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AI imaging helps enhance early diabetic retinopathy detection and improve patient outcomes: JAMA
A recent retrospective cohort study published in the JAMA Ophthalmology highlighted the slow adoption of artificial intelligence (AI) systems for detecting diabetic retinopathy (DR) in the United States, despite their proven efficacy. The study analyzed the use of Current Procedural Terminology (CPT) code 92229, established in January 2021 to support reimbursement for AI-based DR screening, across a database of over 107 million patients spanning 62 healthcare organizations.
The findings revealed that, out of nearly 5 million patients with diabetes examined from January 2019 to December 2023, only 4.2% underwent any ophthalmic imaging for DR. Within this subset, the use of AI-based imaging represented just 0.09% of total screenings, with only 3,440 patients utilizing the AI code 92229 since its inception. By 2023, the frequency of AI imaging had seen only a marginal increase, from 58.0 to 58.6 instances per 100,000 diabetic patients which indicated a slow adoption.
Also, traditional imaging techniques such as optical coherence tomography (OCT, CPT code 92134) and fundus photography (CPT code 92250) were more commonly used. OCT was performed in 80.3% of patients with at least one type of ophthalmic imaging, while fundus photography was utilized in 35.0% of cases. Traditional remote imaging (CPT codes 92227 and 92228) remained minimally used, accounting for only 1.0% and 2.5% of patients, respectively.
While the overall use of remote imaging methods surged by 90.16% between 2021 and 2023, AI-based screening remained disproportionately low. The data indicated that AI-based imaging had a higher referral rate to OCT (7.74%) when compared to traditional remote imaging (5.53%) by showing its potential for more targeted and effective DR detection. However, adoption hurdles such as cost, lack of awareness, and integration issues may be limiting widespread use. More than 80% of patients receiving AI-based imaging were concentrated in the South, a region comprising only 40% of other imaging modalities. Additionally, nearly half of the patients screened with AI systems were Black, in contrast to roughly a quarter seen in other imaging methods.
Despite FDA approval for AI-based systems like LumineticsCore and EyeArt, the broader implementation will require improved support for workflow integration and collaboration between primary care providers and ophthalmologists. The programs such as the Stanford Teleophthalmology Autonomous Testing and Universal Screening initiative highlight the importance of streamlined processes and patient-centered scheduling. Overall, the study points to a need for targeted strategies to boost the uptake of AI imaging, enhance early DR detection, and improve patient outcomes through more accessible and integrated screening solutions.
Source:
Shah, S. A., Sokol, J. T., Wai, K. M., Rahimy, E., Myung, D., Mruthyunjaya, P., & Parikh, R. (2024). Use of Artificial Intelligence–Based Detection of Diabetic Retinopathy in the US. In JAMA Ophthalmology. American Medical Association (AMA). https://doi.org/10.1001/jamaophthalmol.2024.4493
Neuroscience Masters graduate
Jacinthlyn Sylvia, a Neuroscience Master's graduate from Chennai has worked extensively in deciphering the neurobiology of cognition and motor control in aging. She also has spread-out exposure to Neurosurgery from her Bachelor’s. She is currently involved in active Neuro-Oncology research. She is an upcoming neuroscientist with a fiery passion for writing. Her news cover at Medical Dialogues feature recent discoveries and updates from the healthcare and biomedical research fields. She can be reached at editorial@medicaldialogues.in
Dr Kamal Kant Kohli-MBBS, DTCD- a chest specialist with more than 30 years of practice and a flair for writing clinical articles, Dr Kamal Kant Kohli joined Medical Dialogues as a Chief Editor of Medical News. Besides writing articles, as an editor, he proofreads and verifies all the medical content published on Medical Dialogues including those coming from journals, studies,medical conferences,guidelines etc. Email: drkohli@medicaldialogues.in. Contact no. 011-43720751