Proof-of-Concept Analysis of a Deep Learning Model to Conduct Automated Segmentation of OCT Images for Macular Hole Volume.
| Autor: | Pereira A, Oakley JD, Sodhi SK, Russakoff DB, Choudhry N |
|---|---|
| Jazyk: | angličtina |
| Zdroj: | Ophthalmic surgery, lasers & imaging retina [Ophthalmic Surg Lasers Imaging Retina] 2022 Apr; Vol. 53 (4), pp. 208-214. Date of Electronic Publication: 2022 Apr 01. |
| DOI: | 10.3928/23258160-20220315-02 |
| Abstrakt: | Background and Objective: To determine whether an automated artificial intelligence (AI) model could assess macular hole (MH) volume on swept-source optical coherence tomography (OCT) images. Patients and Methods: This was a proof-of-concept consecutive case series. Patients with an idiopathic full-thickness MH undergoing pars plana vitrectomy surgery with 1 year of follow-up were considered for inclusion. MHs were manually graded by a vitreoretinal surgeon from preoperative OCT images to delineate MH volume. This information was used to train a fully three-dimensional convolutional neural network for automatic segmentation. The main outcome was the correlation of manual MH volume to automated volume segmentation. Results: The correlation between manual and automated MH volume was R 2 = 0.94 ( n = 24). Automated MH volume demonstrated a higher correlation to change in visual acuity from preoperative to the postoperative 1-year time point compared with the minimum linear diameter (volume: R 2 = 0.53; minimum linear diameter: R 2 = 0.39). Conclusion: MH automated volume segmentation on OCT imaging demonstrated high correlation to manual MH volume measurements. [ Ophthalmic Surg Lasers Imaging Retina . 2022;53(4):208-214.] . |
| Databáze: | MEDLINE |
| Externí odkaz: |
|