Muhsin Zahra J, Qahwaji Rami, Ghafir Ibrahim, AlShawabkeh Mo'ath, Al Bdour Muawyah, AlRyalat Saif, Al-Taee Majid
Faculty of Engineering and Digital Technologies, University of Bradford, Bradford, BD7 1DP, UK.
Department of Ophthalmology, The Hashemite University, Zarqa, Jordan.
Int Ophthalmol. 2025 Mar 30;45(1):128. doi: 10.1007/s10792-025-03496-4.
To review studies reporting the role of Machine Learning (ML) techniques in the diagnosis of keratoconus (KC) over the past decade, shedding light on recent developments while also highlighting the existing gaps between academic research and practical implementation in clinical settings.
The review process begins with a systematic search of primary digital libraries using relevant keywords. A rigorous set of inclusion and exclusion criteria is then applied, resulting in the identification of 62 articles for analysis. Key research questions are formulated to address advancements in ML for KC diagnosis, corneal imaging modalities, types of datasets utilised, and the spectrum of KC conditions investigated over the past decade. A significant gap between academic research and practical implementation in clinical settings is identified, forming the basis for actionable recommendations tailored for both ML developers and ophthalmologists. Additionally, a proposed roadmap model is presented to facilitate the integration of ML models into clinical practice, enhancing diagnostic accuracy and patient care.
The analysis revealed that the diagnosis of KC predominantly relies on supervised classifiers (97%), with Random Forest being the most used algorithm (27%), followed by Deep Learning including Convolution Neural Networks (16%), Feedforward and Feedback Neural Networks (12%), and Support Vector Machines (12%). Pentacam is identified as the leading corneal imaging modality (56%), and a substantial majority of studies (91%) utilize local datasets, primarily consisting of numerical corneal parameters (77%). The most studied KC conditions were non-KC (NKC) vs. clinical KC (CKC) (29%), NKC vs. Subclinical KC (SCKC) (24%), NKC vs. SCKC vs. CKC (20%), SCKC vs. CKC (7%). However, only 20% of studies focused on addressing KC severity stages, emphasizing the need for more research in this area. These findings highlight the current landscape of ML in KC diagnosis and uncover existing challenges, and suggest potential avenues for further research and development, with particular emphasis on the dominance of certain algorithms and imaging modalities.
Key obstacles include the lack of consensus on an objective diagnostic standard for early KC detection and severity staging, limited multidisciplinary collaboration, and restricted access to public datasets. Further research is crucial to overcome these challenges and apply findings in clinical practice.
回顾过去十年间报道机器学习（ML）技术在圆锥角膜（KC）诊断中作用的研究，阐明近期进展，同时突出学术研究与临床实际应用之间的现有差距。
综述过程始于使用相关关键词对主要数字图书馆进行系统检索。然后应用一套严格的纳入和排除标准，最终确定62篇文章进行分析。制定关键研究问题，以探讨过去十年间ML在KC诊断方面的进展、角膜成像方式、所使用数据集的类型以及所研究的KC病症范围。确定了学术研究与临床实际应用之间的显著差距，为针对ML开发者和眼科医生的可行建议奠定了基础。此外，还提出了一个路线图模型，以促进ML模型融入临床实践，提高诊断准确性并改善患者护理。
分析显示，KC诊断主要依赖监督分类器（97%），其中随机森林是使用最多的算法（27%），其次是深度学习，包括卷积神经网络（16%）、前馈和反馈神经网络（12%）以及支持向量机（12%）。Pentacam被确定为主要的角膜成像方式（56%），并且绝大多数研究（91%）使用本地数据集，主要由数值角膜参数组成（77%）。研究最多的KC病症是正常角膜（NKC）与临床圆锥角膜（CKC）（29%）、NKC与亚临床圆锥角膜（SCKC）（24%）、NKC与SCKC与CKC（20%）、SCKC与CKC（7%）。然而，只有20%的研究专注于解决KC严重程度阶段问题，强调该领域需要更多研究。这些发现突出了当前ML在KC诊断中的现状，揭示了现有挑战，并提出了进一步研究和开发的潜在途径，特别强调了某些算法和成像方式的主导地位。
主要障碍包括在早期KC检测和严重程度分期的客观诊断标准上缺乏共识、多学科合作有限以及获取公共数据集受限。进一步研究对于克服这些挑战并将研究结果应用于临床实践至关重要。
