Department of Ophthalmology, Queensland Children's Hospital, South Brisbane, Queensland, Australia.
Centre for Children's Health Research, South Brisbane, Queensland, Australia.
Transl Vis Sci Technol. 2023 Aug 1;12(8):13. doi: 10.1167/tvst.12.8.13.
Retinopathy of prematurity (ROP) is a sight-threatening vasoproliferative retinal disease affecting premature infants. The detection of plus disease, a severe form of ROP requiring treatment, remains challenging owing to subjectivity, frequency, and time intensity of retinal examinations. Recent artificial intelligence (AI) algorithms developed to detect plus disease aims to alleviate these challenges; however, they have not been tested against a diverse neonatal population. Our study aims to validate ROP.AI, an AI algorithm developed from a single cohort, against a multicenter Australian cohort to determine its performance in detecting plus disease.
Retinal images captured during routine ROP screening from May 2021 to February 2022 across five major tertiary centers throughout Australia were collected and uploaded to ROP.AI. AI diagnostic output was compared with one of five ROP experts. Sensitivity, specificity, negative predictive value, and area under the receiver operator curve were determined.
We collected 8052 images. The area under the receiver operator curve for the diagnosis of plus disease was 0.75. ROP.AI achieved 84% sensitivity, 43% specificity, and 96% negative predictive value for the detection of plus disease after operating point optimization.
ROP.AI was able to detect plus disease in an external, multicenter cohort despite being trained from a single center. Algorithm performance was demonstrated without preprocessing or augmentation, simulating real-world clinical applicability. Further training may improve generalizability for clinical implementation.
These results demonstrate ROP.AI's potential as a screening tool for the detection of plus disease in future clinical practice and provides a solution to overcome current diagnostic challenges.
早产儿视网膜病变（ROP）是一种威胁视力的血管增生性视网膜疾病，影响早产儿。由于视网膜检查的主观性、频率和时间强度，加上疾病（ROP 的一种严重形式，需要治疗）的检测仍然具有挑战性。最近开发的用于检测加病的人工智能（AI）算法旨在缓解这些挑战；然而，它们尚未针对多样化的新生儿人群进行测试。我们的研究旨在验证 ROP.AI，这是一种从单一队列开发的 AI 算法，以确定其在检测加病方面的性能。
从 2021 年 5 月至 2022 年 2 月，从澳大利亚五个主要三级中心的常规 ROP 筛查中收集视网膜图像，并上传到 ROP.AI。将 AI 诊断结果与五位 ROP 专家之一进行比较。确定了敏感性、特异性、阴性预测值和接收者操作特征曲线下的面积。
我们收集了 8052 张图像。诊断加病的接收者操作特征曲线下的面积为 0.75。ROPAI 在经过操作点优化后，对加病的检测达到了 84%的敏感性、43%的特异性和 96%的阴性预测值。
尽管 ROP.AI 是从单一中心训练的，但它能够在外部多中心队列中检测到加病。在没有预处理或增强的情况下，该算法的性能得到了证明，模拟了实际的临床适用性。进一步的培训可能会提高临床实施的通用性。
医学
