探索ICL植入保险库安全性的机器学习模型:回归模型和分类模型的比较分析。

IF 2.6 3区 医学 Q2 OPHTHALMOLOGY
Qing Zhang, Qi Li, Zhilong Yu, Ruibo Yang, Emmanuel Eric Pazo, Yue Huang, Hui Liu, Chen Zhang, Salissou Moutari, Shaozhen Zhao
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引用次数: 0

摘要

导论:准确预测人工晶状体(ICL) V4c植入术后拱顶高度对于减少并发症和获得最佳手术效果至关重要。本研究旨在评估机器学习模型在预测术后拱顶高度方面的性能,重点关注回归和分类方法。方法:本回顾性研究分析了生物统计学和人口统计学数据,包括前房深度、白-白距离、ICL大小等变量。使用梯度增强、随机森林和CatBoost算法建立了回归和分类模型。回归模型预测跳高为连续变量,分类模型将跳高分为二值任务和多类任务。使用回归模型的平均绝对误差(MAE)和均方根误差(RMSE)等指标来评估模型的性能,而分类模型则使用准确率、f1评分和曲线下面积(AUC)。结果:回归模型表现出中等的预测性能,随机森林表现最佳(MAE: 134.0µm, RMSE: 171.3µm, Pearson相关系数:0.45)。另一方面,分类模型比回归方法表现出更大的临床适用性。对于二元分类任务(vault 750µm vs.≤750µm),随机森林成为最有效的分类器,准确率为86±9%,AUC为0.88。在多类别分类中,模型在预测中间跳马高度(250µm≤跳马≤750µm)方面表现出优异的性能。在这一类别中,随机森林的准确率最高,达到94.6%。然而,所有模型都面临着准确分类极限跳马类别的挑战。结论:分类模型,特别是梯度增强模型和随机森林模型,在预测具有临床意义的拱顶类别方面显示出强大的潜力,可以实现个性化手术计划和改进风险管理。虽然回归模型提供了适度的见解,但它们的局限性表明分类方法更适合临床应用。未来的研究应侧重于提高极端拱顶预测模型的准确性,并整合集成深度学习等先进技术,以进一步完善结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring Machine Learning Models for Vault Safety in ICL Implantation: A Comparative Analysis of Regression and Classification Models.

Introduction: Accurate prediction of postoperative vault height following implantable collamer lens (ICL) V4c implantation is critical for minimizing complications and achieving optimal surgical outcomes. This study aims to evaluate the performance of machine learning models in predicting postoperative vault height, focusing on both regression and classification approaches.

Methods: This retrospective study analyzed biometric and demographic data, including anterior chamber depth, white-to-white distance, and ICL size, among other variables. Regression and classification models were developed using gradient boosting, random forest, and CatBoost algorithms. Regression models predicted vault height as a continuous variable, while classification models categorized vault heights into binary and multi-class tasks. Model performance was evaluated using metrics including the mean absolute error (MAE) and root mean squared error (RMSE) for regression models, while accuracy, F1-score, and area under the curve (AUC) were used for classification models.

Results: Regression models demonstrated moderate predictive performance, with random forest delivering the best performance (MAE: 134.0 µm, RMSE: 171.3 µm, Pearson's correlation coefficient: 0.45). On the other hand, classification models exhibited greater clinical applicability than regression approaches. For the binary classification task (vault < 250 µm vs. ≥ 250 µm), gradient boosting achieved the highest overall performance, with accuracy of 89 ± 12% and an AUC of 0.89. In the task of predicting vault > 750 µm vs. ≤ 750 µm, random forest emerged as the most effective classifier, achieving accuracy of 86 ± 9% and an AUC of 0.88. In multi-class classification, models demonstrated superior performance in predicting intermediate vault heights (250 µm ≤ vault ≤ 750 µm). Random forest achieved the highest accuracy of 94.6% in this category. However, all models faced challenges in accurately classifying extreme vault categories.

Conclusions: Classification models, particularly gradient boosting and random forest, demonstrated strong potential for predicting clinically significant vault categories, enabling personalized surgical planning and improved risk management. While regression models offered moderate insights, their limitations suggest that classification approaches are better suited for clinical applications. Future research should focus on enhancing model accuracy for extreme vault prediction and integrating advanced techniques, such as ensemble deep learning, to further refine outcomes.

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来源期刊
Ophthalmology and Therapy
Ophthalmology and Therapy OPHTHALMOLOGY-
CiteScore
4.20
自引率
3.00%
发文量
157
审稿时长
6 weeks
期刊介绍: Aims and Scope Ophthalmology and Therapy is an international, open access, peer-reviewed (single-blind), and rapid publication journal. The scope of the journal is broad and will consider all scientifically sound research from preclinical, clinical (all phases), observational, real-world, and health outcomes research around the use of ophthalmological therapies, devices, and surgical techniques. The journal is of interest to a broad audience of pharmaceutical and healthcare professionals and publishes original research, reviews, case reports/series, trial protocols and short communications such as commentaries and editorials. Ophthalmology and Therapy will consider all scientifically sound research be it positive, confirmatory or negative data. Submissions are welcomed whether they relate to an international and/or a country-specific audience, something that is crucially important when researchers are trying to target more specific patient populations. This inclusive approach allows the journal to assist in the dissemination of quality research, which may be considered of insufficient interest by other journals. Rapid Publication The journal’s publication timelines aim for a rapid peer review of 2 weeks. If an article is accepted it will be published 3–4 weeks from acceptance. The rapid timelines are achieved through the combination of a dedicated in-house editorial team, who manage article workflow, and an extensive Editorial and Advisory Board who assist with peer review. This allows the journal to support the rapid dissemination of research, whilst still providing robust peer review. Combined with the journal’s open access model this allows for the rapid, efficient communication of the latest research and reviews, fostering the advancement of ophthalmic therapies. Open Access All articles published by Ophthalmology and Therapy are open access. Personal Service The journal’s dedicated in-house editorial team offer a personal “concierge service” meaning authors will always have an editorial contact able to update them on the status of their manuscript. The editorial team check all manuscripts to ensure that articles conform to the most recent COPE, GPP and ICMJE publishing guidelines. This supports the publication of ethically sound and transparent research. Digital Features and Plain Language Summaries Ophthalmology and Therapy offers a range of additional features designed to increase the visibility, readership and educational value of the journal’s content. Each article is accompanied by key summary points, giving a time-efficient overview of the content to a wide readership. Articles may be accompanied by plain language summaries to assist readers who have some knowledge of, but not in-depth expertise in, the area to understand the scientific content and overall implications of the article. The journal also provides the option to include various types of digital features including animated abstracts, video abstracts, slide decks, audio slides, instructional videos, infographics, podcasts and animations. All additional features are peer reviewed to the same high standard as the article itself. If you consider that your paper would benefit from the inclusion of a digital feature, please let us know. Our editorial team are able to create high-quality slide decks and infographics in-house, and video abstracts through our partner Research Square, and would be happy to assist in any way we can. For further information about digital features, please contact the journal editor (see ‘Contact the Journal’ for email address), and see the ‘Guidelines for digital features and plain language summaries’ document under ‘Submission guidelines’. For examples of digital features please visit our showcase page https://springerhealthcare.com/expertise/publishing-digital-features/ Publication Fees Upon acceptance of an article, authors will be required to pay the mandatory Rapid Service Fee of €5250/$6000/£4300. The journal will consider fee discounts and waivers for developing countries and this is decided on a case by case basis. Peer Review Process Upon submission, manuscripts are assessed by the editorial team to ensure they fit within the aims and scope of the journal and are also checked for plagiarism. All suitable submissions are then subject to a comprehensive single-blind peer review. Reviewers are selected based on their relevant expertise and publication history in the subject area. The journal has an extensive pool of editorial and advisory board members who have been selected to assist with peer review based on the afore-mentioned criteria. At least two extensive reviews are required to make the editorial decision, with the exception of some article types such as Commentaries, Editorials, and Letters which are generally reviewed by one member of the Editorial Board. Where reviewer recommendations are conflicted, the editorial board will be contacted for further advice and a presiding decision. Manuscripts are then either accepted, rejected or authors are required to make major or minor revisions (both reviewer comments and editorial comments may need to be addressed). Once a revised manuscript is re-submitted, it is assessed along with the responses to reviewer comments and if it has been adequately revised it will be accepted for publication. Accepted manuscripts are then copyedited and typeset by the production team before online publication. Appeals against decisions following peer review are considered on a case-by-case basis and should be sent to the journal editor. Preprints We encourage posting of preprints of primary research manuscripts on preprint servers, authors’ or institutional websites, and open communications between researchers whether on community preprint servers or preprint commenting platforms. Posting of preprints is not considered prior publication and will not jeopardize consideration in our journals. Authors should disclose details of preprint posting during the submission process or at any other point during consideration in one of our journals. Once the manuscript is published, it is the author’s responsibility to ensure that the preprint record is updated with a publication reference, including the DOI and a URL link to the published version of the article on the journal website. Please follow the link for further information on preprint sharing: https://www.springer.com/gp/authors-editors/journal-author/journal-author-helpdesk/submission/1302#c16721550 Copyright Ophthalmology and Therapy''s content is published open access under the Creative Commons Attribution-Noncommercial License, which allows users to read, copy, distribute, and make derivative works for non-commercial purposes from the material, as long as the author of the original work is cited. The author assigns the exclusive right to any commercial use of the article to Springer. For more information about the Creative Commons Attribution-Noncommercial License, click here: http://creativecommons.org/licenses/by-nc/4.0. Contact For more information about the journal, including pre-submission enquiries, please contact christopher.vautrinot@springer.com.
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