Evidence map›Paper›PMID 37300662›Full record

ArticleInternational journal of computer assisted radiology and surgery2023

Evaluation of continuous curvilinear capsulorhexis based on a neural-network.

Chuang Lin, Yang Yang, Handa Gao, Yu Zheng, Ke Ma, Chenhan Guang

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Article in International journal of computer assisted radiology and surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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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.

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Chuang LinSchool of Mechanical Engineering and Automation, Beihang University, Beijing, 100083, China.
Yang YangSchool of Mechanical Engineering and Automation, Beihang University, Beijing, 100083, China.
Handa GaoSchool of Mechanical Engineering and Automation, Beihang University, Beijing, 100083, China.
Yu ZhengCollege of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China.
Ke MaEye Center of Beijing Tongren Hospital, Capital Medical University, Beijing, 100005, China.
Chenhan GuangSchool of Mechanical Engineering and Automation, Beihang University, Beijing, 100083, China. guangchenhan@buaa.edu.cn.

Funding

Key Technologies Research and Development Program 2017YFB1302700National Natural Science Foundation of China 51875011
6 · The paper itself

Abstract

purposeContinuous curvilinear capsulorhexis (CCC), as a prerequisite for successful cataract surgery, is one of the most important and difficult steps in phacoemulsification. In clinical practice, the size and circularity of the capsular tear and eccentricity with the lens are often employed as indicators to evaluate the effect of CCC.

methodsWe present a neural network-based model to improve the efficiency and accuracy of evaluation for capsulorhexis results. The capsulorhexis results evaluation model consists of the detection network based on U-Net and the nonlinear fitter built from fully connected layers. The detection network is responsible for detecting the positions of the round capsular tear and lens margin, and the nonlinear fitter is utilized to fit the outputs of the detection network and to compute the capsulorhexis results evaluation indicators. We evaluate the proposed model on an artificial eye phantom and compare its performance with the medical evaluation method.

resultsThe experimental results show that the average detection error of the proposed evaluation model is within 0.04 mm. Compared with the medical method (the average detection error is 0.28 mm), the detection accuracy of the proposed evaluation model is more accurate and stable.

conclusionWe propose a neural network-based capsulorhexis results evaluation model to improve the accuracy of evaluation for capsulorhexis results. The results of the evaluation experiments show that the proposed results evaluation model evaluates of the effect of capsulorhexis better than the medical evaluation method.

Indexed as

Cataract ExtractionLens Capsule, CrystallineCapsulorhexisEye, ArtificialHumansCapsulorhexis results evaluationContinuous curvilinear capsulorhexisEdge segmentationNonlinear fitting

Identifiers

PMID37300662

What OpenQuestion holds

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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.