Machine learning can appropriately classify the collimation of ventrodorsal and dorsoventral thoracic radiographic images of dogs and cats
Bibliographic record
Abstract
OBJECTIVES: To determine the feasibility of machine learning algorithms for the classification of appropriate collimation of the cranial and caudal borders in ventrodorsal and dorsoventral thoracic radiographs. SAMPLES: 900 ventrodorsal and dorsoventral canine and feline thoracic radiographs were retrospectively acquired from the Picture Archiving and Communication system (PACs) system of the Ontario Veterinary College. PROCEDURES: Radiographs acquired from April 2020 to May 2021 were labeled by 1 radiologist in Summer of 2022 as either appropriately or inappropriately collimated for the cranial and caudal borders. A machine learning model was trained to identify the appropriate inclusion of the entire lung field at both the cranial and caudal borders. Both individual models and a combined overall inclusion model were assessed based on the combined results of both the cranial and caudal border assessments. RESULTS: The combined overall inclusion model showed a precision of 91.21% (95% CI [91, 91.4]), accuracy of 83.17% (95% CI [83, 83.4]), and F1 score of 87% (95% CI [86.8, 87.2]) for classification when compared with the radiologist's quality assessment. The model took on average 6 ± 1 second to run. CLINICAL RELEVANCE: Deep learning-based methods can classify small animal thoracic radiographs as appropriately or inappropriately collimated. These methods could be deployed in a clinical setting to improve the diagnostic quality of thoracic radiographs in small animal practice.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".