import_pentacam: Open-Source OCR Recovery of Corneal Tomography Measurements From Pentacam DICOM, PDF, and Image Archives
- 1Lumael Eye Medical Institute Ltd., Ashkelon, Israel
- 2Schepens Eye Research Institute of Mass. Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
Abstract
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.
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BibTeX
@article{kiroshka2026importpentacam,
title = {{import_pentacam: Open-Source OCR Recovery of Corneal Tomography Measurements From Pentacam DICOM, PDF, and Image Archives}},
author = {Katerina Kiroshka and Tobias Elze},
journal = {Translational Vision Science \& Technology},
year = {2026},
month = oct,
doi = {10.1167/tvst.15.10.46},
url = {https://doi.org/10.1167/tvst.15.10.46},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - import_pentacam: Open-Source OCR Recovery of Corneal Tomography Measurements From Pentacam DICOM, PDF, and Image Archives
AU - Katerina Kiroshka
AU - Tobias Elze
PY - 2026
DA - 2026/10/27
JO - Translational Vision Science & Technology
DO - 10.1167/tvst.15.10.46
UR - https://doi.org/10.1167/tvst.15.10.46
AB - 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.
SN - 2164-2591
ER -