Development and international validation of custom-engineered and code-free deep-learning models for detection of plus disease in retinopathy of prematurity: a retrospective study.

BACKGROUND: Retinopathy of prematurity (ROP), a leading cause of childhood blindness, is diagnosed through interval screening by paediatric ophthalmologists. However, improved survival of premature neonates coupled with a scarcity of available experts has raised concerns about the sustainability of this approach. We aimed to develop bespoke and code-free deep learning-based classifiers for plus disease, a hallmark of ROP, in an ethnically diverse population in London, UK, and externally validate them in ethnically, geographically, and socioeconomically diverse populations in four countries and three continents. Code-free deep learning is not reliant on the availability of expertly trained data scientists, thus being of particular potential benefit for low resource health-care settings. METHODS: This retrospective cohort study used retinal images from 1370 neonates admitted to a neonatal unit at Homerton University Hospital NHS Foundation Trust, London, UK, between 2008 and 2018. Images were acquired using a Retcam Version 2 device (Natus Medical, Pleasanton, CA, USA) on all babies who were either born at less than 32 weeks gestational age or had a birthweight of less than 1501 g. Each images was graded by two junior ophthalmologists with disagreements adjudicated by a senior paediatric ophthalmologist. Bespoke and code-free deep learning models (CFDL) were developed for the discrimination of healthy, pre-plus disease, and plus disease. Performance was assessed internally on 200 images with the majority vote of three senior paediatric ophthalmologists as the reference standard. External validation was on 338 retinal images from four separate datasets from the USA, Brazil, and Egypt with images derived from Retcam and the 3nethra neo device (Forus Health, Bengaluru, India). FINDINGS: Of the 7414 retinal images in the original dataset, 6141 images were used in the final development dataset. For the discrimination of healthy versus pre-plus or plus disease, the bespoke model had an area under the curve (AUC) of 0·986 (95% CI 0·973-0·996) and the CFDL model had an AUC of 0·989 (0·979-0·997) on the internal test set. Both models generalised well to external validation test sets acquired using the Retcam for discriminating healthy from pre-plus or plus disease (bespoke range was 0·975-1·000 and CFDL range was 0·969-0·995). The CFDL model was inferior to the bespoke model on discriminating pre-plus disease from healthy or plus disease in the USA dataset (CFDL 0·808 [95% CI 0·671-0·909, bespoke 0·942 [0·892-0·982]], p=0·0070). Performance also reduced when tested on the 3nethra neo imaging device (CFDL 0·865 [0·742-0·965] and bespoke 0·891 [0·783-0·977]). INTERPRETATION: Both bespoke and CFDL models conferred similar performance to senior paediatric ophthalmologists for discriminating healthy retinal images from ones with features of pre-plus or plus disease; however, CFDL models might generalise less well when considering minority classes. Care should be taken when testing on data acquired using alternative imaging devices from that used for the development dataset. Our study justifies further validation of plus disease classifiers in ROP screening and supports a potential role for code-free approaches to help prevent blindness in vulnerable neonates. FUNDING: National Institute for Health Research Biomedical Research Centre based at Moorfields Eye Hospital NHS Foundation Trust and the University College London Institute of Ophthalmology. TRANSLATIONS: For the Portuguese and Arabic translations of the abstract see Supplementary Materials section

Scleral buckle surgery in Ghana: a decade comparison of the anatomic and visual outcome

Imoro Z Braimah,1,2 Stephen Akafo,1,2 Jay Chhablani3 1Department of Surgery, School of Medicine and Dentistry, College of Health Sciences, University of Ghana, Accra, Ghana; 2Eye Centre, Korle Bu Teaching Hospital, Accra, Ghana; 3Srimati Kanuri Santhamma Centre for Vitreo-Retinal Diseases, KAR Campus, L V Prasad Eye Institute, Hyderabad, Telangana, India Purpose: To compare the anatomic and visual outcome of scleral buckle (SB) surgery in Korle Bu Teaching Hospital between 2002 and 2005 and 2011 and 2014. Materials and methods: In this retrospective comparative study, the medical records of patients who have undergone SB for rhegmatogenous retinal detachment from January 2002 to December 2005 (group A) and from January 2011 to December 2014 (group B) in Korle Bu Teaching Hospital were examined. The clinical history, surgical techniques, and outcomes of treatment were analyzed. The main outcome measures were primary anatomic success (retina reattached for at least 3 months postoperatively after a single procedure), overall anatomic success (combined primary anatomic success and success following revision of SB with at least 3 months follow-up), mean postoperative best-corrected visual acuity (BCVA), and complications. Results: One hundred fifty-eight eyes (71 eyes in group A and 87 eyes in group B) were treated with SB in this study. The mean duration of rhegmatogenous retinal detachment was 105.5 days. Thirty-four (21.8%) of fellow eyes had BCVA worse than 6/60 at presentation. The primary anatomic success was comparable between the two groups; 70% in group A and 67.9% in group B (P=0.79). The overall anatomic success was also comparable between the two groups (76.7% in group A vs 79.8% in group B) (P=0.788). The mean postoperative BCVA in logMAR was significantly better than the mean preoperative BCVA (P<0.0001). Group B had significantly better mean BCVA (P=0.002) and longer duration of follow-up (P<0.0001) compared with group A at the last follow-up visit. Conclusion: The anatomic success of SB between the two time periods was comparable. A longer postoperative duration of follow-up was associated with a better visual outcome after SB. Keywords: rhegmatogenous retinal detachment, scleral buckle surgery, laser retinopexy, cryotherapy, Ghan