Evidence map›Paper›PMID 31448182›Full record

ArticleTranslational vision science & technology2019

Automatic Classification of Anterior Chamber Angle Using Ultrasound Biomicroscopy and Deep Learning.

Guohua Shi, Zhenying Jiang, Guohua Deng, Guangxing Liu, Yuan Zong, Chunhui Jiang, Qian Chen, Yi Lu, Xinhuai Sun

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Translational vision science & technology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
2.6field-weighted citation impact, top 11% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Risk factors of angle opening after lens extraction in patients with shallow peripheral anterior chamber.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 5 institutions in 1 country.

Guohua ShiSuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu Province, China.
Zhenying JiangDepartment of Ophthalmology and Visual Science, Eye, Ear, Nose and Throat Hospital, Shanghai Medical College of Fudan University, Shanghai, China.
Guohua DengDepartment of Ophthalmology, the Third People's Hospital of Changzhou, Changzhou, Jiangsu Province, China.
Guangxing LiuSuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu Province, China.
Yuan ZongDepartment of Ophthalmology and Visual Science, Eye, Ear, Nose and Throat Hospital, Shanghai Medical College of Fudan University, Shanghai, China.
Chunhui JiangDepartment of Ophthalmology and Visual Science, Eye, Ear, Nose and Throat Hospital, Shanghai Medical College of Fudan University, Shanghai, China.
Qian ChenDepartment of Ophthalmology and Visual Science, Eye, Ear, Nose and Throat Hospital, Shanghai Medical College of Fudan University, Shanghai, China.
Yi LuDepartment of Ophthalmology and Visual Science, Eye, Ear, Nose and Throat Hospital, Shanghai Medical College of Fudan University, Shanghai, China.
Xinhuai SunDepartment of Ophthalmology and Visual Science, Eye, Ear, Nose and Throat Hospital, Shanghai Medical College of Fudan University, Shanghai, China.
Fudan University · CNChinese Academy of Sciences · CNChangzhou Third People's Hospital · CNChinese Academy of Medical Sciences & Peking Union Medical College · CNShanghai Medical College of Fudan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo develop a software package for automated classification of anterior chamber angle of the eye by using ultrasound biomicroscopy.

methodsUltrasound biomicroscopy images were collected, and the trabecular-iris angle was manually measured and classified into three categories: open angle, narrow angle, and angle closure. Inception v3 was used as the classifying convolutional neural network and the algorithm was trained.

resultsWith a recall rate of 97% in the test set, the neural network's classification accuracy can reach 97.2% and the overall area under the curve was 0.988. The sensitivity and specificity were 98.04% and 99.09% for the open angle, 96.30% and 98.13% for the narrow angle, and 98.21% and 99.05% for the angle closure categories, respectively.

conclusionsPreliminary results show that an automated classification of the anterior chamber angle achieved satisfying sensitivity and specificity and could be helpful in clinical practice. TRANSLATIONAL RELEVANCE: The present work suggests that the algorithm described here could be useful in the categorizing of anterior chamber angle and screening for subjects who are at high risk of angle closure.

Indexed as

anterior chamber angleautomatic classificationultrasound biomicroscopy

Identifiers

PMID31448182
PMCPMC6703191
OpenAlexW2969677637

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.