, Xingling Li, Jie Liu

Proceedings Volume 13073, Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023); 130730V (2024) https://doi.org/10.1117/12.3026437
Event: Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023), 2023, Changsha, China

Abstract

Cataract, the leading cause of global blindness, represents a focal concern within the field of blindness prevention. Its diagnosis primarily relies on the observation of lens opacification under slit-lamp examination, coupled with best-corrected visual acuity assessment. With the rapid evolution of artificial intelligence, the ophthalmic domain has increasingly incorporated AI technologies; however, research in the realm of cataracts remains relatively limited. This study employs computer vision segmentation techniques to obtain precise images of cataractous lens nuclei and utilizes deep learning methodologies for training and validation, yielding commendable results in the realm of graded diagnostic accuracy. The application of computer vision for meticulous cataractous lens nuclear region-of-interest imaging, coupled with adept deep learning methodologies, has demonstrated notable efficacy in achieving superior diagnostic outcomes.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Dexun Jiang, Jianglong Hao, Yuanlong Chen, Xingling Li, and Jie Liu "Graded diagnosis of nuclear cataracts utilizing precision localization and deep learning algorithms", Proc. SPIE 13073, Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023), 130730V (9 February 2024); https://doi.org/10.1117/12.3026437

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