D0023 - Deep Learning for the Automatic Identification of Neoplastic Biliary Nodules in Patients With Indeterminate Biliary Stenosis During Digital Cholangioscopy
Centro Hospitalar de S. João Porto, Porto, Portugal
Tiago Ribeiro, MD1, Miguel Mascarenhas, MD1, Joao Afonso, MD2, Filipe Vilas-Boas, 2, João Ferreira, PhD3, Pedro Pereira, 2, Guilherme Macedo, MD, PhD1 1Centro Hospitalar de S. João, Porto, Porto, Portugal; 2Centro Hospitalar de São João, Porto, Porto, Portugal; 3FEUP: Faculdade de Engenharia da Universidade do Porto, Porto, Porto, Portugal
Introduction: Digital single-operator cholangioscopy (DSOC) provides direct visual exploration of the biliary tract. This tool has become essential for distinguishing benign from malignant biliary strictures. The visual aspect of these lesions is highly sensitive for the diagnosis of malignancy. Macroscopic characteristic such as tumor vessels, papillary projections, masses and nodules are common findings in patients with malignant biliary strictures. Recent endoscopic literature has demonstrated large interest in artificial intelligence (AI) systems for detection of endoscopic lesions. However, t
he application of these systems for automatic characterization of biliary lesions has not been explored. This study aimed to develop a convolutional neural network for automatic detection of biliary nodules in D-SOC images.
Methods: A convolutional neural network (CNN) was designed for automatic identification of biliary nodules using DSOC images. A total of 16150 frames were extracted from a pool of 85 patients undergoing DSOC (Spyglass™ DS II system, Boston Scientific, Marlborough, MA, USA). Each frame was classified by two endoscopists with experience in D-SOC regarding the presence or absence of nodules. Two image datasets were built for training and validation of the CNN (80% and 20% of the full image dataset, respectively). The performance of the model was measured by calculating the area under the receiving operating characteristic curve (AUC), sensitivity, specificity, positive and negative predictive values (PPV and NPV, respectively).
Results: The architecture of the network was optimized for the detection of malignant biliary nodules. The CNN reached a sensitivity of 94.0%, a specificity of 99.9%, a PPV of 98.8% and a NPV of 99.7%. The overall accuracy of the deep learning system was 99.6%. The AUC was 1.00.
Discussion: The development of deep learning algorithms for application to DSOC may further potentiate the diagnostic capabilities of this modality. The application of these AI systems on real-time may help to guide biopsies and, thus, mitigate the current limitations of DSOC-guided biopsies, to achieve a more accurate diagnosis and timelier treatments. Our deep learning algorithm has demonstrated high performance levels for the detection of malignant nodules of the biliary tract, with high sensitivity, specificity and overall accuracy.
Figure: Figure 1: 1A - Output obtained from the application of the CNN. A blue bar represents a correct prediction. B – benign/normal findings; NN – nodules. 1B - ROC analyses of the network’s performance. AUC: area under the curve; CNN: convolutional neural network; B: benign/normal findings; NN: nodules.
Disclosures:
Tiago Ribeiro indicated no relevant financial relationships.
Miguel Mascarenhas indicated no relevant financial relationships.
Joao Afonso indicated no relevant financial relationships.
Filipe Vilas-Boas indicated no relevant financial relationships.
João Ferreira indicated no relevant financial relationships.
Pedro Pereira indicated no relevant financial relationships.
Guilherme Macedo indicated no relevant financial relationships.
Tiago Ribeiro, MD1, Miguel Mascarenhas, MD1, Joao Afonso, MD2, Filipe Vilas-Boas, 2, João Ferreira, PhD3, Pedro Pereira, 2, Guilherme Macedo, MD, PhD1. D0023 - Deep Learning for the Automatic Identification of Neoplastic Biliary Nodules in Patients With Indeterminate Biliary Stenosis During Digital Cholangioscopy, ACG 2022 Annual Scientific Meeting Abstracts. Charlotte, NC: American College of Gastroenterology.
