Multimodal convolutional neural network–based algorithm for real-time detection and differentiation of malignant and inflammatory biliary strictures in cholangioscopy: a proof-of-concept study (with video)

URN zum Zitieren dieses Dokuments:

urn:nbn:de:bvb:355-epub-765141 Copy

DOI zum Zitieren dieses Dokuments:

10.5283/epub.76514 Copy

Ziegler, Joceline ; Dobsch, Philipp ; Rozema, Marten ; Zuber-Jerger, Ina ; Weigand, Kilian ; Reuther, Stefan ; Müller, Martina ; Kandulski, Arne

Lizenz: Creative Commons Namensnennung-NichtKommerziell-KeineBearbeitung 4.0 International PDF - Veröffentlichte Version (2MB)

Veröffentlichungsdatum dieses Volltextes: 09 Apr 2025 08:04

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Details

Dokumentenart:	Artikel
Open Access Art:	DEAL (Elsevier)
Titel eines Journals oder einer Zeitschrift:	Gastrointestinal Endoscopy
Verlag:	Elsevier
Band:	101
Nummer des Zeitschriftenheftes oder des Kapitels:	4
Seitenbereich:	830-842.e2
Datum:	13 September 2024
Institutionen:	Medizin > Lehrstuhl für Innere Medizin I
Sonstige Projekte:	Bayerische Forschungsstiftung, AZ-1534-21
Identifikationsnummer:	Wert Typ 10.1016/j.gie.2024.09.001 DOI
Dewey-Dezimal-Klassifikation:	600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin
Status:	Veröffentlicht
Begutachtet:	Ja, diese Version wurde begutachtet
An der Universität Regensburg entstanden:	Zum Teil
Dokumenten-ID:	76514

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Zusammenfassung

Background and Aims Deep learning algorithms gained attention for detection (computer-aided detection [CADe]) of biliary tract cancer in digital single-operator cholangioscopy (dSOC). We developed a multimodal convolutional neural network (CNN) for detection (CADe), characterization and discriminating (computer-aided diagnosis [CADx]) between malignant, inflammatory, and normal biliary tissue in ...

Zusammenfassung

Background and Aims
Deep learning algorithms gained attention for detection (computer-aided detection [CADe]) of biliary tract cancer in digital single-operator cholangioscopy (dSOC). We developed a multimodal convolutional neural network (CNN) for detection (CADe), characterization and discriminating (computer-aided diagnosis [CADx]) between malignant, inflammatory, and normal biliary tissue in raw dSOC videos. In addition, clinical metadata were included in the CNN algorithm to overcome limitations of image-only models.
Methods
Based on dSOC videos and images of 111 patients (total of 15,158 still frames), a real-time CNN-based algorithm for CADe and CADx was developed and validated. We established an image-only model and metadata injection approach. In addition, frame-wise and case-based predictions on complete dSOC video sequences were validated. Model embeddings were visualized, and class activation maps highlighted relevant image regions.
Results
The concatenation-based CADx approach achieved a per-frame area under the receiver-operating characteristic curve of .871, sensitivity of .809 (95% CI, .784-.832), specificity of .773 (95% CI, .761-.785), positive predictive value of .450 (95% CI, .423-.467), and negative predictive value of .946 (95% CI, .940-.954) with respect to malignancy on 5715 test frames from complete videos of 20 patients. For case-based diagnosis using average prediction scores, 6 of 8 malignant cases and all 12 benign cases were identified correctly.
Conclusions
Our algorithm distinguishes malignant and inflammatory bile duct lesions in dSOC videos, indicating the potential of CNN-based diagnostic support systems for both CADe and CADx. The integration of non-image data can improve CNN-based support systems, targeting current challenges in the assessment of biliary strictures.

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Metadaten zuletzt geändert: 28 Aug 2025 13:36

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