Evaluation of Artificial Intelligence–based Quantitative Analysis to Identify Clinically Significant Severe Retinopathy of Prematurity

Abstract

Purpose: To evaluate the screening potential of a deep learning algorithm-derived severity score by determining its ability to detect clinically significant severe retinopathy of prematurity (ROP).Methods: Fundus photographs were collected, and standard panel diagnosis was generated for each examination by combining three independent image-based gradings. All images were analyzed using a deep learning algorithm, and a quantitative assessment of retinal vascular abnormality (DeepROP score) was assigned on a 1 to 100 scale. The area under the receiver operating curve and distribution pattern of all diagnostic parameters and categories of ROP were analyzed. The correlation between the DeepROP score and expert rank ordering according to overall ROP severity of 50 examinations was calculated.Results: A total of 9,882 individual examinations with 54,626 images from 2,801 infants were analyzed. Fifty-six examinations (0.6%) demonstrated Type 1 ROP and 54 examinations (0.5%) demonstrated Type 2 ROP. The DeepROP score had an area under the receiver operating curve of 0.981 for detecting Type 1 ROP and 0.986 for Type 2 ROP. There was a statistically significant correlation between the expert rank ordering of overall disease severity and the DeepROP score (correlation coefficient 0.758, P , 0.001). When hypothetical referral cutoff score of 35 was selected, all cases of severe ROP (Type 1 and Type 2 ROP) was captured and 8,562 eyes (87.6%) with no or mild ROP were excluded. Conclusion:The DeepROP score determined by deep learning algorithm was an objective and quantitative indicator for the severity of ROP, and it had potential in automated detecting clinically significant severe ROP.

Fundus Tessellated Density of Pathologic Myopia

“…Indeed, the advancements in artificial intelligence–assisted imaging processing have opened up new possibilities for semi-quantitative and quantitative analysis of fundus photographs and OCT scans for various ocular diseases, 16–19 and even possible to predict the progression and prognosis of myopia based on ocular biometrics 20 . However, there is a gap in the existing literature regarding the specific focus on FTD in patients with PM and the associated pathological changes observed in fundus photographs.…”

Artificial intelligence in retinal disease: clinical application, challenges, and future directions

“…Authors have developed a ROP vascular severity score with good correlation with the labels set by the International Classification of Retinopathy of Prematurity committee [ 147 ]. The DeepROP score [ 148 ] and i-ROP DL system are DL algorithms developed to evaluate clinically significant severe ROP at the posterior pole [ 149 ]. ROP plus disease, a more aggressive form of ROP, is often difficult to diagnose given the lack of consensus among ophthalmologists; several authors have evaluated automated algorithms that may be able to objectively diagnose plus disease [ 150 – 153 ].…”

Application of Artificial Intelligence in the Early Detection of Retinopathy of Prematurity: Review of the Literature

“…This study also predicted that up to 51.3% fewer examinations could be required for low-risk infants without missing cases of TR-ROP. Another study used a DL system to calculate a VSS and this metric alone was used to identify patients at an increased risk of ROP [31]. An area under the operating characteristic curve was calculated at 0.99 for the detection of referral-requiring ROP (RR-ROP), corresponding to a 100% sensitivity and 90% specificity for the diagnosis of RR-ROP.…”

“…Several validation and comparison studies of an AI-based quantitative VSS to predict occurrence of ROP have been conducted with promising results [24, 25, 31, 32, 33, 34]. A comparison study was conducted to determine how well a DL ROP algorithm-based VSS correlates to expert labels of disease in retinal fundus photos [32].…”