Smartphone-based Snellen visual acuity charts has become popularized; however, their accuracy has not been established. This study aimed to evaluate the equivalence of a smartphone-based visual acuity chart with a standard 6-m Snellen visual acuity (6SVA) chart. First, a review of available Snellen chart applications on iPhone was performed to determine the most accurate application based on optotype size. Subsequently, a prospective comparative study was performed by measuring conventional 6SVA and then iPhone visual acuity using the ‘Snellen’ application on an Apple iPhone 4. Eleven applications were identified, with accuracy of optotype size ranging from 4.4–39.9%. Eighty-eight patients from general medical and surgical wards in a tertiary hospital took part in the second part of the study. The mean difference in logMAR visual acuity between the two charts was 0.02 logMAR (95% limit of agreement −0.332, 0.372 logMAR). The largest mean difference in logMAR acuity was noted in the subgroup of patients with 6SVA worse than 6/18 (n=5), who had a mean difference of two Snellen visual acuity lines between the charts (0.276 logMAR). We did not identify a Snellen visual acuity app at the time of study, which could predict a patients standard Snellen visual acuity within one line. There was considerable variability in the optotype accuracy of apps. Further validation is required for assessment of acuity in patients with severe vision impairment.

Fungal keratitis (FK) can be challenging to diagnose and treat. In this retrospective case series, FK cases presenting at the University of Pittsburgh Medical Center, Pennsylvania, USA, from 2015 to 2021 were reviewed for ocular risk factors, clinical presentation, management, and outcomes. Twenty-eight cases of FK were included. The median presenting age was 58.5 (18.5) years, and the median symptom duration prior to presentation was 10 (35.8) days. Predisposing ocular risk factors included contact lens use (67.9%), recent ocular trauma/abrasion (42.9%), and history of ocular surgery (42.9%). The median presenting visual acuity (VA) was 1.35 (1.72) LogMAR. About half presented with a central ulcer (42.9%), large infiltrate (6.7 (6.3) mm²), corneal thinning (50.0%), and hypopyon (32.1%). The majority of isolated fungal species were filamentous (75.0%). Most common antifungal medications included topical voriconazole (71.4%) and natamycin (53.6%) drops and oral voriconazole (64.3%). Surgical management was necessary in 32.1% of cases and enucleation in one case. Defect resolution occurred in 42.5 (47.0) days, and median final VA was 0.5 (1.84) LogMAR. Features associated with poor final visual outcomes included poor initial VA (p < 0.001) and larger defect size (p = 0.002). In conclusion, unlike prior studies in the northeast region of the USA, FK was commonly caused by filamentous fungi, and antifungal management most often consisted of topical and oral voriconazole.

While color fundus photos are used in routine clinical practice to diagnose ophthalmic conditions, evidence suggests that ocular imaging contains valuable information regarding the systemic health features of patients. These features can be identified through computer vision techniques including deep learning (DL) artificial intelligence (AI) models. We aim to construct a DL model that can predict systemic features from fundus images and to determine the optimal method of model construction for this task. Data were collected from a cohort of patients undergoing diabetic retinopathy screening between March 2020 and March 2021. Two models were created for each of 12 systemic health features based on the DenseNet201 architecture: one utilizing transfer learning with images from ImageNet and another from 35,126 fundus images. Here, 1277 fundus images were used to train the AI models. Area under the receiver operating characteristics curve (AUROC) scores were used to compare the model performance. Models utilizing the ImageNet transfer learning data were superior to those using retinal images for transfer learning (mean AUROC 0.78 vs. 0.65, p-value < 0.001). Models using ImageNet pretraining were able to predict systemic features including ethnicity (AUROC 0.93), age > 70 (AUROC 0.90), gender (AUROC 0.85), ACE inhibitor (AUROC 0.82), and ARB medication use (AUROC 0.78). We conclude that fundus images contain valuable information about the systemic characteristics of a patient. To optimize DL model performance, we recommend that even domain specific models consider using transfer learning from more generalized image sets to improve accuracy.

Dr Chandrashan Perera MBBS1,2 1Editor-In-Chief, Journal of Mobile Technology in Medicine, 2Austin Hospital, Melbourne, Australia Corresponding Author: editor@journalmtm.com Journal MTM 1:1:1-2, 2012 ...