Cost-effectiveness of Artificial Intelligence–Based Retinopathy of Prematurity Screening

Abstract

IMPORTANCE Artificial intelligence (AI)-based retinopathy of prematurity (ROP) screening may improve ROP care, but its cost-effectiveness is unknown. OBJECTIVE To evaluate the relative cost-effectiveness of autonomous and assistive AI-based ROP screening compared with telemedicine and ophthalmoscopic screening over a range of estimated probabilities, costs, and outcomes. DESIGN, SETTING, AND PARTICIPANTSA cost-effectiveness analysis of AI ROP screening compared with ophthalmoscopy and telemedicine via economic modeling was conducted. Decision trees created and analyzed modeled outcomes and costs of 4 possible ROP screening strategies: ophthalmoscopy, telemedicine, assistive AI with telemedicine review, and autonomous AI with only positive screen results reviewed. A theoretical cohort of infants requiring ROP screening in the United States each year was analyzed.MAIN OUTCOMES AND MEASURES Screening and treatment costs were based on Current Procedural Terminology codes and included estimated opportunity costs for physicians. Outcomes were based on the Early Treatment of ROP study, defined as timely treatment, late treatment, or correctly untreated. Incremental cost-effectiveness ratios were calculated at a willingness-to-pay threshold of $100 000. One-way and probabilistic sensitivity analyses were performed comparing AI strategies to telemedicine and ophthalmoscopy to evaluate the cost-effectiveness across a range of assumptions. In a secondary analysis, the modeling was repeated and assumed a higher sensitivity for detection of severe ROP using AI compared with ophthalmoscopy. RESULTSThis theoretical cohort included 52 000 infants born 30 weeks' gestation or earlier or weighed 1500 g or less at birth. Autonomous AI was as effective and less costly than any other screening strategy.

Optical Coherence Tomography for Retinopathy of Prematurity and the Future of Retinopathy of Prematurity Screening

“…Numerous studies have shown that both telemedicine and screening for ROP are highly beneficial for quality of life, cost saving, and cost-effectiveness. 8 As we move toward a model where AI may be part of our screening programs, Morrison and colleagues 9 suggest that AI-based screening for ROP may be more costeffective than telemedicine and ophthalmoscopy. They demonstrated in a probabilistic sensitivity analysis that autonomous AI screening was more than 60% likely to be cost- effective at all willingness-to-pay levels vs other modalities.…”

Equity within AI systems: What can health leaders expect?

“…For example, autonomous AI was as effective for and less costly (up to $34 for compared with telemedicine and $64 and $91 compared with ophthalmoscopy) for retinopathy of prematurity screening. 54 Also, research shows that AI-based tools produce cost savings if used as a strategy in screening colonoscopy, 55 and for breast cancer screening. 56 While this is encouraging, it cannot be generalized and need to be studied on a case by case basis.…”

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID-19: A Narrative Review

“…According to a recent study on the internet of things (IoT), machine learning is helping to speed up the growth of industrial supply chains, which saves both time and money. Morrison et al [ 280 ] developed a cost-effectiveness analysis of an AI-based solution [ 280 ] for retinopathy of prematurity (ROP) screening. The authors adapted the decision tree’s machine learning solutions and compared them against three different kinds of strategies such as ophthalmoscopy, telemedicine, and assistive AI with telemedicine review.…”