On the construction of a large-scale database of AI-assisted annotating lung ventilation-perfusion scintigraphy for pulmonary embolism (VQ4PEDB)
Bibliographic record
Abstract
Introduction: Ventilation-perfusion (V/Q) nuclear scintigraphy remains a vital diagnostic tool for assessing pulmonary embolism (PE) and other lung conditions. Interpretation of these images requires specific expertise which may benefit from recent advances in artificial intelligence (AI) to improve diagnostic accuracy and confidence in reporting. Our study aims to develop a multi-center dataset combining imaging and clinical reports to aid in creating AI models for PE diagnosis. Methods: We established a comprehensive imaging registry encompassing patient-level V/Q image data along with relevant clinical reports, CTPA images, DVT ultrasound impressions, D-dimer lab tests, and thrombosis unit records. Data extraction was performed at two hospitals in Canada and at multiple sites in the United States, followed by a rigorous de-identification process. We utilized the V7 Darwin platform for crowdsourced annotation of V/Q images including segmentation of V/Q mismatched vascular defects. The annotated data was then ingested into Deep Lake, a SQL-based database, for AI model training. Quality assurance involved manual inspections and algorithmic validation. Results: A query of The Ottawa Hospital's data warehouse followed by initial data screening yielded 2,137 V/Q studies with 2,238 successfully retrieved as DICOM studies. Additional contributions included 600 studies from University Health Toronto, and 385 studies by private company Segmed Inc. resulting in a total of 3,122 V/Q planar and SPECT images. The majority of studies were acquired using Siemens, Philips, and GE scanners, adhering to standardized local imaging protocols. After annotating 1,500 studies from The Ottawa Hospital, the analysis identified 138 high-probability, 168 intermediate-probability, 266 low-probability, 244 very low-probability, and 669 normal, and 15 normal perfusion with reversed mismatched ventilation defect studies. In 1,500 patients were 3,511 segmented vascular perfusion defects. Conclusion: The VQ4PEDB comprised 8 unique ventilation agents and 11 unique scanners. The VQ4PEDB database is unique in its depth and breadth in the domain of V/Q nuclear scintigraphy for PE, comprising clinical reports, imaging studies, and annotations. We share our experience in addressing challenges associated with data retrieval, de-identification, and annotation. VQ4PEDB will be a valuable resource to development and validate AI models for diagnosing PE and other pulmonary diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".