import_pentacam : Open-Source OCR Recovery of Corneal Tomography Measurements From Pentacam DICOM, PDF, and Image Archives
Purpose: Pentacam corneal tomography data accumulate in clinical Digital Imaging and Communications in Medicine (DICOM) archives as embedded portable document format (PDF) printouts and proprietary binary readouts rather than structured records; the device's comma-separated values (CSV) export is impractical for retrospective multisite use. We describe an open-source pipeline that recovers structured Pentacam measurements from archives. Methods: import_pentacam is an open-source Rust tool that extracts up to 66 numerical fields and embedded corneal-map images per examination from DICOM, PDF, or image inputs, with single-file, file-list, and recursive picture archiving and communication system (PACS) directory modes supporting continuous incremental updates. Cross-firmware layout variation is normalized by affine transforms onto printout templates. The pipeline was developed on 300 examinations across three printout types and evaluated on a 200-examination holdout; Belin/Ambrósio Ectasia was added as a new printout type without retraining and tested on 333 examinations. Ground truth was derived from automated annotators with manual review of disagreements. Results: Field-level accuracy was 99.46% on training, 98.70% on the holdout, and 99.03% on the Belin/Ambrósio corpus. Agreement with paired DICOM Structured Reports was 100% on 518 fields. Institutional deployment recovered 126,184 eye visits from 29,550 patients across an 18-year archive. Conclusions: Automated recovery of structured Pentacam data from clinical DICOM archives is feasible at large multisite institutional scale. The software supports continuous incremental updates and is deployable at other Pentacam-equipped institutions for longitudinal ectasia research. Translational Relevance: Routinely collected institutional Pentacam examinations are a substantial resource for longitudinal corneal tomography research. This pipeline converts that resource into structured data without disrupting clinical workflows, enabling continuously updated retrospective cohorts and federated extraction across institutions.
Authors
- Tobias Elze (ORCID: https://orcid.org/0000-0002-2032-0496)
- Katerina Kiroshka
Institutions
- Massachusetts Eye and Ear Infirmary (US)
- Harvard University (US)
- Barzilai Medical Center (IL)
Publication Details
- Journal
- Translational Vision Science & Technology
- Published
- 2026-10-27
- DOI
- https://doi.org/10.1167/tvst.15.10.46
- Primary Topic
- Electronic Health Records Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.00