PDF(Pubmed)
Abstract：
UNASSIGNED: New developments in artificial intelligence, particularly with promising results in early detection and management of keratoconus, have favorably altered the natural history of the disease over the last few decades. Features of artificial intelligence in different machine such as anterior segment optical coherence tomography, and femtosecond laser technique have improved safety, precision, effectiveness, and predictability of treatment modalities of keratoconus (from contact lenses to keratoplasty techniques). These options ingrained in artificial intelligence are already underway and allow ophthalmologist to approach disease in the most non-invasive way.
UNASSIGNED: This study comprehensively describes all of the treatment modalities of keratoconus considering machine learning strategies.
UNASSIGNED: A multidimensional comprehensive systematic narrative review.
UNASSIGNED: A comprehensive search was done in the five main electronic databases (PubMed, Scopus, Web of Science, Embase, and Cochrane), without language and time or type of study restrictions. Afterward, eligible articles were selected by screening the titles and abstracts based on main mesh keywords. For potentially eligible articles, the full text was also reviewed.
UNASSIGNED: Artificial intelligence demonstrates promise in keratoconus diagnosis and clinical management, spanning early detection (especially in subclinical cases), preoperative screening, postoperative ectasia prediction after keratorefractive surgery, and guiding surgical decisions. The majority of studies employed a solitary machine learning algorithm, whereas minor studies assessed multiple algorithms that evaluated the association of various keratoconus staging and management strategies. Last but not least, AI has proven effective in guiding the implantation of intracorneal ring segments in keratoconus corneas and predicting surgical outcomes.
UNASSIGNED: The efficient and widespread clinical translation of machine learning models in keratoconus management is a crucial goal of potential future approaches to better visual performance in keratoconus patients.
UNASSIGNED: The article has been registered through PROSPERO, an international database of prospectively registered systematic reviews, with the ID: CRD42022319338.
Keratoconus: from fundamentals to future Artificial intelligence has changed how we treat the eye disease keratoconus in recent years. This study examines the many keratoconus therapies available, including surgery and contact lens wear, and how artificial intelligence can improve the safety and accuracy of these procedures. We combed through numerous papers to locate this data. To achieve the best outcomes, several parameters and methods should be evaluated. According to the study, some elements from eye scans are more useful than others. The idea behind using artificial intelligence is to help patients see better and treat keratoconus more effectively.
UNASSIGNED: This study comprehensively describes all of the treatment modalities of keratoconus considering machine learning strategies.
UNASSIGNED: A multidimensional comprehensive systematic narrative review.
UNASSIGNED: A comprehensive search was done in the five main electronic databases (PubMed, Scopus, Web of Science, Embase, and Cochrane), without language and time or type of study restrictions. Afterward, eligible articles were selected by screening the titles and abstracts based on main mesh keywords. For potentially eligible articles, the full text was also reviewed.
UNASSIGNED: Artificial intelligence demonstrates promise in keratoconus diagnosis and clinical management, spanning early detection (especially in subclinical cases), preoperative screening, postoperative ectasia prediction after keratorefractive surgery, and guiding surgical decisions. The majority of studies employed a solitary machine learning algorithm, whereas minor studies assessed multiple algorithms that evaluated the association of various keratoconus staging and management strategies. Last but not least, AI has proven effective in guiding the implantation of intracorneal ring segments in keratoconus corneas and predicting surgical outcomes.
UNASSIGNED: The efficient and widespread clinical translation of machine learning models in keratoconus management is a crucial goal of potential future approaches to better visual performance in keratoconus patients.
UNASSIGNED: The article has been registered through PROSPERO, an international database of prospectively registered systematic reviews, with the ID: CRD42022319338.
Keratoconus: from fundamentals to future Artificial intelligence has changed how we treat the eye disease keratoconus in recent years. This study examines the many keratoconus therapies available, including surgery and contact lens wear, and how artificial intelligence can improve the safety and accuracy of these procedures. We combed through numerous papers to locate this data. To achieve the best outcomes, several parameters and methods should be evaluated. According to the study, some elements from eye scans are more useful than others. The idea behind using artificial intelligence is to help patients see better and treat keratoconus more effectively.
摘要：
■人工智能的新发展，特别是在圆锥角膜的早期发现和管理方面有希望的结果，在过去的几十年里，已经有利地改变了这种疾病的自然史。人工智能在不同机器中的特征，如眼前节光学相干断层扫描，飞秒激光技术提高了安全性，精度,有效性,以及圆锥角膜治疗方式的可预测性（从隐形眼镜到角膜移植术）。这些在人工智能中根深蒂固的选择已经在进行中，允许眼科医生以最无创的方式治疗疾病。
■本研究全面描述了考虑机器学习策略的圆锥角膜的所有治疗方式。
■多维综合系统叙事回顾。
■在五个主要的电子数据库（PubMed，Scopus,WebofScience,Embase,和Cochrane），没有语言和时间或学习类型的限制。之后,通过根据主要网格关键词筛选标题和摘要来选择符合条件的文章.对于可能符合条件的文章，并对全文进行了审查。
■人工智能在圆锥角膜诊断和临床管理方面显示出希望，跨越早期检测(特别是在亚临床病例中)，术前筛查,角膜屈光性手术后的扩张预测，指导手术决策。大多数研究采用了单独的机器学习算法，而次要研究评估了多种算法，这些算法评估了各种圆锥角膜分期和管理策略之间的关联。最后但并非最不重要的,AI已被证明可有效指导角膜内环形节段在圆锥角膜中的植入并预测手术结果。
■机器学习模型在圆锥角膜管理中的有效和广泛的临床翻译是圆锥角膜患者更好的视觉表现的潜在未来方法的关键目标。
■该文章已通过PROSPERO注册，预期注册的系统评价的国际数据库，ID：CRD42022319338。
圆锥角膜：从基础到未来人工智能近年来改变了我们治疗圆锥角膜的方式。这项研究检查了许多可用的圆锥角膜疗法，包括手术和隐形眼镜佩戴，以及人工智能如何提高这些程序的安全性和准确性。我们梳理了许多论文来找到这些数据。为了取得最好的结果，应该评估几个参数和方法。根据研究,眼睛扫描中的一些元素比其他元素更有用。使用人工智能背后的想法是帮助患者更好地看到并更有效地治疗圆锥角膜。
■本研究全面描述了考虑机器学习策略的圆锥角膜的所有治疗方式。
■多维综合系统叙事回顾。
■在五个主要的电子数据库（PubMed，Scopus,WebofScience,Embase,和Cochrane），没有语言和时间或学习类型的限制。之后,通过根据主要网格关键词筛选标题和摘要来选择符合条件的文章.对于可能符合条件的文章，并对全文进行了审查。
■人工智能在圆锥角膜诊断和临床管理方面显示出希望，跨越早期检测(特别是在亚临床病例中)，术前筛查,角膜屈光性手术后的扩张预测，指导手术决策。大多数研究采用了单独的机器学习算法，而次要研究评估了多种算法，这些算法评估了各种圆锥角膜分期和管理策略之间的关联。最后但并非最不重要的,AI已被证明可有效指导角膜内环形节段在圆锥角膜中的植入并预测手术结果。
■机器学习模型在圆锥角膜管理中的有效和广泛的临床翻译是圆锥角膜患者更好的视觉表现的潜在未来方法的关键目标。
■该文章已通过PROSPERO注册，预期注册的系统评价的国际数据库，ID：CRD42022319338。
圆锥角膜：从基础到未来人工智能近年来改变了我们治疗圆锥角膜的方式。这项研究检查了许多可用的圆锥角膜疗法，包括手术和隐形眼镜佩戴，以及人工智能如何提高这些程序的安全性和准确性。我们梳理了许多论文来找到这些数据。为了取得最好的结果，应该评估几个参数和方法。根据研究,眼睛扫描中的一些元素比其他元素更有用。使用人工智能背后的想法是帮助患者更好地看到并更有效地治疗圆锥角膜。
