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Machine teaching, a machine learning subfield, may allow for rapid development of artificial intelligence systems able to automatically identify emerging ocular biomarkers from small imaging datasets. We sought to use machine teaching to automatically identify retinal ischemic perivascular lesions (RIPLs) and subretinal drusenoid deposits (SDDs), two emerging ocular biomarkers of cardiovascular disease. IRB approval was obtained. Four small datasets of SD-OCT B-scans were used to train and test two distinct automated systems, one identifying RIPLs and the other identifying SDDs. An open-source interactive machine-learning software program, RootPainter, was used to perform annotation and training simultaneously over a 6-hour period. For SDDs at the B-scan level, test-set accuracy = 92%, sensitivity = 100%, specificity = 88%, positive predictive value (PPV) = 82%, and negative predictive value (NPV) = 100%. For RIPLs at the B-scan level, test-set accuracy = 90%, sensitivity = 60%, specificity = 93%, PPV = 50%, and NPV = 95%. Machine teaching demonstrates promise within ophthalmic imaging to rapidly allow for automated identification of novel biomarkers from small image datasets. [Ophthalmic Surg Lasers Imaging Retina 2024;55:475–478.] [ABSTRACT FROM AUTHOR]

Context.--The pathogenesis of primary ocular adnexal marginal zone lymphoma (POAMZL) remains unclear. The reported associations with Chlamydia psittaci infection and MYD88 mutations are highly variable. Objective.--To examine MYD88 L265P mutation in ocular marginal zone lymphomas and correlate with clinicopathologic features and Chlamydia infection. Design.--Presence of MYD88 L265P mutation and Chlamydia infection in lymphoma was analyzed by using sensitive polymerase chain reaction (PCR) methods. Results.--The MYD88 L265P mutation was identified in 8 of 22 POAMZLs (36%), including 2 of 3 cases in which PCR failed to detect clonal IGH gene rearrangement; none of the 4 secondary marginal zone lymphomas were positive. Test results for Chlamydia were negative in all cases. Patients with and without the MYD88 mutation had similar clinicopathologic features. Conclusions.--The MYD88 mutational analysis provides important information in diagnostic workup of POAMZL. The frequent MYD88 mutation suggests a critical role of this aberration in the pathogenesis of POAMZL and may serve as a therapeutic target for patients with progressive disease. (Arch Pathol Lab Med. 2019;143:483-493; doi: 10.5858/arpa.2018-0092-OA), Lymphomas of ocular adnexa comprise 8% of extranodal lymphomas. (1) Primary ocular adnexal marginal zone lymphoma (POAMZL) is the most common orbital tumor, comprising more than half of all orbital [...]

To investigate prevalence of diabetes (DM), diabetic retinopathy (DR), and areas with highest rates of undetected DR. To quantify and map locations of disparities as they relate to poverty and minority populations. Retrospective cohort study from large regional health data repository (HealthLNK). Geographic Information System (GIS) analysis mapped rates of DM and DR in Chicago area ZIP Codes. Of 1,086,921 adults who met the inclusion criteria, 143,790 with DM were identified. ZIP Codes with higher poverty rates were correlated with higher prevalence of DM and DR (Pearson's correlation coefficient 0.614, p <.05, 0.333, p <.05). Poverty was negatively correlated with likelihood of DR diagnosis (−0.638, p <.05). Relative risks of DM and DR were calculated in each ZIP Code and compared to actual rates. 36 high-risk ZIP Codes had both high-risk of DM and low DR detection. In high-risk ZIP Codes 85.4% of households self-identified as ethnic minority and 33.0% were below the Federal Poverty Level (FPL). Both percentages were significantly higher than the Chicago average of 50.5% minority and 19.9% below FPL (p <.05). 67 ideal ZIP Codes had both low risk of DM and high DR detection. In ideal ZIP Codes 32.6% of households self-identified as minority, and 10.2% were below the FPL (p <.05). A health care disparity exists with regards to DM and DR. High-risk ZIP Codes are associated with higher poverty and higher minority population, and they are highly concentrated in just 17% of the ZIP codes in the Chicago area. [ABSTRACT FROM AUTHOR]

Purpose:Diabetic retinopathy (DR) is a microvascular complication of diabetes mellitus (DM). Standard of care for patients with DM is an annual eye examination or retinal imaging to assess for DR, the latter of which may be completed through telemedicine approaches. One significant issue is poor-quality images that prevent adequate screening and are thus ungradable. We used artificial intelligence to enable point-of-care (at time of imaging) identification of ungradable images in a DR screening program. Methods:Nonmydriatic retinal images were gathered from patients with DM imaged during a primary care or endocrinology visit from September 1, 2017, to June 1, 2021. The Topcon TRC-NW400 retinal camera (Topcon Corp., Tokyo, Japan) was used. Images were interpreted by 5 ophthalmologists for gradeability, presence and stage of DR, and presence of non-DR pathologies. A convolutional neural network with Inception V3 network architecture was trained to assess image gradeability. Images were divided into training and test sets, and 10-fold cross-validation was performed. Results:A total of 1,377 images from 537 patients (56.1% female, median age 58) were analyzed. Ophthalmologists classified 25.9% of images as ungradable. Of gradable images, 18.6% had DR of varying degrees and 26.5% had non-DR pathology. 10 fold cross-validation produced an average area under receiver operating characteristic curve (AUC) of 0.922 (standard deviation: 0.027, range: 0.882 to 0.961). The final model exhibited similar test set performance with an AUC of 0.924. Conclusions:This model accurately assesses gradeability of nonmydriatic retinal images. It could be used for increasing the efficiency of DR screening programs by enabling point-of-care identification of poor-quality images. [ABSTRACT FROM AUTHOR]