This is an observational cross sectional study aimed to evaluate the performance of the artificial intelligence algorithm in detecting any grade of diabetic retinopathy using retinal images from patients with diabetes.

Start Date11 Jul 2018

Sponsor / Collaborator

Medios Technologies Pte Ltd.

[+1]

CTRI/2018/06/014665 / Not yet recruitingNot Applicable

Simple, Mobile-based Artificial Intelligence AlgoRithms in the detection of Diabetic ReTinopathy (SMART) Study - SMART

Start Date02 Jul 2018

Sponsor / Collaborator

Medios Technologies Pte Ltd.

100 Clinical Results associated with Medios Technologies Pte Ltd.

0 Patents (Medical) associated with Medios Technologies Pte Ltd.

Literatures (Medical) associated with Medios Technologies Pte Ltd.

01 Aug 2024·Indian Journal of Ophthalmology

Evaluation of an AI algorithm trained on an ethnically diverse dataset to screen a previously unseen population for diabetic retinopathy

Article

Author: Yeghiazaryan, Nune ; Dutt, Sreetama ; Sivaraman, Anand ; Rao, Divya P ; Jrbashyan, Nairuhi ; Hambardzumyan, Narine ; Shahsuvaryan, Marianne ; Das, Taraprasad ; Savoy, Florian M

Purpose:This study aimed to determine the generalizability of an artificial intelligence (AI) algorithm trained on an ethnically diverse dataset to screen for referable diabetic retinopathy (RDR) in the Armenian population unseen during AI development.Methods:This study comprised 550 patients with diabetes mellitus visiting the polyclinics of Armenia over 10 months requiring diabetic retinopathy (DR) screening. The Medios AI-DR algorithm was developed using a robust, diverse, ethnically balanced dataset with no inherent bias and deployed offline on a smartphone-based fundus camera. The algorithm here analyzed the retinal images captured using the target device for the presence of RDR (i.e., moderate non-proliferative diabetic retinopathy (NPDR) and/or clinically significant diabetic macular edema (CSDME) or more severe disease) and sight-threatening DR (STDR, i.e., severe NPDR and/or CSDME or more severe disease). The results compared the AI output to a consensus or majority image grading of three expert graders according to the International Clinical Diabetic Retinopathy severity scale.Results:On 478 subjects included in the analysis, the algorithm achieved a high classification sensitivity of 95.30% (95% CI: 91.9%–98.7%) and a specificity of 83.89% (95% CI: 79.9%–87.9%) for the detection of RDR. The sensitivity for STDR detection was 100%.Conclusion:The study proved that Medios AI-DR algorithm yields good accuracy in screening for RDR in the Armenian population. In our literature search, this is the only smartphone-based, offline AI model validated in different populations.

01 Apr 2024·Eye

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

Article

Author: Pradhan, Zia Sultan ; Savoy, Florian M ; Rao, Divya Parthasarathy ; S, Shruthi ; Hsu, Chao-Kai ; P V, Jayasree ; Shroff, Sujani ; Rao, Harsha L ; Sivaraman, Anand ; Negiloni, Kalpa

AbstractBackground/ObjectivesAn affordable and scalable screening model is critical for undetected glaucoma. The study evaluated the performance of an offline, smartphone-based AI system for the detection of referable glaucoma against two benchmarks: specialist diagnosis following full glaucoma workup and consensus image grading.Subjects/MethodsThis prospective study (tertiary glaucoma centre, India) included 243 subjects with varying severity of glaucoma and control group without glaucoma. Disc-centred images were captured using a validated smartphone-based fundus camera analysed by the AI system and graded by specialists. Diagnostic ability of the AI in detecting referable Glaucoma (Confirmed glaucoma) and no referable Glaucoma (Suspects and No glaucoma) when compared to a final diagnosis (comprehensive glaucoma workup) and majority grading (image grading) by Glaucoma specialists (pre-defined criteria) were evaluated.ResultsThe AI system demonstrated a sensitivity and specificity of 93.7% (95% CI: 87.6–96.9%) and 85.6% (95% CI:78.6–90.6%), respectively, in the detection of referable glaucoma when compared against final diagnosis following full glaucoma workup. True negative rate in definite non-glaucoma cases was 94.7% (95% CI: 87.2–97.9%). Amongst the false negatives were 4 early and 3 moderate glaucoma. When the same set of images provided to the AI was also provided to the specialists for image grading, specialists detected 60% (67/111) of true glaucoma cases versus a detection rate of 94% (104/111) by the AI.ConclusionThe AI tool showed robust performance when compared against a stringent benchmark. It had modest over-referral of normal subjects despite being challenged with fundus images alone. The next step involves a population-level assessment.

100 Deals associated with Medios Technologies Pte Ltd.

100 Translational Medicine associated with Medios Technologies Pte Ltd.

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