Selecting high-throughput scanners for clinical use: A multicenter institution experience
Bibliographic record
Abstract
OBJECTIVE: To evaluate and implement whole-slide imaging (WSI) scanners for a fully digital pathology workflow at the University Health Network (UHN) in Canada, a multicenter institution. The goal was to optimize clinical diagnosis, education, telepathology, consultation, and artificial intelligence (AI) applications. Given the competitive digital pathology market, a thorough assessment was conducted to select the most suitable scanners for UHN's primary diagnosis at main and satellite sites, remote teleconsultation, intraoperative consultation, and multidisciplinary education. METHODS: A request for proposal was issued to evaluate WSI scanners based on technical specifications, compatibility, previous performance, implementation strategy, operational excellence, and postinstallation support. A multidisciplinary committee scored vendors, and the highest-scoring scanners were further assessed for image accuracy, loading and offloading efficiency, scanning speed, throughput, and artifact handling. RESULTS: The UHN selected a fleet of WSI scanners with varying functionality from multiple vendors. Successful implementation included seamless integration with the image management system, laboratory information system, hospital information system, and digital storage. This transition enhanced workflow efficiency, streamlined telepathology services, and supported AI-driven applications. CONCLUSIONS: Despite high costs, WSI scanners substantially improved slide accessibility, reduced turnaround times, and enhanced workflow flexibility. Their integration supports AI advancements, facilitates second opinions, improves access to educational materials, and facilitates proficiency testing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".