Deep learning to diagnose pouch of Douglas obliteration with ultrasound sliding sign
Notice bibliographique
Résumé
OBJECTIVES: Pouch of Douglas (POD) obliteration is a severe consequence of inflammation in the pelvis, often seen in patients with endometriosis. The sliding sign is a dynamic transvaginal ultrasound (TVS) test that can diagnose POD obliteration. We aimed to develop a deep learning (DL) model to automatically classify the state of the POD using recorded videos depicting the sliding sign test. METHODS: Two expert sonologists performed, interpreted, and recorded videos of consecutive patients from September 2018 to April 2020. The sliding sign was classified as positive (i.e. normal) or negative (i.e. abnormal; POD obliteration). A DL model based on a temporal residual network was prospectively trained with a dataset of TVS videos. The model was tested on an independent test set and its diagnostic accuracy including area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive value (PPV/NPV) was compared to the reference standard sonologist classification (positive or negative sliding sign). RESULTS: In a dataset consisting of 749 videos, a positive sliding sign was depicted in 646 (86.2%) videos, whereas 103 (13.8%) videos depicted a negative sliding sign. The dataset was split into training (414 videos), validation (139), and testing (196) maintaining similar positive/negative proportions. When applied to the test dataset using a threshold of 0.9, the model achieved: AUC 96.5% (95% CI: 90.8-100.0%), an accuracy of 88.8% (95% CI: 83.5-92.8%), sensitivity of 88.6% (95% CI: 83.0-92.9%), specificity of 90.0% (95% CI: 68.3-98.8%), a PPV of 98.7% (95% CI: 95.4-99.7%), and an NPV of 47.7% (95% CI: 36.8-58.2%). CONCLUSIONS: We have developed an accurate DL model for the prediction of the TVS-based sliding sign classification. LAY SUMMARY: Endometriosis is a disease that affects females. It can cause very severe scarring inside the body, especially in the pelvis - called the pouch of Douglas (POD). An ultrasound test called the 'sliding sign' can diagnose POD scarring. In our study, we provided input to a computer on how to interpret the sliding sign and determine whether there was POD scarring or not. This is a type of artificial intelligence called deep learning (DL). For this purpose, two expert ultrasound specialists recorded 749 videos of the sliding sign. Most of them (646) were normal and 103 showed POD scarring. In order for the computer to interpret, both normal and abnormal videos were required. After providing the necessary inputs to the computer, the DL model was very accurate (almost nine out of every ten videos was correctly determined by the DL model). In conclusion, we have developed an artificial intelligence that can interpret ultrasound videos of the sliding sign that show POD scarring that is almost as accurate as the ultrasound specialists. We believe this could help increase the knowledge on POD scarring in people with endometriosis.
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 | 0,000 | 0,004 |
| 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 ».