Automated Quantitative Cytology Imaging Analysis System in Cervical Cancer Screening in Shanxi Province, China
Bibliographic record
Abstract
Purpose: In China the number of pathologists is far from being enough to meet the demands of ongoing population based cervical cancer screening programs. This article aims to present our experience with automated quantitative cytology imaging platform, a reading system with an artificial intelligence that we currently use routinely for cervical cancer screening in Shanxi province.Methods: From 2012-2016 a total of 40 178 women were screened. Women were divided into three groups and each group had two subgroups. Smear and liquid based technique were compared using manual and automated platform.Results: Detection rates of CIN2 + and positive rates of CIN2 were higher in all three groups when automated quantitative cytology platform was used compared with groups where reading was done by the pathologist using conventional microscope. Operator’s costs associated with automated quantitative cytology platform vs. conventional reading using light microscope were compared too. The overall costs of operations based on automated platform were proven to be lower.Conclusion: The use of automated platform and artificial intelligence as a means to overcome the lack of cytotechnologists and pathologists and to implement proper quality control in the large scale population based cervical cancer screening seems very promising.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".