Computer Assisted Detection of Polycystic Ovary Morphology in Ultrasound Images
Why this work is in the frame
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Bibliographic record
Abstract
Polycystic ovary syndrome (PCOS) is an endocrine abnormality with multiple diagnostic criteria due to its heterogenic manifestations. One of the diagnostic criteria includes analysis of ultrasound images of ovaries for the detection of number, size, and distribution of follicles within the ovary. This involves manual tracing and counting of follicles on the ultrasound images to determine the presence of a polycystic ovary (PCO). We describe a novel method that automates PCO detection. Our algorithm involves segmentation of follicles from ultrasound images, quantifying the attributes of the automatically segmented follicles using stereology, storing follicle attributes as feature vectors, and finally classification of the feature vector into two categories. The classification categories are: PCO present and PCO absent. An automatic PCO diagnostic tool would save considerable time spent on manual tracing of follicles and measuring the length and width of every follicle. Our procedure was able to achieve classification accuracy of 92.86% using a linear discriminant classifier. Our classifier will improve the rapidity and accuracy of PCOS diagnosis, reducing the risk of the severe complications that can arise from delayed diagnosis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it