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

Pheochromocytoma: a changing perspective and current concepts

This article aims to review current concepts in diagnosing and managing pheochromocytoma and paraganglioma (PPGL). Personalized genetic testing is vital, as 40–60% of tumors are linked to a known mutation. Tumor DNA should be sampled first. Next-generation sequencing is the best and most cost-effective choice and also helps with the expansion of current knowledge. Recent advancements have also led to the increased incorporation of regulatory RNA, metabolome markers, and the NETest in PPGL workup. PPGL presentation is highly volatile and nonspecific due to its multifactorial etiology. Symptoms mainly derive from catecholamine (CMN) excess or mass effect, primarily affecting the cardiovascular system. However, paroxysmal nature, hypertension, and the classic triad are no longer perceived as telltale signs. Identifying high-risk subjects and diagnosing patients at the correct time by using appropriate personalized methods are essential. Free plasma/urine catecholamine metabolites must be first-line examinations using liquid chromatography with tandem mass spectrometry as the gold standard analytical method. Reference intervals should be personalized according to demographics and comorbidity. The same applies to result interpretation. Threefold increase from the upper limit is highly suggestive of PPGL. Computed tomography (CT) is preferred for pheochromocytoma due to better cost-effectiveness and spatial resolution. Unenhanced attenuation of >10HU in non-contrast CT is indicative. The choice of extra-adrenal tumor imaging is based on location. Functional imaging with positron emission tomography/computed tomography and radionuclide administration improves diagnostic accuracy, especially in extra-adrenal/malignant or familial cases. Surgery is the mainstay treatment when feasible. Preoperative α-adrenergic blockade reduces surgical morbidity. Aggressive metastatic PPGL benefits from systemic chemotherapy, while milder cases can be managed with radionuclides. Short-term postoperative follow-up evaluates the adequacy of resection. Long-term follow-up assesses the risk of recurrence or metastasis. Asymptomatic carriers and their families can benefit from surveillance, with intervals depending on the specific gene mutation. Trials primarily focusing on targeted therapy and radionuclides are currently active. A multidisciplinary approach, correct timing, and personalization are key for successful PPGL management

Effect of socioeconomic deprivation as determined by the English deprivation deciles on the progression of diabetic retinopathy and maculopathy:a multivariate case-control analysis of 88 910 patients attending a South-East London diabetic eye screening service

PURPOSE: To determine associations between deprivation using the Index of Multiple Deprivation (IMD and individual IMD subdomains) with incident referable diabetic retinopathy/maculopathy (termed rDR).METHODS: Anonymised demographic and screening data collected by the South-East London Diabetic Eye Screening Programme were extracted from September 2013 to December 2019. Multivariable Cox proportional models were used to explore the association between the IMD, IMD subdomains and rDR.RESULTS: From 118 508 people with diabetes who attended during the study period, 88 910 (75%) were eligible. The mean (± SD) age was 59.6 (±14.7) years; 53.94% were male, 52.58% identified as white, 94.28% had type 2 diabetes and the average duration of diabetes was 5.81 (±6.9) years; rDR occurred in 7113 patients (8.00%). Known risk factors of younger age, Black ethnicity, type 2 diabetes, more severe baseline DR and diabetes duration conferred a higher risk of incident rDR. After adjusting for these known risk factors, the multivariable analysis did not show a significant association between IMD (decile 1 vs decile 10) and rDR (HR: 1.08, 95% CI: 0.87 to 1.34, p=0.511). However, high deprivation (decile 1) in three IMD subdomains was associated with rDR, namely living environment (HR: 1.64, 95% CI: 1.12 to 2.41, p=0.011), education skills (HR: 1.64, 95% CI: 1.12 to 2.41, p=0.011) and income (HR: 1.19, 95% CI: 1.02 to 1.38, p=0.024).CONCLUSION: IMD subdomains allow for the detection of associations between aspects of deprivation and rDR, which may be missed when using the aggregate IMD. The generalisation of these findings outside the UK population requires corroboration internationally.</p