Deep learning-based anterior segment identification and parameter assessment of primary angle closure disease in ultrasound biomicroscopy images
Purpose To develop an artificial intelligence algorithm to automatically identify the anterior segment structures and assess multiple parameters of primary angle closure disease (PACD) in ultrasound biomicroscopy (UBM) images.Design Development and validation of an artificial intelligence algorithm...
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2025-01-01
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| Series: | BMJ Open Ophthalmology |
| Online Access: | https://bmjophth.bmj.com/content/10/1/e001600.full |
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| author | Xingzhi Sun Guotong Xie Kun Lv Xiaoyue Zhang Bin Lv Yuan Ni Yao Ma Fangting Li Kangyi Yang Jiayin Qin Huijuan Wu |
| author_facet | Xingzhi Sun Guotong Xie Kun Lv Xiaoyue Zhang Bin Lv Yuan Ni Yao Ma Fangting Li Kangyi Yang Jiayin Qin Huijuan Wu |
| author_sort | Xingzhi Sun |
| collection | DOAJ |
| description | Purpose To develop an artificial intelligence algorithm to automatically identify the anterior segment structures and assess multiple parameters of primary angle closure disease (PACD) in ultrasound biomicroscopy (UBM) images.Design Development and validation of an artificial intelligence algorithm for UBM images.Methods:2339 UBM images from 592 subjects were collected for algorithm development. A multitissue segmentation model based on deep learning was developed for automatic identification of anterior segments and localisation of scleral spur. Then, measurement of the typical angle parameters was performed from the predicted results, including angle-opening distance at 500 µm (AOD 500), trabecular–ciliary angle (TCA) and iris area. We then collected 222 UBM images from 45 subjects in two centres for model validation.Results The multitissue identification model established in this study reached mean Intersection over Union (IoU) of 0.98, 0.98 and 0.98 on cornea segmentation, iris segmentation and ciliary body segmentation and a mean error distance of 1.07 pixels on scleral spur localisation. Our model got a mean IoU of 0.98, 0.98 and 0.99 on cornea segmentation, iris segmentation and ciliary body segmentation and a mean error distance of 0.49 pixels on scleral spur localisation in open-angle images and received 0.98, 0.98, 0.978 and 1.42 pixels respectively in angle-closure images. The mean differences between automatic and manual measurement of the angle parameters were 3.07 μm of AOD, 3.34 degrees of TCA and 0.05 mm2 of iris area.Conclusions The automatic method of multitissue identification for PACD eyes developed was feasible, and the automatic measurement of angle parameters was reliable. |
| format | Article |
| id | doaj-art-67f3e8f3cf7e43579ff66fc116dad53d |
| institution | DOAJ |
| issn | 2397-3269 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open Ophthalmology |
| spelling | doaj-art-67f3e8f3cf7e43579ff66fc116dad53d2025-08-20T02:41:14ZengBMJ Publishing GroupBMJ Open Ophthalmology2397-32692025-01-0110110.1136/bmjophth-2023-001600Deep learning-based anterior segment identification and parameter assessment of primary angle closure disease in ultrasound biomicroscopy imagesXingzhi Sun0Guotong Xie1Kun Lv2Xiaoyue Zhang3Bin Lv4Yuan Ni5Yao Ma6Fangting Li7Kangyi Yang8Jiayin Qin9Huijuan Wu10Ping An Technology, Shenzhen, ChinaPing An Technology, Shenzhen, ChinaDepartment of Ophthalmology, Peking University People`s Hospital, Beijing, ChinaPing An Technology, Shenzhen, ChinaPing An Insurance Group Company of China Ltd, Shenzhen, ChinaPing An Insurance Group Company of China Ltd, Shenzhen, ChinaBeijing Key Laboratory of Ocular Disease and Optometry Science, Peking University People`s Hospital, Beijing, ChinaDepartment of Ophthalmology, Peking University People`s Hospital, Beijing, ChinaDepartment of Ophthalmology, Peking University People`s Hospital, Beijing, ChinaDepartment of Ophthalmology, Peking University International Hospital, Beijing, ChinaDepartment of Ophthalmology, Peking University People`s Hospital, Beijing, ChinaPurpose To develop an artificial intelligence algorithm to automatically identify the anterior segment structures and assess multiple parameters of primary angle closure disease (PACD) in ultrasound biomicroscopy (UBM) images.Design Development and validation of an artificial intelligence algorithm for UBM images.Methods:2339 UBM images from 592 subjects were collected for algorithm development. A multitissue segmentation model based on deep learning was developed for automatic identification of anterior segments and localisation of scleral spur. Then, measurement of the typical angle parameters was performed from the predicted results, including angle-opening distance at 500 µm (AOD 500), trabecular–ciliary angle (TCA) and iris area. We then collected 222 UBM images from 45 subjects in two centres for model validation.Results The multitissue identification model established in this study reached mean Intersection over Union (IoU) of 0.98, 0.98 and 0.98 on cornea segmentation, iris segmentation and ciliary body segmentation and a mean error distance of 1.07 pixels on scleral spur localisation. Our model got a mean IoU of 0.98, 0.98 and 0.99 on cornea segmentation, iris segmentation and ciliary body segmentation and a mean error distance of 0.49 pixels on scleral spur localisation in open-angle images and received 0.98, 0.98, 0.978 and 1.42 pixels respectively in angle-closure images. The mean differences between automatic and manual measurement of the angle parameters were 3.07 μm of AOD, 3.34 degrees of TCA and 0.05 mm2 of iris area.Conclusions The automatic method of multitissue identification for PACD eyes developed was feasible, and the automatic measurement of angle parameters was reliable.https://bmjophth.bmj.com/content/10/1/e001600.full |
| spellingShingle | Xingzhi Sun Guotong Xie Kun Lv Xiaoyue Zhang Bin Lv Yuan Ni Yao Ma Fangting Li Kangyi Yang Jiayin Qin Huijuan Wu Deep learning-based anterior segment identification and parameter assessment of primary angle closure disease in ultrasound biomicroscopy images BMJ Open Ophthalmology |
| title | Deep learning-based anterior segment identification and parameter assessment of primary angle closure disease in ultrasound biomicroscopy images |
| title_full | Deep learning-based anterior segment identification and parameter assessment of primary angle closure disease in ultrasound biomicroscopy images |
| title_fullStr | Deep learning-based anterior segment identification and parameter assessment of primary angle closure disease in ultrasound biomicroscopy images |
| title_full_unstemmed | Deep learning-based anterior segment identification and parameter assessment of primary angle closure disease in ultrasound biomicroscopy images |
| title_short | Deep learning-based anterior segment identification and parameter assessment of primary angle closure disease in ultrasound biomicroscopy images |
| title_sort | deep learning based anterior segment identification and parameter assessment of primary angle closure disease in ultrasound biomicroscopy images |
| url | https://bmjophth.bmj.com/content/10/1/e001600.full |
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