PenoMeter: a machine learning and algorithmic tool to advance Peyronie’s disease assessment
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».