A comparison of surgical outcomes between pre-and full-term patients with exotropia

Clinical profile of childhood exotropia in a tertiary eye care center in North India

PURPOSE

To identify different types of exotropia in children less than 16 years and their clinical presentation. The secondary objective is to identify the age of onset, age of presentation, birth history, and ocular and systemic associations as well as to evaluate their motor and sensory status.

METHODS

This hospital-based descriptive observational study was conducted from September 2018 to December 2019. Patients with a clinical diagnosis of exotropia of age less than 16 years, and exotropia of ≥ 10 PD were included in the study. Data were collected using a structured proforma. All statistical calculations were performed using Microsoft Excel Office version 2016 and R version 4.0.2.

RESULTS

Two hundred eighty-six (286) consecutive children with exotropia were analyzed. Intermittent exotropia (72%) was the most common form of exotropia, followed by exotropia with a neurological association (11%), infantile (8%), sensory (6%), restrictive (2%), and paralytic (1%) types. Exotropia with neurological association had a significantly earlier age of presentation (median = 42months) as compared to other types (P = 0.039).

CONCLUSION

We recorded a huge amount of cases of exotropia associated with neurological abnormalities and this group of patients had significantly more number of preterm and low birth weight children. This study has its limitations as it is not a population-based study and prevalence rates could not be calculated.

An artificial intelligence platform for the diagnosis and surgical planning of strabismus using corneal light-reflection photos

Background

Strabismus affects approximately 0.8–6.8% of the world’s population and can lead to abnormal visual function. However, Strabismus screening and measurement are laborious and require professional training. This study aimed to develop an artificial intelligence (AI) platform based on corneal light-reflection photos for the diagnosis of strabismus and to provide preoperative advice.

Methods

An AI platform consisting of three deep learning (DL) systems for strabismus diagnosis, angle evaluation, and operation plannings based on corneal light-reflection photos was trained and retrospectively validated using a retrospective development data set obtained between Jan 1, 2014, and Dec 31, 2018. Corneal light-reflection photos were collected to train the DL systems for strabismus screening and deviation evaluations in the horizontal strabismus while concatenated images (each composed of two photos representing different gaze states) were procured to train the DL system for operative advice regarding exotropia. The AI platform was further prospectively validated using a prospective development data set captured between Sep 1, 2019, and Jun 10, 2020.

Results

In total, 5,797 and 571 photos were included in the retrospective and prospectively development data sets, respectively. In the retrospective test sets, the screening system detected strabismus with a sensitivity of 99.1% [95% confidence interval (95% CI), 98.1–99.7%], a specificity of 98.3% (95% CI, 94.6–99.5%), and an AUC of 0.998 (95% CI, 0.993–1.000, P<0.001). Compared to the angle measured by the perimeter arc, the deviation evaluation system achieved a level of accuracy of ±6.6º (95% LoA) with a small bias of 1.0º. Compared to the real design, the operation advice system provided advice regarding the target angle within ±5.5º (95% LoA). Regarding strabismus in the prospective test set, the AUC was 0.980. The platform achieved a level of accuracy of ±7.0º (95% LoA) in the deviation evaluation and ±6.1º (95% LoA) in the target angle suggestion.

Conclusions

The AI platform based on corneal light-reflection photos can provide reliable references for strabismus diagnosis, angle evaluation, and surgical plannings.