Periocular Measurement AI Platform (HRNetV2)
Automated calculation of periocular measurements (MRD1, MRD2, PFH, IICD, OICD, IPD, HPA) from standardized facial photographs
▸ More attributes
Brief overview
This study developed an HRNet-v2 deep learning facial landmark detection network to automate periocular anthropometric measurements from standardized facial photographs, including MRD1, MRD2, palpebral fissure height, interpupillary distance, and canthal distances. Across 958 eyes from 479 prospectively enrolled patients at a single Australian center, the model achieved mean absolute errors below 1 mm for all measurements and excellent agreement with human graders (ICC >0.90 for most metrics). The system used manually annotated periocular landmarks and heatmap-based landmark localization to derive linear measurements. No external validation, public code, pretrained weights, or clinical deployment were reported. The model was evaluated under standardized imaging conditions and remains internally validated for research use.
MAE (mm): MRD1 0.29, MRD2 0.30, PFH 0.31, HPA 0.74, OICD 0.73, IICD 0.88, IPD 0.22.
MAE (mm): MRD1 0.29, MRD2 0.30, PFH 0.31, HPA 0.74, OICD 0.73, IICD 0.88, IPD 0.22. ICC: 0.90–0.99 across metrics; landmark error rate 0.51% with no failures.
| Study design | Diagnostic accuracy study |
|---|---|
| Center type | Single-center |
| Patients | 479 |
| Images / eyes | 958 eyes |
| Split | 70/30 |
| Reporting frameworks | None stated |
| Age range | Mean 59 |
|---|---|
| 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_rana_2024 |
|---|---|
| Full author list | Beecher, M., Caltabiano, C., Macri, C., Zhao, Y., Verjans, J., & Selva, D. |
| Journal | Eur J Ophthalmol |
| DOI | 10.1177/11206721241249773 |
| Conditions | Periorbital measurement |
Model internals ▸
| Architecture family | CNN |
|---|---|
| Architecture detail | HRNet-v2 landmark detection model. |
Disclosures ▸
| Funding | Not reported |
|---|---|
| Conflicts of interest | No conflicts |
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)