Artificial intelligence-based pathologic myopia identification system in the ophthalmology residency training program

Abstract

Background: Artificial intelligence (AI) has been successfully applied to the screening tasks of fundus diseases. However, few studies focused on the potential of AI to aid medical teaching in the residency training program. This study aimed to evaluate the effectiveness of the AI-based pathologic myopia (PM) identification system in the ophthalmology residency training program and assess the residents’ feedback on this system.Materials and Methods: Ninety residents in the ophthalmology department at the Second Affiliated Hospital of Zhejiang University were randomly assigned to three groups. In group A, residents learned PM through an AI-based PM identification system. In group B and group C, residents learned PM through a traditional lecture given by two senior specialists independently. The improvement in resident performance was evaluated by comparing the pre-and post-lecture scores of a specifically designed test using a paired t-test. The difference among the three groups was evaluated by one-way ANOVA. Residents’ evaluations of the AI-based PM identification system were measured by a 17-item questionnaire.Results: The post-lecture scores were significantly higher than the pre-lecture scores in group A (p < 0.0001). However, there was no difference between pre-and post-lecture scores in group B (p = 0.628) and group C (p = 0.158). Overall, all participants were satisfied and agreed that the AI-based PM identification system was effective and helpful to acquire PM identification, myopic maculopathy (MM) classification, and “Plus” lesion localization.Conclusion: It is still difficult for ophthalmic residents to promptly grasp the knowledge of identification of PM through a single traditional lecture, while the AI-based PM identification system effectively improved residents’ performance in PM identification and received satisfactory feedback from residents. The application of the AI-based PM identification system showed advantages in promoting the efficiency of the ophthalmology residency training program.

Performance of artificial intelligence for the detection of pathological myopia from colour fundus images: a systematic review and meta-analysis

“…Tools based on AI models can work with clinicians to guide triage and referral decisions in general practice or non-specialist centres with a high case burden, as described by De Fauw et al (42). This may have particular bene t for clinicians in training, or in regions with reduced incidence of PM, who may derive bene t from decision support in selecting cases of PM (43), or in areas with poor access to healthcare services. (44) Identi cation and close follow-up of patients with uncomplicated PM is crucial in enabling early management of treatable complications such as myopic choroidal neovascularisation (CNV) -for example with anti-VEGF therapy (45) -optimisation of visual acuity and stabilisation of progressive myopia.…”

Artificial intelligence technology for myopia challenges: A review

“…Besides the above-mentioned research with direct outcomes, completing semantic segmentation tasks on fundus images of myopic eyes helps us better comprehend the morphological changes ( Read et al, 2019 ). It can also aid in the training of physicians to interpret images ( Fang et al, 2022 ). In labeling the choroid and the layers of the retina in OCT images, Cahyo et al (2020) took advantage of the multi-scale feature fusion characteristic of UNet, thus preserving more information.…”

Application of artificial intelligence to the public health education

“…Among them, the recurrent neural network and the feed-forward neural network have achieved 97.59 and 100% in the diagnosis of liver disease hepatitis virus, respectively ( 28 ). Moreover, AI has been successfully applied to healthy education and clinical training, including ophthalmology ( 29 ), radiology ( 30 ), physical ( 31 ), and residency training ( 32 ). For example, Fang et al ( 32 ) have applied AI-based pathologic myopia identification system in the ophthalmology residency training and achieved satisfactory training efficiency.…”

“…Moreover, AI has been successfully applied to healthy education and clinical training, including ophthalmology ( 29 ), radiology ( 30 ), physical ( 31 ), and residency training ( 32 ). For example, Fang et al ( 32 ) have applied AI-based pathologic myopia identification system in the ophthalmology residency training and achieved satisfactory training efficiency. All of these have shown the powerful potential of AI.…”