PenoMeter: a machine learning and algorithmic tool to advance Peyronie’s disease assessment
Bibliographic record
Abstract
BACKGROUND: Peyronie's disease curvature assessment is a critical step for patient assessment; however, tools for objective, unbiased, and reproducible quantification are currently limited. AIM: To develop an automated computational tool to identify the penis from a 2D image and to accurately and reproducibly measure the degree of angulation. METHODS: We developed PenoMeter using instance segmentation to identify penile anatomical components, key point detection to identify shaft corners, geometric calculations to locate and measure the angulation of the point of maximal curvature. We trained our model on training datasets and evaluated the PenoMeter using a separate dataset of digital penile images. OUTCOMES: The PenoMeter is an artificial intelligence-powered assistive diagnostic toolkit that can automatically assess the curvature angle of penile 2D images that holds potential for healthcare practitioners to use in assistance for PD assessments. RESULTS: The PenoMeter's reported angulation, relative to the mean angulation reported by three subspecialized urologists, falls within their range of variability in 57 out of 66 cases (86%) and outside their range of variability in 9 out of 66 cases (14%) of digital images. The PenoMeter demonstrated no intra-observer variance (0°) in repeated measures over time compared to the three subspecialized urologists who demonstrated intra-observer variability between by 3.8° to 7.8°. CLINICAL AND TRANSLATIONAL IMPLICATIONS: The PenoMeter can be utilized for initial PD assessment and tracking treatment outcomes in time-series data for both clinical and research contexts. STRENGTHS AND LIMITATIONS: Strengths of the PenoMeter include unbiased and objective quantification of penile curvature. Furthermore, it demonstrates no intra-observer variability, making it appealing for evaluating time-series digital images. Limitations of the PenoMeter include the lack of a measure of confidence for curvature assessment. Detection and measurement of other forms of PD deformities such as indentations, hourglass deformities, torque and distal tapering require further development. Finally, accurate curvature quantification is reliant on reproducibly acquiring accurate digital images and an accurate and consistent assessment of penile rigidity; therefore, a well-defined process for image acquisition and clinician assessment of penile rigidity immediately prior to digital photo capture would be required to enhance accuracy of obtaining a representatively accurate image for processing. CONCLUSIONS: The PenoMeter's performance in penile curvature assessment of digital photos are objective, accurate and reproducible, and therefore carries potential to assist clinicians' initial PD assessments and treatment outcome tracking. However, the PenoMeter is not currently positioned to replace the current gold-standard in-office assessment.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".