Post-Blepharoplasty Age Prediction AI (FaceAge)
Assessment of postoperative rejuvenation using AI-estimated facial age
▸ More attributes
Brief overview
This study aggregates four commercial facial age-estimation CNN APIs — Amazon Rekognition, Microsoft Azure Face, Face++, and Inferdo — to quantify perceived age reduction after lower blepharoplasty from standard facial photographs. In a single-centre retrospective cohort of 150 Asian patients, the platform estimated a mean postoperative rejuvenation effect of roughly 2 years, with Face++ showing the strongest standalone accuracy (ICC 0.78; MAE 4.8 years). The software was implemented as a Python/Qt graphical interface and tested only on pre/postoperative images acquired at one institution. No code repository, pretrained weights, external validation cohort, or clinical deployment workflow were reported.
Best individual model: Face++ (ICC 0.78; MAE 4.82 yrs).
Best individual model: Face++ (ICC 0.78; MAE 4.82 yrs). Ensemble (average of four): ICC 0.71; MAE 5.94 yrs. Mean perceived age reduction across all patients: −1.68 years (p < .0001). Men showed greater reduction than women; combined upper+lower procedures produced the largest effect.
| Study design | Retrospective cohort |
|---|---|
| Center type | Single-center |
| Patients | 150 |
| Images / eyes | Not reported |
| Split | Not reported |
| Reporting frameworks | None stated |
| Age range | >18 |
|---|---|
| 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_chiou_2024 |
|---|---|
| Full author list | Yen, C. I., Hsiao, Y. C., & Chen, H. C. |
| Journal | Aesthet Surg J |
| DOI | 10.1093/asj/sjae182 |
| Conditions | Blepharoptosis |
Model internals ▸
| Architecture family | CNN |
|---|---|
| Architecture detail | Four commercial age-estimation CNN APIs, wrapped in a custom application (“FaceAge”). |
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)