Lanza Michele, Koprowski Robert, Boccia Rosa, Ruggiero Adriano, De Rosa Luigi, Tortori Antonia, Wilczyński Sławomir, Melillo Paolo, Sbordone Sandro, Simonelli Francesca
Multidisciplinary Department of Medical, Surgical and Dental Sciences, University of Campania Luigi Vanvitelli, 80100 Napoli, Italy.
Institute of Biomedical Engineering, Faculty of Science and Technology, University of Silesia in Katowice, Bedzińska 39, 41-200 Sosnowiec, Poland.
J Clin Med. 2021 Nov 19;10(22):5399. doi: 10.3390/jcm10225399.
Artificial intelligence (AI) is becoming ever more frequently applied in medicine and, consequently, also in ophthalmology to improve both the quality of work for physicians and the quality of care for patients. The aim of this study is to use AI, in particular classification tree, for the evaluation of both ocular and systemic features involved in the onset of complications due to cataract surgery in a teaching hospital.
The charts of 1392 eyes of 1392 patients, with a mean age of 71.3 ± 8.2 years old, were reviewed to collect the ocular and systemic data before, during and after cataract surgery, including post-operative complications. All these data were processed by a classification tree algorithm, producing more than 260 million simulations, aiming to develop a predictive model.
Postoperative complications were observed in 168 patients. According to the AI analysis, the pre-operative characteristics involved in the insurgence of complications were: ocular comorbidities, lower visual acuity, higher astigmatism and intra-operative complications.
Artificial intelligence application may be an interesting tool in the physician's hands to develop customized algorithms that can, in advance, define the post-operative complication risk. This may help in improving both the quality and the outcomes of the surgery as well as in preventing patient dissatisfaction.
人工智能（AI）在医学领域的应用日益频繁，因此在眼科领域也得到了应用，以提高医生的工作质量和患者的护理质量。本研究的目的是使用人工智能，特别是分类树，来评估一家教学医院中白内障手术并发症发生时涉及的眼部和全身特征。
回顾了1392例患者的1392只眼睛的病历，患者平均年龄为71.3±8.2岁，收集白内障手术前、手术期间和手术后的眼部和全身数据，包括术后并发症。所有这些数据都通过分类树算法进行处理，产生了超过2.6亿次模拟，旨在开发一个预测模型。
168例患者出现术后并发症。根据人工智能分析，并发症发生前的术前特征包括：眼部合并症、视力较低、散光较高和术中并发症。
人工智能应用可能是医生手中一个有趣的工具，可用于开发定制算法，提前确定术后并发症风险。这可能有助于提高手术质量和效果，并防止患者不满。
