Diagnostic Performance of the Offline Medios Artificial Intelligence for Glaucoma Detection in a Rural Tele-Ophthalmology Setting.

This study assesses the diagnostic efficacy of offline Medios Artificial Intelligence (AI) glaucoma software in a primary eye care setting, using nonmydriatic fundus images from Remidio's Fundus-on-Phone (FOP NM-10). Artificial intelligence results were compared with tele-ophthalmologists' diagnoses and with a glaucoma specialist's assessment for those participants referred to a tertiary eye care hospital. Prospective cross-sectional study PARTICIPANTS: Three hundred three participants from 6 satellite vision centers of a tertiary eye hospital. At the vision center, participants underwent comprehensive eye evaluations, including clinical history, visual acuity measurement, slit lamp examination, intraocular pressure measurement, and fundus photography using the FOP NM-10 camera. Medios AI-Glaucoma software analyzed 42-degree disc-centric fundus images, categorizing them as normal, glaucoma, or suspect. Tele-ophthalmologists who were glaucoma fellows with a minimum of 3 years of ophthalmology and 1 year of glaucoma fellowship training, masked to artificial intelligence (AI) results, remotely diagnosed subjects based on the history and disc appearance. All participants labeled as disc suspects or glaucoma by AI or tele-ophthalmologists underwent further comprehensive glaucoma evaluation at the base hospital, including clinical examination, Humphrey visual field analysis, and OCT. Artificial intelligence and tele-ophthalmologist diagnoses were then compared with a glaucoma specialist's diagnosis. Sensitivity and specificity of Medios AI. Out of 303 participants, 299 with at least one eye of sufficient image quality were included in the study. The remaining 4 participants did not have sufficient image quality in both eyes. Medios AI identified 39 participants (13%) with referable glaucoma. The AI exhibited a sensitivity of 0.91 (95% confidence interval [CI]: 0.71-0.99) and specificity of 0.93 (95% CI: 0.89-0.96) in detecting referable glaucoma (definite perimetric glaucoma) when compared to tele-ophthalmologist. The agreement between AI and the glaucoma specialist was 80.3%, surpassing the 55.3% agreement between the tele-ophthalmologist and the glaucoma specialist amongst those participants who were referred to the base hospital. Both AI and the tele-ophthalmologist relied on fundus photos for diagnoses, whereas the glaucoma specialist's assessments at the base hospital were aided by additional tools such as Humphrey visual field analysis and OCT. Furthermore, AI had fewer false positive referrals (2 out of 10) compared to the tele-ophthalmologist (9 out of 10). Medios offline AI exhibited promising sensitivity and specificity in detecting referable glaucoma from remote vision centers in southern India when compared with teleophthalmologists. It also demonstrated better agreement with glaucoma specialist's diagnosis for referable glaucoma participants. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Upadhyaya S ，Rao DP ，Kavitha S ，Ballae Ganeshrao S ，Negiloni K ，Bhandary S ，Savoy FM ，Venkatesh R ... -  《-》

Agreement of a Novel Artificial Intelligence Software With Optical Coherence Tomography and Manual Grading of the Optic Disc in Glaucoma.

The offline artificial intelligence (AI) on a smartphone-based fundus camera shows good agreement and correlation with the vertical cup-to-disc ratio (vCDR) from the spectral-domain optical coherence tomography (SD-OCT) and manual grading by experts. The purpose of this study is to assess the agreement of vCDR measured by a new AI software from optic disc images obtained using a validated smartphone-based imaging device, with SD-OCT vCDR measurements, and manual grading by experts on a stereoscopic fundus camera. In a prospective, cross-sectional study, participants above 18 years (Glaucoma and normal) underwent a dilated fundus evaluation, followed by optic disc imaging including a 42-degree monoscopic disc-centered image (Remidio NM-FOP-10), a 30-degree stereoscopic disc-centered image (Kowa nonmyd WX-3D desktop fundus camera), and disc analysis (Cirrus SD-OCT). Remidio FOP images were analyzed for vCDR using the new AI software, and Kowa stereoscopic images were manually graded by 3 fellowship-trained glaucoma specialists. We included 473 eyes of 244 participants. The vCDR values from the new AI software showed strong agreement with SD-OCT measurements [95% limits of agreement (LoA)=-0.13 to 0.16]. The agreement with SD-OCT was marginally better in eyes with higher vCDR (95% LoA=-0.15 to 0.12 for vCDR>0.8). Interclass correlation coefficient was 0.90 (95% CI, 0.88-0.91). The vCDR values from AI software showed a good correlation with the manual segmentation by experts (interclass correlation coefficient=0.89, 95% CI, 0.87-0.91) on stereoscopic images (95% LoA=-0.18 to 0.11) with agreement better for eyes with vCDR>0.8 (LoA=-0.12 to 0.08). The new AI software vCDR measurements had an excellent agreement and correlation with the SD-OCT and manual grading. The ability of the Medios AI to work offline, without requiring cloud-based inferencing, is an added advantage.

Shroff S ，Rao DP ，Savoy FM ，Shruthi S ，Hsu CK ，Pradhan ZS ，Jayasree PV ，Sivaraman A ，Sengupta S ，Shetty R ，Rao HL ... -  《-》

Diagnostic Accuracy of Community-Based Diabetic Retinopathy Screening With an Offline Artificial Intelligence System on a Smartphone.

Natarajan S ，Jain A ，Krishnan R ，Rogye A ，Sivaprasad S ... -  《-》

Medios- An offline, smartphone-based artificial intelligence algorithm for the diagnosis of diabetic retinopathy.

Sosale B ，Sosale AR ，Murthy H ，Sengupta S ，Naveenam M ... -  《-》

Evaluation of an offline, artificial intelligence system for referable glaucoma screening using a smartphone-based fundus camera: a prospective study.

Rao DP ，Shroff S ，Savoy FM ，S S ，Hsu CK ，Negiloni K ，Pradhan ZS ，P V J ，Sivaraman A ，Rao HL ... -  《-》