Purpose

To describe a case of bilateral retinal pigmentary changes in the setting of immune checkpoint inhibitor therapy (ICIT).

Observations

A 69-year-old man with a history of advanced cutaneous melanoma was started on combination ICIT with nivolumab and ipilimumab and stereotactic body radiation therapy. Soon after, he developed photopsias and nyctalopia with findings of discrete retinal pigmentary changes bilaterally. Initial visual acuities were 20/20 and 20/30 in the right and left eye, respectively. Multi-modal imaging revealed sub-retinal deposits with progressive changes in pigmentation and autofluorescence, associated with decreased peripheral fields on formal perimetry. A full-field electroretinogram revealed attenuated and delayed a- and b-waves. Positive serum retinal autoantibodies were identified. The patient developed left-sided optic nerve edema and center-involving cystoid macular edema which improved after treatment with sub-tenon's triamcinolone.

Conclusions

The use of ICIT has greatly expanded in oncologic practice with subsequent increases in immune related adverse events that pose significant systemic and ophthalmologic morbidities. We propose that the new retinal pigmentary changes seen in this case are the sequelae of an autoimmune inflammatory response against pigmented cells. This adds to the rare side effects that may occur after ICIT.

Purpose

Optical coherence tomography (OCT) is widely used in the management of retinal pathologies, including age-related macular degeneration (AMD), diabetic macular edema (DME), and primary open-angle glaucoma (POAG). We used machine learning techniques to understand diagnostic performance gains from expanding macular OCT B-scans compared with foveal-only OCT B-scans for these conditions.

Methods

Electronic medical records were extracted to obtain 61 B-scans per eye from patients with AMD, diabetic retinopathy, or POAG. We constructed deep neural networks and random forest ensembles and generated area under the receiver operating characteristic (AUROC) and area under the precision recall (AUPR) curves.

Results

After extracting 630,000 OCT images, we achieved improved AUROC and AUPR curves when comparing the central image (one B-scan) to all images (61 B-scans). The AUROC and AUPR points of diminishing return for diagnostic accuracy for macular OCT coverage were found to be within 2.75 to 4.00 mm (14-19 B-scans), 4.25 to 4.50 mm (20-21 B-scans), and 4.50 to 6.25 mm (21-28 B-scans) for AMD, DME, and POAG, respectively. All models with >0.25 mm of coverage had statistically significantly improved AUROC/AUPR curves for all diseases (P < 0.05).

Conclusions

Systematically expanded macular coverage models demonstrated significant differences in total macular coverage required for improved diagnostic accuracy, with the largest macular area being relevant in POAG followed by DME and then AMD. These findings support our hypothesis that the extent of macular coverage by OCT imaging in the clinical setting, for any of the three major disorders, has a measurable impact on the functionality of artificial intelligence decision support.

Translational relevance

We used machine learning techniques to improve OCT imaging standards for common retinal disease diagnoses.

Purpose

This study aimed to compare a new Artificial Intelligence (AI) method to conventional mathematical warping in accurately overlaying peripheral retinal vessels from two different imaging devices: confocal scanning laser ophthalmoscope (cSLO) wide-field images and SLO ultra-wide field images.

Methods

Images were captured using the Heidelberg Spectralis 55-degree field-of-view and Optos ultra-wide field. The conventional mathematical warping was performed using Random Sample Consensus-Sample and Consensus sets (RANSAC-SC). This was compared to an AI alignment algorithm based on a one-way forward registration procedure consisting of full Convolutional Neural Networks (CNNs) with Outlier Rejection (OR CNN), as well as an iterative 3D camera pose optimization process (OR CNN + Distortion Correction [DC]). Images were provided in a checkerboard pattern, and peripheral vessels were graded in four quadrants based on alignment to the adjacent box.

Results

A total of 660 boxes were analysed from 55 eyes. Dice scores were compared between the three methods (RANSAC-SC/OR CNN/OR CNN + DC): 0.3341/0.4665/4784 for fold 1-2 and 0.3315/0.4494/4596 for fold 2-1 in composite images. The images composed using the OR CNN + DC have a median rating of 4 (out of 5) versus 2 using RANSAC-SC. The odds of getting a higher grading level are 4.8 times higher using our OR CNN + DC than RANSAC-SC (p < 0.0001).

Conclusion

Peripheral retinal vessel alignment performed better using our AI algorithm than RANSAC-SC. This may help improve co-localizing retinal anatomy and pathology with our algorithm.