Telemedicine for Retinopathy of Prematurity in 2020

Outcome of real-time telescreening for retinopathy of prematurity using videoconferencing in a community setting in Eastern India

PURPOSE

To evaluate the feasibility and outcome of a real-time retinopathy of prematurity (ROP) telescreening strategy using videoconferencing in a community setting in India.

METHOD

In a prospective study, trained allied ophthalmic personnel obtained the fundus images in the presence of the parents and local childcare providers. Analysis of images and parental counseling were done in real time by an ROP specialist located at a tertiary center using videoconferencing software. A subset of babies was also examined using bedside indirect ophthalmoscopy by an ROP care-trained ophthalmologist. The data were analyzed using descriptive statistics, sensitivity, specificity, positive and negative predictive values, and correlation coefficient.

RESULTS

Over 9 months, we examined 576 babies (1152 eyes) in six rural districts of India. The parents accepted the model as they recognized that a remotely located specialist was evaluating all images in real time. The strategy saved the travel time for ROP specialists by 477 h (47.7 working days) and for parents (47,406 h or 1975.25 days), along with the associated travel cost. In a subgroup analysis (100 babies, 200 eyes), the technology had a high sensitivity (97.2%) and negative predictivity value (92.7%). It showed substantial agreement (k = 0.708) with the bedside indirect ophthalmoscopy by ROP specialists with respect to the detection of treatment warranting ROP. Also, the strategy helped train the participants.

CONCLUSION

Real-time ROP telescreening using videoconferencing is sensitive enough to detect treatment warranting ROPs and saves skilled workforce and time. The real-time audiovisual connection allows optimal supervision of imaging, provides excellent training opportunities, and connects ophthalmologists directly with the parents.

Color fundus imaging in retinopathy of prematurity screening: Present and future

Advent of pediatric handheld fundus cameras like RetCam, 3netra Forus, and Phoenix ICON pediatric retinal camera has aided in effective screening of retinopathy of prematurity (ROP), especially in countries with limited number of trained specialists. Recent advent of various smartphone-based cameras has made pediatric fundus photography furthermore affordable and portable. Future advances like ultra-wide field fundus cameras, trans-pars-planar illumination pediatric fundus camera, artificial intelligence, deep learning algorithm, and handheld SS-OCTA can help in more accurate imaging and documentation. This article summarizes the features of existing and upcoming imaging modalities in detail, including their features, advantages, challenges, and effectiveness, which can help in implementation of telescreening as a standard screening protocol for ROP across developing as well as developed countries.

80 Years of vision: preventing blindness from retinopathy of prematurity

Retinopathy of prematurity (ROP) is one of the leading yet preventable causes of childhood blindness worldwide. The purpose of this review is to provide a practical template for observational and treatment methods in order to reduce the overall incidence of any ROP and to improve both short-term and long-term outcomes once Type 1 ROP (treatable ROP) develops.

The Role of Telemedicine to Alleviate an Increasingly Burdened Healthcare System: Retinopathy of Prematurity

Telemedicine-based remote digital fundus imaging (RDFI-TM) offers a promising platform for the screening of retinopathy of prematurity. RDFI-TM addresses some of the challenges faced by ophthalmologists in examining this vulnerable population in both low- and high-income countries. In this review, we studied the evidence on the use of RDFI-TM and analyzed the practical framework for RDFI-TM systems. We assessed the novel technological advances that can be deployed within RDFI-TM systems including noncontact imaging systems, smartphone-based imaging tools, and deep learning algorithms.