Cornea Exposure Rate AI System (AnigmaView)
Automated measurement of the Corneal Exposure Ratio (CER) from standardized photographs
▸ More attributes
Brief overview
This study validated an AI-driven CER measurement tool for assessing ptosis surgery outcomes, showing excellent agreement with manual measurements and perfect repeatability. Generalizability is limited by the single-center, ethnically homogenous cohort and reliance on proprietary software.
AI vs manual ImageJ: ICC 0.98–0.99 (excellent).
AI vs manual ImageJ: ICC 0.98–0.99 (excellent). Repeatability ICC = 1.00. Pre-to-post CER changes matched manual results (≈20–28% improvement depending on severity subgroup).
| Study design | Prospective cohort |
|---|---|
| Center type | Single-center |
| Patients | 50 |
| Images / eyes | 100 eyes |
| Split | Not reported |
| Reporting frameworks | None stated |
| Age range | >19 |
|---|---|
| Sex (% female) | Not reported |
| Race / ethnicity reported | N |
| Subgroup performance | N |
- Concept
- Prototype
- Internal val.
- External val.
- Regulator
- Clinical
▸ Why this score?
The APPRAISE-AI tool grades AI/ML medical studies across 24 items in 6 subdomains, scored independently by two reviewers. The total reflects the methodological transparency of the publication — not the underlying clinical value of the model.
How to read this: high scores in clinical relevance and reporting with low scores in methodological conduct, robustness, or reproducibility is the dominant pattern in ophthalmic AI today.
Quality bands: Low <40 · Moderate 40–59 · High 60–79 · Very High 80+. Most published ophthalmic AI sits in the Low–Moderate band; studies that break into High typically combine multi-center datasets, prospective evaluation, and open-source release.
Bibliographic record ▸
| Model ID | oculoplastics_park_2025 |
|---|---|
| Full author list | Yang, H., Cho, K., Park, J., Kim, S., Seo, M., Shin, Y., Im, G., Kim, M., Song, S. H., & Seo, C. W. |
| Journal | J Clin Med |
| DOI | 10.3390/jcm14051691 |
| Conditions | Ptosis, blepharoptosis |
Model internals ▸
| Architecture family | CNN |
|---|---|
| Architecture detail | DeepLabV3+–based segmentation pipeline integrated into the Anigma-view v1.0.6 software |
Disclosures ▸
| Funding | K-Health National Medical AI Service and Establishing Industrial Ecosystem Project by the Special Account for Regional Balanced Development of the Ministry of Science and ICT and the Daejeon Metropolitan City, 2024 (H0503-24-1001) |
|---|---|
| Conflicts of interest | Authors Chang-Wook Seo, Kyungmin Cho, JungJin Park, Seonghyeon Kim, Myunggyun Seo, Yerim Shin, Gyeonghun Im, and Minju Kim are employees of Anigma Technologies Inc., Daejeon, Republic of Korea. Author Seung-Han Song serves as an outside director of Anigma Technologies Inc. |
Status, version, reviewers ▸
| Card status | Published |
|---|---|
| Card version | v1.0.0 |
| First added | 2026-04-28 |
| Last reviewed | 2026-04-28 |
| Reviewers | attribution opt-in pending |
Card changelog ▸
- 2026-04-28 initial publication (v1.0.0)