Digital Single-operator Cholangioscopy Research Articles

Comparison of disposable digital single-operator cholangioscopy versus direct peroral cholangioscopy for the diagnosis of intraductal superficial lesions of the bile duct.

Disposable digital single-operator cholangioscopy (D-SOC) and direct peroral cholangioscopy (D-POC) using an ultraslim endoscope are established POC modalities for the diagnosis and treatment of various biliary diseases. We compared the usefulness of D-SOC and D-POC for the diagnosis of intraductal superficial lesions of the bile duct (ISL-Bs). In total, 38 consecutive patients with suspected biliary diseases who underwent both D-SOC and D-POC were enrolled. The primary outcome was the detection rate of the ISL-Bs, and the secondary outcomes were the technical success of POC and POC-guided forceps biopsy sampling (POC-FB), procedure time, visualization quality, and tissue adequacy. D-SOC had a higher technical success rate than D-POC but without a statistically significant difference (100% vs. 92.1%, P = 0.248). D-POC had a marginally higher ISL-B detection rate (34.2% vs. 28.9%, P = 0.683) and significantly higher visualization quality (P = 0.033). The mean procedure time was significantly shorter with D-SOC (11.00 ± 1.34 vs. 19.03 ± 2.95 min, P < 0.001). The technical success rate of POC-FB and tissue adequacy did not differ between the two techniques (D-SOC vs. D-POC: 81.8% vs. 84.6%, P = 0.693 and 77.8% vs. 90.9%, P = 0.566). Our results demonstrate that both POC systems are safe and useful for the detection, characterization, and diagnosis of minute ISL-Bs. While D-SOC displayed a shorter procedure time and a tendency for higher technical success rate, D-POC provided superior visualization quality, allowing detailed observation of the surface structure and microvascular patterns.

Accurate and safe diagnosis and treatment of neoplastic biliary lesions using a novel 9F and 11F digital single-operator cholangioscope.

Background and study aims Digital single-operator cholangioscopy (DSOC) allows the diagnosis of biliary duct disorders and treatment for complicated stones. However, these technologies have limitations such as the size of the probe and working channel, excessive cost, and low image resolution. Recently, a novel DSOC system (eyeMAX, Micro-Tech, Nanjing, China) was developed to address these limitations. We aimed to evaluate the usefulness and safety of a novel 9F and 11F DSOC system in terms of neoplastic diagnostic accuracy based on visual examination, ability to evaluate tumor extension and to achieve complete biliary stone clearance, and procedure-related adverse events (AEs). Patients and methods Data from ≥ 18-year-old patients who underwent DSOC from July 2021 to April 2022 were retrospectively recovered and divided into a diagnostic and a therapeutic cohort. Results A total of 80 patients were included. In the diagnostic cohort (n = 49/80), neovascularity was identified in 26 of 49 patients (46.9%). Biopsy was performed in 65.3% patients with adequate tissue sample obtained in 96.8% of cases. Biopsy confirmed neoplasia in 23 of 32 cases. DSOC visual impression achieved 91.6% sensitivity and 87.5% specificity in diagnosing neoplasms. In the therapeutic cohort (n = 43/80), 26 of 43 patients required lithotripsy alone. Total stone removal was achieved in 71% patients in the first session. Neither early nor late AEs were documented in either the diagnostic or therapeutic cohort. Conclusions The novel DSOC device has excellent diagnostic accuracy in distinguishing neoplastic biliary lesions as well as therapeutic benefits in the context of total stone removal, with no documented AEs.

A Randomized Comparative Study of the Use of Individual Modality and Combination of Endoscopic Retrograde Cholangiopancreatography (ERCP) and Digital Single-Operator Cholangioscopy (DSOC) for Diagnosis of Indeterminate Biliary Strictures

Abstract Background: To determine the superiority of the combination of endoscopic retrograde cholangiopancreatography (ERCP) and digital single-operator cholangioscopy (DSOC) in the same sitting over the individual modality alone in patients with indeterminate biliary strictures. Materials and Methods: A randomized study enrolled 60 adult patients with biliary strictures who were randomized into two groups: ERCP + DSOC and ERCP/DSOC. Histopathologic or cytologic assessment was performed in terms of benign, indeterminate, or malignant nature of the strictures. Procedural adverse events were documented. Accuracy in terms of sensitivity (Sn), specificity (Sp), and predictive value [positive (PPV) and negative (NPV)] were noted. Results: As per final diagnosis, in ERCP/DSOC group, there were 12 (40%) benign cases and 18 (60%) malignant cases, and in group ERCP + DSOC, there were 8 (26.67%) benign cases and 22 (73.33%) malignant cases. ERCP/DSOC labeled 16 (53.33%) patients as benign, 8 (26.67%) as malignant, and 6 (20%) as indeterminate, while ERCP + DSOC labeled 8 (26.67%) as benign, 17 (56.67%) as malignant, and 5 (16.67%) as indeterminate. The Sn, Sp, PPV, and NPV of ERCP/DSOC were 44.4%, 75%, 100%, and 56.25%, and for ERCP + DSOC was 77.27%, 62.50%, 100%, and 62.5%, respectively (P = 0.033). Side effects were statistically similar in both the groups (P &gt; 0.05). Conclusion: To conclude, the combination of ERCP with DSOC is safe and effective with higher diagnostic sensitivity (77.27%) in comparison to standard ERCP or DSOC alone (44.4%) for the diagnosis of biliary strictures.

Diagnostic accuracy and interobserver agreement of cholangioscopy for indeterminate biliary strictures: A single-center experience

Background and study aimsCharacterization of indeterminate biliary strictures (IDBS) still represents a major challenge. Digital single-operator cholangioscopy (DSOC) could potentially overcome limits of conventional biopsy and brush sampling. The aim of this study was to compare diagnostic accuracy of visual evaluation and DSOC-guided biopsies to conventional trans-papillary sampling techniques and to evaluate the inter-observer agreement (IOA) on visual diagnosis. Patients and methodsAll consecutive patients undergoing DSOC-guided biopsy after conventional sampling techniques for IDBS during a six—year period were retrospectively evaluated. Final diagnosis was based on histological evaluation of the surgical specimen if available or a clinical follow-up of at least 6 months. For IOA, 20-second DSOC clips were retrospectively reviewed by 6 experts and 6 trainees and classified according to the Monaco Classification. ResultsThirty-five patients underwent DSOC for IDBS in the study period; 14 patients (F = 9) with a median age of 64 years (range 53–76) met the study aim. After DSOC, strictures location was changed in three patients (additional yield of 21.4 %). Intraductal DSOC-guided biopsy were technically successful in all cases, with an adequacy of 92.8 %. No adverse events were recorded. Final diagnosis was benign disease in five cases and cholangiocarcinoma in the others. For IOA, 29 videos were evaluated with almost perfect agreement for final diagnosis (kappa 0.871; agreement 93.1, p <0.001), although overall accuracy of DSOC visual finding was 73.6 % and 64.4 % for experts and trainees, respectively. ConclusionsDSOC could improve diagnostic accuracy for IDBS, since it showed high sensitivity for visual finding and high specificity for DSOC guided-biopsy. Visual diagnosis seems reliable for diagnosis using the Monaco Classification.

Deep Learning for Automatic Diagnosis and Morphologic Characterization of Malignant Biliary Strictures Using Digital Cholangioscopy: A Multicentric Study.

Digital single-operator cholangioscopy (D-SOC) has enhanced the ability to diagnose indeterminate biliary strictures (BSs). Pilot studies using artificial intelligence (AI) models in D-SOC demonstrated promising results. Our group aimed to develop a convolutional neural network (CNN) for the identification and morphological characterization of malignant BSs in D-SOC. A total of 84,994 images from 129 D-SOC exams in two centers (Portugal and Spain) were used for developing the CNN. Each image was categorized as either a normal/benign finding or as malignant lesion (the latter dependent on histopathological results). Additionally, the CNN was evaluated for the detection of morphologic features, including tumor vessels and papillary projections. The complete dataset was divided into training and validation datasets. The model was evaluated through its sensitivity, specificity, positive and negative predictive values, accuracy and area under the receiver-operating characteristic and precision-recall curves (AUROC and AUPRC, respectively). The model achieved a 82.9% overall accuracy, 83.5% sensitivity and 82.4% specificity, with an AUROC and AUPRC of 0.92 and 0.93, respectively. The developed CNN successfully distinguished benign findings from malignant BSs. The development and application of AI tools to D-SOC has the potential to significantly augment the diagnostic yield of this exam for identifying malignant strictures.

S167 A Randomized Study of the Efficacy of Endoscopic Retrograde Cholangiopancreatography (ERCP) and Digital Single Operator Cholangioscopy (DSOC) in the Diagnosis of Indeterminate Biliary Strictures

S167 A Randomized Study of the Efficacy of Endoscopic Retrograde Cholangiopancreatography (ERCP) and Digital Single Operator Cholangioscopy (DSOC) in the Diagnosis of Indeterminate Biliary Strictures

Usefulness of artificial intelligence-assisted digital single-operator cholangioscopy as a second-opinion consultation tool during interhospital assessment of an indeterminate biliary stricture: a case report.

Video 1Application of a novel digital single-operator cholangioscopy-based convolutional neuronal network to detect neoplastic lesions as a second-opinion consultation tool between 2 hospitals and biopsy sampling.

Improved fluoroscopy-guided biopsies in the diagnosis of indeterminate biliary strictures: a multi-center retrospective study

To evaluate the diagnostic accuracy of improved fluoroscopy-guided biopsies for indeterminate biliary strictures (IBDS). A multi-center retrospective study was performed. Patients with IBDS who underwent digital single-operator cholangioscopy (DSOC) and improved fluoroscopy-guided biopsies procedures were included. The individual sensitivity, specificity, and accuracy were analyzed. A total of 67 patients were enrolled in this multi-center retrospective study. The DSOC and improved fluoroscopy-guided biopsies procedures were successfully performed in all cases (100%). The sensitivity, specificity, and accuracy values were 83.3%, 89.5%, and 85.1% for DSOC visual impression; 95.8%, 94.7%, and 95.5% for improved fluoroscopy-guided biopsies procedures, respectively. The sensitivity and accuracy of improved fluoroscopy-guided biopsies were significantly higher compared with DSOC visual impression. Four patients (6.0%, 4/67) occurred adverse events after the procedures. Improved fluoroscopy-guided biopsies had a high diagnostic accuracy of IBDS diagnosis.

A real-time interpretable artificial intelligence model for thecholangioscopic diagnosis of malignant biliary stricture (with videos).

It is crucial to accurately determine malignant biliary strictures (MBSs) for early curative treatment. This study aimed to develop a real-time interpretable artificial intelligence (AI) system to predict MBSs under digital single-operator cholangioscopy (DSOC). A novel interpretable AI system called MBSDeiT was developed consisting of 2 models to identify qualified images and then predict MBS in real time. The overall efficiency of MBSDeiT was validated at the image level on internal, external, and prospective testing data sets and subgroup analyses, and at the video level on the prospective data sets; these findings were compared with those of the endoscopists. The association between AI predictions and endoscopic features was evaluated to increase the interpretability. MBSDeiT can first automatically select qualified DSOC images with an area under the curve (AUC) of .963 and .968 to .973 on the internal testing data set and the external testing data sets, and then identify MBSs with an AUC of .971 on the internal testing data set, an AUC of .978 to .999 on the external testing data sets, and an AUC of .976 on the prospective testing data set, respectively. MBSDeiT accurately identified 92.3% of MBSs in prospective testing videos. Subgroup analyses confirmed the stability and robustness of MBSDeiT. The AI system achieved superior performance to that of expert and novice endoscopists. The AI predictions were significantly associated with 4 endoscopic features (nodular mass, friability, raised intraductal lesion, and abnormal vessels; P< .05) under DSOC, which is consistent with the endoscopists' predictions. The study findings suggest that MBSDeiT could be a promising approach for the accurate diagnosis of MBSs under DSOC.

Digital single-operator cholangioscopy to guide selective cannulation of complex biliary strictures

BackgroundSelective cannulation and stenting of complex, tight, and/or angulated biliary strictures under endoscopic retrograde cholangiopancreaticography (ERCP) can be challenging. Digital single-operator cholangioscopy (SOC) may facilitate guidewire advancement through the stricture with endoscopic visual guidance. We aimed to describe a case series on clinical outcomes of this technique for selective cannulation, when used after failed conventional ERCP attempts.MethodsConsecutive patients who underwent therapeutic digital SOC for selective cannulation of biliary strictures after failed conventional ERCP were retrospectively included.ResultsTen patients with a malignant (n = 6) or benign (n = 4) biliary stricture were included. Digital SOC-assisted selective guidewire insertion and stent placement across the biliary stricture were technically successful in five (50%) patients. Bilirubin levels improved in all patients with technical success. One (10%) patient developed a post-ERCP pancreatitis.ConclusionsTechnically successful cannulation of biliary strictures with digital SOC was achieved in half of patients in whom cannulation with conventional ERCP failed, sparing them more invasive interventions. Stricture opacification during the failed ERCP was seen in all technically successful SOCs.

Gel-immersion electrohydraulic lithotripsy during digital single-operator cholangioscopy is helpful when bleeding occurs.

Gel-immersion electrohydraulic lithotripsy during digital single-operator cholangioscopy is helpful when bleeding occurs.

S25 Deep Learning for the Automatic Identification of Neoplastic Biliary Nodules in Patients With Indeterminate Biliary Stenosis During Digital Cholangioscopy

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, the 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. Conclusion: 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 1).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.

Digital single-operator cholangioscopy in diagnostic and therapeutic bilio-pancreatic diseases: A prospective, multicenter study

Background and aimDigital single-operator cholangioscopy (D-SOC) is an endoscopic procedure that is increasingly used for the management of bilio-pancreatic diseases.We aimed to investigate the efficacy and safety of D-SOC for diagnostic and therapeutic indications. MethodsThis is a multicenter, prospective study(January 2016-June 2019) across eighteen tertiary centers. The primary outcome was procedural success of D-SOC. Secondary outcomes were: D-SOC visual assessment and diagnostic yield of SpyBite biopsy in cases of biliary strictures, stone clearance rate in cases of difficult biliary stones, rate of adverse events(AEs) for all indications. ResultsD-SOC was performed in 369 patients (201(54,5%) diagnostic and 168(45,5%)therapeutic). Overall, procedural success rate was achieved in 360(97,6%) patients.The sensitivity, specificity, PPV, NPV and accuracy in biliary strictures were: 88,5%, 77,3%, 83,3%, 84,1% and 83,6% for D-SOC visual impression; 80,2%, 92,6%, 95,1%, 72,5% and 84,7% for the SpyBite biopsy, respectively.For difficult biliary stones, complete duct clearance was obtained in 92,1% patients (82,1% in a single session). Overall, AEs occurred in 37(10%) cases.The grade of AEs was mild or moderate for all cases, except one which was fatal. ConclusionD-SOC is effective for diagnostic and therapeutic indications.Most of the AEs were minor and managed conservatively, even though a fatal event has happened that is not negligible and should be considered before using D-SOC.

Digital single-operator cholangioscopy for biliary stricture after cadaveric liver transplantation.

BACKGROUNDBiliary strictures after liver transplantation (LT) remain clinically arduous and challenging situations, and endoscopic retrograde cholangiopancreatography (ERCP) has been considered as the gold standard for the management of biliary strictures after LT. Nevertheless, in the treatment of biliary strictures after LT with ERCP, many studies show that there is a large variation in diagnostic accuracy and therapeutic success rate. Digital single-operator peroral cholangioscopy (DSOC) is considered a valuable diagnostic modality for indeterminate biliary strictures.AIMTo evaluate DSOC in addition to ERCP for management of biliary strictures after LT.METHODSNineteen patients with duct-to-duct biliary reconstruction who underwent ERCP for suspected biliary complications between March 2019 and March 2020 at Beijing Chaoyang Hospital, Capital Medical University, were consecutively enrolled in this observational study. After evaluating bile ducts using fluoroscopy, cholangioscopy using a modern digital single-operator cholangioscopy system (SpyGlass DS™) was performed during the same procedure with patients under conscious sedation. All patients received peri-interventional antibiotic prophylaxis. Biliary strictures after LT were classified according to the manifestations of choledochoscopic strictures and the manifestations of transplanted hepatobiliary ducts.RESULTSTwenty-one biliary strictures were found in a total of 19 patients, among which anastomotic strictures were evident in 18 (94.7%) patients, while non-anastomotic strictures in 2 (10.5%), and space-occupying lesions in 1 (5.3%). Stones were found in 11 (57.9%) and loose sutures in 8 (42.1%). A benefit of cholangioscopy was seen in 15 (78.9%) patients. Cholangioscopy was crucial for selective guidewire placement prior to planned intervention in 4 patients. It was instrumental in identifying biliary stone and/or loose sutures in 9 patients in whom ERCP failed. It also provided a direct vision for laser lithotripsy. A space-occupying lesion in the bile duct was diagnosed by cholangioscopy in one patient. Patients with biliary stricture after LT displayed four types: (A) mild inflammatory change (n = 9); (B) acute inflammatory change edema, ulceration, and sloughing (n = 3); (C) chronic inflammatory change; and (D) acute suppurative change. Complications were seen in three patients with post-interventional cholangitis and another three with hyperamylasemia.CONCLUSIONDSOC can provide important diagnostic information, helping plan and perform interventional procedures in LT-related biliary strictures.

Automatic detection of tumor vessels in indeterminate biliary strictures in digital single-operator cholangioscopy

Background and study aims Indeterminate biliary strictures pose a significative clinical challenge. Dilated, irregular, and tortuous vessels, often described as tumor vessels, are frequently reported in biliary strictures with high malignancy potential during digital single-operator cholangioscopy (D-SOC). In recent years, the development of artificial intelligence (AI) algorithms for application to endoscopic practice has been intensely studied. We aimed to develop an AI algorithm for automatic detection of tumor vessels (TVs) in D-SOC images. Patients and methods A convolutional neural network (CNN) was developed. A total of 6475 images from 85 patients who underwent D-SOC (Spyglass, Boston Scientific, Marlborough, Massachusetts, United States) were included. Each frame was evaluated for the presence of TVs. The performance of the CNN was measured by calculating the area under the curve (AUC), sensitivity, specificity, positive and negative predictive values. Results The sensitivity, specificity, positive predictive value, and negative predictive value were 99.3 %, 99.4 %, 99.6% and 98.7 %, respectively. The AUC was 1.00. Conclusions Our CNN was able to detect TVs with high accuracy. Development of AI algorithms may enhance the detection of macroscopic characteristics associated with high probability of biliary malignancy, thus optimizing the diagnostic workup of patients with indeterminate biliary strictures.

Digital single-operator cholangioscopy with EHL as salvage therapy of an internalized and stone-impacted biliary stent 13 years after implantation.

Digital single-operator cholangioscopy with EHL as salvage therapy of an internalized and stone-impacted biliary stent 13 years after implantation.

Automatic Identification of Papillary Projections in Indeterminate Biliary Strictures Using Digital Single-Operator Cholangioscopy.

INTRODUCTION:Characterization of biliary strictures is challenging. Papillary projections (PP) are often reported in biliary strictures with high malignancy potential during digital single-operator cholangioscopy. In recent years, the development of artificial intelligence (AI) algorithms for application to endoscopic practice has been intensely studied. We aimed to develop an AI algorithm for automatic detection of PP in digital single-operator cholangioscopy images.METHODS:A convolutional neural network (CNN) was developed. Each frame was evaluated for the presence of PP. The CNN's performance was measured by the area under the curve, sensitivity, specificity, and positive and negative predictive values.RESULTS:A total of 3,920 images from 85 patients were included. Our model had a sensitivity and specificity 99.7% and 97.1%, respectively. The area under the curve was 1.00.DISCUSSION:Our CNN was able to detect PP with high accuracy. Future development of AI tools may optimize the macroscopic characterization of biliary strictures.

S18 Deep Learning and Digital Single-Operator Cholangioscopy: Automatic Detection and Classification of Malignant Biliary Masses in Patients With Indeterminate Biliary Strictures

S18 Deep Learning and Digital Single-Operator Cholangioscopy: Automatic Detection and Classification of Malignant Biliary Masses in Patients With Indeterminate Biliary Strictures

S979 Artificial Intelligence and Digital Single-operator Cholangioscopy: Automatic Identification of Tumor Vessels in Patients with Indeterminate Biliary Stenosis

S979 Artificial Intelligence and Digital Single-operator Cholangioscopy: Automatic Identification of Tumor Vessels in Patients with Indeterminate Biliary Stenosis

Artificial intelligence for automatic diagnosis of biliary stricture malignancy status in single-operator cholangioscopy: a pilot study

The diagnosis and characterization of biliary strictures (BSs) is challenging. The introduction of digital single-operator cholangioscopy (DSOC) that allows direct visual inspection of the lesion and targeted biopsy sampling significantly improved the diagnostic yield in patients with indeterminate BSs. However, the diagnostic efficiency of DSOC remains suboptimal. Convolutional neural networks (CNNs) have shown great potential for the interpretation of medical images. We aimed to develop a CNN-based system for automatic detection of malignant BSs in DSOC images. We developed, trained, and validated a CNN-based on DSOC images. Each frame was labeled as a normal/benign finding or as a malignant lesion if histopathologic evidence of biliary malignancy was available. The entire dataset was split for 5-fold cross-validation. In addition, the image dataset was split for constitution of training and validation datasets. The performance of the CNN was measured by calculating the area under the receiving operating characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive values. A total of 11,855 images from 85 patients were included (9695 malignant strictures and 2160 benign findings). The model had an overall accuracy of 94.9%, sensitivity of 94.7%, specificity of 92.1%, and AUC of .988 in cross-validation analysis. The image processing speed of the CNN was 7 ms per frame. The developed deep learning algorithm accurately detected and differentiated malignant strictures from benign biliary conditions. The introduction of artificial intelligence algorithms to DSOC systems may significantly increase its diagnostic yield for malignant strictures.