IEEE Transactions on Pattern Analysis and Machine Intelligence

Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

Computer and Robot Vision

Artificial Intelligence (AI) has long promised to increase healthcare affordability, quality and accessibility but FDA, until recently, had never authorized an autonomous AI diagnostic system. This pivotal trial of an AI system to detect diabetic retinopathy (DR) in people with diabetes enrolled 900 subjects, with no history of DR at primary care clinics, by comparing to Wisconsin Fundus Photograph Reading Center (FPRC) widefield stereoscopic photography and macular Optical Coherence Tomography (OCT), by FPRC certified photographers, and FPRC grading of Early Treatment Diabetic Retinopathy Study Severity Scale (ETDRS) and Diabetic Macular Edema (DME). More than mild DR (mtmDR) was defined as ETDRS level 35 or higher, and/or DME, in at least one eye. AI system operators underwent a standardized training protocol before study start. Median age was 59 years (range, 22-84 years); among participants, 47.5% of participants were male; 16.1% were Hispanic, 83.3% not Hispanic; 28.6% African American and 63.4% were not; 198 (23.8%) had mtmDR. The AI system exceeded all pre-specified superiority endpoints at sensitivity of 87.2% (95% CI, 81.8-91.2%) (>85%), specificity of 90.7% (95% CI, 88.3-92.7%) (>82.5%), and imageability rate of 96.1% (95% CI, 94.6-97.3%), demonstrating AI's ability to bring specialty-level diagnostics to primary care settings. Based on these results, FDA authorized the system for use by health care providers to detect more than mild DR and diabetic macular edema, making it, the first FDA authorized autonomous AI diagnostic system in any field of medicine, with the potential to help prevent vision loss in thousands of people with diabetes annually. ClinicalTrials.gov NCT02963441.

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Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices

https://amcor.asahikawa-med.ac.jp/modules/xoonips/download.php?id=25896459

Optical Coherence Tomography Angiography in Diabetic Retinopathy: A Prospective Pilot Study.

Artificial intelligence in retina.

Retinal image quality assessment using generic image quality indicators

Automated lesion detectors in retinal fundus images

Purpose:To evaluate the predictive value of microaneurysm (MA) formation rate concerning the development of clinically significant macular edema (CSME) in patients with mild-to-moderate nonproliferative diabetic retinopathy as evaluated by an automated analysis of central field fundus 30° photograph

Microaneurysm formation rate as a predictive marker for progression to clinically significant macular edema in nonproliferative diabetic retinopathy.

Aim: To assess a two-step automated system (RetmarkerSR) that analyzes retinal photographs to detect diabetic retinopathy for the purpose of reducing the burden of manual grading. Methods: Anonymous images from 5,386 patients screened in 2007 were obtained from a nonmydriatic diabetic retinopathy screening program in Portugal and graded by an experienced ophthalmologist. RetmarkerSR earmarked microaneurysms, generating two outputs: ‘disease’ or ‘no disease’. A second-step analysis, based on coregistration, combining two visits, was subsequently performed in 289 patients who underwent repeated examinations in 2008. The study was extended by analyzing all referrals considered urgent by the ophthalmologist from 2001 to 2007. Results were compared with those obtained by manual grading. Results: The RetmarkerSR classified in a first-step analysis 2,560 patients (47.5%) as having ‘no disease’ and 2,826 patients (52.5%) as having ‘disease’, thus requiring manual grading. RetmarkerSR detected all eyes considered urgent referrals. The two-step analysis further reduced the number of false-positive results by 26.3%, indicating an overall sensitivity of 95.8% and a specificity of 63.2%. Conclusion: Automated grading of diabetic retinopathy may safely reduce the burden of grading patients in diabetic retinopathy screening programs. The novel two-step automated analysis system offers improved sensitivity and specificity over published automated analysis systems.

Improved automated screening of diabetic retinopathy.

Purpose: To describe the procedures of a nonmydriatic diabetic retinopathy (DR) screening program in the Central Region of Portugal and the added value of the int

Carlos Manta Oliveira

Papers

Retinal image quality assessment using generic image quality indicators

Automated lesion detectors in retinal fundus images

Microaneurysm formation rate as a predictive marker for progression to clinically significant macular edema in nonproliferative diabetic retinopathy.

Improved automated screening of diabetic retinopathy.

Screening for Diabetic Retinopathy in the Central Region of Portugal. Added Value of Automated ‘Disease/No Disease' Grading