A novel eyelid motion monitor

Videographic analysis of blink dynamics in patients with chronic progressive external ophthalmoplegia, myogenic ptosis, and facial nerve palsy using smartphone camera: A comparative analysis

Background and Aims

Congenital myogenic ptosis (CMP), chronic progressive external ophthalmoplegia (CPEO), and facial nerve palsy (FNP) are among the disorders which can seriously affect the blink dynamics of patients. Smartphone videography is a simple, convenient, and inexpensive way to capture eyelid movement. This study has measured and compared a variety of blink dynamics in these patients compared to healthy controls using 2-dimensional smartphone videography to enhance the utility of this method in both clinical and research settings.

Methods

A total of 30 adult participants with a complaint of impaired eyelid movements including 10 with CMP, 10 with CPEO, and 10 with unilateral FNP, as well as 10 healthy controls were recruited. Using a smartphone camera with a resolution of 240 frames per second in 720 p, various blink dynamics were measured.

Results

All case groups had significantly lower values of peak and average closing velocities, average opening velocity, and palpebral aperture and significantly higher values of eyelid closing duration, compared to controls. FNP participants also had significantly lower values in the full blink rate and peak opening velocity (POV) measures, and CPEO patients showed significantly lower values in the POV. Other measures were not statistically significantly different compared to healthy controls.

Conclusion

Our results indicated that all patients with CMP, FNP, and CPEO had different blinking dynamics compared to healthy controls, which is consistent with previous studies. Smartphone videography has achieved sufficient resolution and frame-rate to provide valuable information and anatomic details for clinical and research purposes. Further studies could utilize smartphone videography for further investigation and confirmation of the methodology in various conditions.

PeriorbitAI: Artificial Intelligence Automation of Eyelid and Periorbital Measurements

PURPOSE

To develop a deep learning semantic segmentation network to automate the assessment of 8 periorbital measurements DESIGN: Development and validation of an artificial intelligence (AI) segmentation algorithm METHODS: A total of 418 photographs of periorbital areas were used to train a deep learning semantic segmentation model to segment iris, aperture, and brow areas. These data were used to develop a post-processing algorithm that measured margin reflex distance (MRD) 1 and 2, medial canthal height (MCH), lateral canthal height (LCH), medial brow height (MBH), lateral brow height (LBH), medial intercanthal distance (MID), and lateral intercanthal distance (LID). The algorithm validity was evaluated on a prospective hold-out test set against 3 graders. The main outcome measures were dice coefficient, mean absolute difference, intraclass correlation coefficient, and Bland-Altman analysis. A smartphone video was also segmented and evaluated as proof of concept.

RESULTS

The AI algorithm performed in close agreement with all human graders, with a mean absolute difference of 0.5 mm for MRD1, MRD2, LCH, and MCH. The mean absolute difference between graders is approximately 1.5-2 mm for LBH and MBH and approximately 2-4 mm for MID and LID. The 95% confidence intervals for all graders overlapped in most cases, demonstrating that the algorithm performs similarly to human graders. The segmentation of a smartphone video demonstrated that MRD1 can be dynamically measured.

CONCLUSIONS

We present, to our knowledge, the first open-sourced, artificial intelligence system capable of automating static and dynamic periorbital measurements. A fully automated tool stands to transform the delivery of clinical care and quantification of surgical outcomes.