Retinal Specialist versus Artificial Intelligence Detection of Retinal Fluid from OCT Age-Related Eye Disease Study 2: 10-Year Follow-On Study
Retinal Specialist versus Artificial Intelligence Detection of Retinal Fluid from OCT Age-Related Eye Disease Study 2: 10-Year Follow-On Study
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DOI：
10.1016/j.opatha.2020.06.038
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发表时间：
2021-01-01
期刊：
影响因子：
13.7
通讯作者：
Chew, Emily Y.
中科院分区：
文献类型：
--
作者：
Keenan, Tiarnan D. L.;Clemons, Traci E.;Chew, Emily Y.
目的：评估视网膜专家在检测年龄相关性黄斑变性 (AMD) 眼睛的谱域 OCT (SD-OCT) 扫描中视网膜液存在的表现，并将性能与人工智能算法进行比较。设计：对人类视网膜专家和 Notal OCT 分析仪 (NOA) 对 2 种常见设备的 SD-OCT 扫描的视网膜液等级进行前瞻性比较。参与者：总共 1127 只眼睛651 名年龄相关眼病研究 2 10 年随访研究 (AREDS2-10Y) 参与者的 SD-OCT 扫描由阅读中心分级员分级（作为基本事实）。方法：AREDS2-10Y 研究人员对每次 SD-OCT 扫描是否存在视网膜内和视网膜下液体进行分级。另外，同样的扫描由 NOA 进行分级。主要结果指标：准确性（主要）、敏感性、特异性、精密度和 F1 分数。结果：在 1127 只眼睛中，32.8% 存在视网膜液体。对于检测视网膜液，研究人员的准确度为 0.805（95% 置信区间 [CI]，0.780-0.828），灵敏度为 0.468（95% CI，0.416-0.520），特异性为 0.970（95% CI，0.955-0.981）。 NOA 指标分别为 0.851（95% CI，0.829-0.871）、0.822（95% CI，0.779-0.859）、0.865（95% CI，0.839-0.889）。对于检测视网膜内液体，研究者指标为 0.815（95% CI，0.792-0.837）、0.403（95% CI，0.349-0.459）和 0.978（95% CI，0.966-0.987）； NOA 指标分别为 0.877（95% CI，0.857-0.896）、0.763（95% CI，0.713-0.808）和 0.922（95% CI，0.902-0.940）。对于检测视网膜下液，研究者指标为 0.946（95% CI，0.931-0.958）、0.583（95% CI，0.471-0.690）和 0.973（95% CI，0.962-0.982）； NOA 指标分别为 0.863 (95% CI, 0.842-0.882)、0.940 (95% CI, 0.867-0.980) 和 0.857 (95% CI, 0.835-0.877)。 结论：在使用 2 种常见设备获得的 SD-OCT 扫描的大型且具有挑战性的样本中，视网膜专家的结果并不完美检测视网膜液的准确性和灵敏度较低。对于视网膜内积液和疑难病例（B 扫描较少时出现的积液量较少）尤其如此。基于人工智能的检测达到了更高的准确度。该软件工具可以帮助医生检测视网膜液，这对于诊断、再治疗和预后任务非常重要。由爱思唯尔代表美国眼科学会出版
Purpose: To evaluate the performance of retinal specialists in detecting retinal fluid presence in spectral domain OCT (SD-OCT) scans from eyes with age-related macular degeneration (AMD) and compare performance with an artificial intelligence algorithm.Design: Prospective comparison of retinal fluid grades from human retinal specialists and the Notal OCT Analyzer (NOA) on SD-OCT scans from 2 common devices.Participants: A total of 1127 eyes of 651 Age-Related Eye Disease Study 2 10-year Follow-On Study (AREDS2-10Y) participants with SD-OCT scans graded by reading center graders (as the ground truth).Methods: The AREDS2-10Y investigators graded each SD-OCT scan for the presence/absence of intraretinal and subretinal fluid. Separately, the same scans were graded by the NOA.Main Outcome Measures: Accuracy (primary), sensitivity, specificity, precision, and F1-score.Results: Of the 1127 eyes, retinal fluid was present in 32.8%. For detecting retinal fluid, the investigators had an accuracy of 0.805 (95% confidence interval [CI], 0.780-0.828), a sensitivity of 0.468 (95% CI, 0.416-0.520), a specificity of 0.970 (95% CI, 0.955-0.981). The NOA metrics were 0.851 (95% CI, 0.829-0.871), 0.822 (95% CI, 0.779-0.859), 0.865 (95% CI, 0.839-0.889), respectively. For detecting intraretinal fluid, the investigator metrics were 0.815 (95% CI, 0.792-0.837), 0.403 (95% CI, 0.349-0.459), and 0.978 (95% CI, 0.966-0.987); the NOA metrics were 0.877 (95% CI, 0.857-0.896), 0.763 (95% CI, 0.713-0.808), and 0.922 (95% CI, 0.902-0.940), respectively. For detecting subretinal fluid, the investigator metrics were 0.946 (95% CI, 0.931-0.958), 0.583 (95% CI, 0.471-0.690), and 0.973 (95% CI, 0.962-0.982); the NOA metrics were 0.863 (95% CI, 0.842-0.882), 0.940 (95% CI, 0.867-0.980), and 0.857 (95% CI, 0.835-0.877), respectively.Conclusions: In this large and challenging sample of SD-OCT scans obtained with 2 common devices, retinal specialists had imperfect accuracy and low sensitivity in detecting retinal fluid. This was particularly true for intraretinal fluid and difficult cases (with lower fluid volumes appearing on fewer B-scans). Artificial intelligence based detection achieved a higher level of accuracy. This software tool could assist physicians in detecting retinal fluid, which is important for diagnostic, re-treatment, and prognostic tasks. Published by Elsevier on behalf of the American Academy of Ophthalmology