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DIAGNOSTIC ACCURACY OF COMPUTER AIDED DETECTION (CADE) ASSISTED READING VERSUS CLINICIAN READING FOR POLYP DETECTION IN COLON CAPSULE ENDOSCOPY: A MULTICENTER PROSPECTIVE STUDY

Ian Io Lei 1, Nicholas Parsons 2, Cristiana Huhulea 1, Hagen Wenzek 3, Liz White 3, Pablo Laiz 3, Charlie Noble 4, Alexander R. Robertson 5, Anastasios Koulaouzidis 6, Ramesh P. Arasaradnam 1

Affiliations

1 University Hospital Coventry and Warwickshire, Coventry, United Kingdom

2 University of Warwick, Coventry, United Kingdom

3 Corporate Health International, Inverness, United Kingdom

4 Noblesoft, Surrey, United Kingdom

5 University Hospitals Leicester, Leicester, United Kingdom

6 SDU, Svendborg, Denmark|||Odense University Hospital, Svendborg, Denmark

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This summary was generated by an AI large language model based on the content transcript. It is for informational purposes only and should not be considered a substitute for clinical judgment. Always rely on your professional expertise and the full clinical context when making clinical decisions.

Abstract

Introduction

Colon Capsule Endoscopy (CCE) offers a non-invasive alternative to colonoscopy for lower gastrointestinal (GI) investigations. However, its adoption is limited by lengthy reading times, reader fatigue, and variable diagnostic accuracy. These factors are often influenced by bowel preparation quality and completion rates. Artificial intelligence (AI) has shown the potential to improve diagnostic accuracy of polyp detection and reduce reading times in capsule endoscopy.

Aims & Methods

Objective(s): The primary aim is to assess the diagnostic accuracy of Computer-Aided Detection (CADe) system (AiSPEED™) assisted CCE reading using full-video analysis in a real-world clinical setting. Secondary outcome was to assess a) non-inferiority compared to standard clinician reading in detecting polypoid lesions and b) mean reading time.
Methods: CESCAIL study is a multi-centre study of 14 UK centres recruited 720 participants between November 2021 and September 2024. Participants (prospectively and retrospectively enrolled ) included those ≥18 years referred for CCE under the urgent cancer pathway or for post-polypectomy surveillance. CCEs were performed using PillCam™ COLON 2 and analysed using AiSPEED™. CCE videos were evaluated by separate clinicians who provided standard readings, while AI-assisted reading included an AI-assisted pre-reader review and subsequent clinician validation.

Results

720 patients were enrolled, with 673 included in the final analysis. The capsule excretion rate before battery depletion was 77.1%, with adequate bowel preparation achieved at 78.1% (with standard) and 74.0% (with AI-assisted reading) (McNemar p=0.108). In the per-patient analysis, the diagnostic yield for detecting polyps was 63.6% (428/673) for standard reading and 70.6% (475/673) for AI-assisted reading, using the expert panel as the reference standard. These findings confirmed both non-inferiority (p<0.001) and superiority (p<0.001) of the AI-assisted reading. The diagnostic accuracy for detecting polyps requiring follow-up procedure (flexible sigmoidoscopy/colonoscopy) was 0.96 (95% CI: 0.95–0.98) for AI-assisted reading, compared to 0.91 (95% CI: 0.89–0.93) for standard reading (p<0.0001) (see Table 1). The mean reading time per video was 8.7 minutes for AI-assisted reading compared to 47.3 minutes for standard reading, representing a 5.4-fold reduction.

Table 1: Per-Patient Outcome Summary (3 different endpoints) (n = 673)
Clinical outcome	Presence or Absence of Polyp (Per-Patient)			Polyp size≥ 6mm or ≥3 Polyps (Per-Patient)			Polyp size≥ 10mm or ≥5 Polyps (Per-Patient)
NHS England Guideline (11, 12)	Colonoscopy (Tier 1-3) vs no follow-up (Tier 4)			Polyp size≥ 6mm or ≥3 polyps (Tier 1-2) vs Polyp size <6mm or <3 (Tier 3-4)			Urgent Colon (Tier1) vs Other Outcome (Tier 2-4)
ESGE guidline (13)	-			ESGE 2012 guideline: Colonoscopy vs 5-year surveillance			-
Performance measures (%) (DTComPair)	Standard Arm (95% CI)	AI-assisted Arm (95% CI)	P value (McNemar’s)	Standard Arm (95% CI)	AI-assisted Arm (95% CI)	P value (McNemar’s)	Standard Arm (95% CI)	AI-assisted Arm (95% CI)	P value (McNemar’s)
Sensitivity	0.88 (0.85, 0.91)	0.96 (0.95, 0.98)	<0.001***	0.87 (0.84, 0.90)	0.96 (0.94, 0.98)	0.004**	0.90 (0.87, 0.93)	0.96 (0.93, 0.98)	0.004**
Specificity	0.99 (0.97,1.00)	0.96 (0.93,0.99)	0.058	0.99 (0.98,1.00)	0.96 (0.94,0.99)	0.035*	0.97 (0.95,0.99)	0.97 (0.96,0.99)	0.81
PPV	1.00 (0.99,1.00)	0.98 (0.97, 0.99)	0.071	1.00 (0.99,1.00)	0.98 (0.97, 0.99)	0.043*	0.97 (0.96,0.99)	0.98 (0.96, 0.99)	0.72
NPV	0.76 (0.71-0.82)	0.91 (0.88,0.95)	<0.001***	0.80 (0.75-0.85)	0.92 (0.86,0.95)	<0.001***	0.90 (0.87-0.93)	0.95 (0.93,0.97)	0.004**
Diagnostic accuracy	0.91 (0.89-0.93)	0.96 (0.95-0.98)	<0.001***	0.91 (0.89-0.93)	0.96 (0.94-0.97)	<0.001**	0.94 (0.92-0.95)	0.96 (0.95-0.98)	0.017*

Conclusion

AI-assisted reading with AiSPEED™ offers the potential for greater accuracy in polyp detection and significantly reduced clinician reading times during CCE examination compared to standard reading methods.

References

1. Moen S, Vuik FER, Kuipers EJ, Spaander MCW. Artificial Intelligence in Colon Capsule Endoscopy-A Systematic Review. Diagnostics (Basel). 2022;12(8).
2. Mota J, Joao Almeida M, Mendes F, Martins M, Ribeiro T, Afonso J, et al. A Comprehensive Review of Artificial Intelligence and Colon Capsule Endoscopy: Opportunities and Challenges. Diagnostics (Basel). 2024;14(18).
3. Spada C, Piccirelli S, Hassan C, Ferrari C, Toth E, Gonzalez-Suarez B, et al. AI-assisted capsule endoscopy reading in suspected small bowel bleeding: a multicentre prospective study. Lancet Digit Health. 2024;6(5):e345-e53.
4. Spada C, Hassan C, Galmiche JP, Neuhaus H, Dumonceau JM, Adler S, et al. Colon capsule endoscopy: European Society of Gastrointestinal Endoscopy (ESGE) Guideline. Endoscopy. 2012;44(5):527-36.
5. PillCam COLON 2 for investigation of the colon through direct visualisation [Internet]. NATIONAL INSTITUTE FOR HEALTH AND CARE EXCELLENCE. August 2024. Available from: https://www.nice.org.uk/guidance/gid-dg10083/documents/final-scope-2.

Disclosure

Hagen Wenzek serves as the CEO, and Elizabeth White is the Project Manager at Corporate Health International which owns the intellectual property of AiSPEED^TM

Conference

UEG Week Berlin 2025

Topics

Colorectal

Submission format

Abstract

Session

Endoscopy: New techniques and training (Posters)

Citation

United European Gastroenterology Journal 2025; 13 (Supplement 8)

Published

2025

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COMPARISON OF ENDOSCOPIC ULTRASOUND-GUIDED VERSUS PERCUTANEOUS BILIARY DRAINAGE FOR PREOPERATIVE MANAGEMENT OF MALIGNANT DISTAL BILIARY OBSTRUCTION AFTER FAILED ERCP: A MULTICENTER RETROSPECTIVE STUDY

Kenji Ikezawa

A NOVEL DEEP LEARNING MODEL FOR PREDICTING COLORECTAL ANASTOMOTIC DEHISCENCE: A PIONEER MULTICENTER TRANSATLANTIC STUDY

Manuel Limbert

POLYDEEP ADVANCE 3: MULTICENTRE RANDOMIZED CONTROLLED TRIAL COMPARING THE POLYDEEP-ASSISTED COLONOSCOPY WITH CONVENTIONAL COLONOSCOPY

Joaquín Cubiella

ARTIFICIAL INTELLIGENCE FOR COMPREHENSIVE PANCREATIC LESION PROFILING: MULTICENTER VALIDATION OF SOLID/CYSTIC LESION DETECTION/CHARACTERIZATION AND IPMN GRADING

Guilherme Macedo

Words of welcome

Julia Mayerle

Transglutaminase inhibition in CeD

Detlef Schuppan

UEG Presentation

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COMPARISON OF ENDOSCOPIC ULTRASOUND-GUIDED VERSUS PERCUTANEOUS BILIARY DRAINAGE FOR PREOPERATIVE MANAGEMENT OF MALIGNANT DISTAL BILIARY OBSTRUCTION AFTER FAILED ERCP: A MULTICENTER RETROSPECTIVE STUDY

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COMPARISON OF ENDOSCOPIC ULTRASOUND-GUIDED VERSUS PERCUTANEOUS BILIARY DRAINAGE FOR PREOPERATIVE MANAGEMENT OF MALIGNANT DISTAL BILIARY OBSTRUCTION AFTER FAILED ERCP: A MULTICENTER RETROSPECTIVE STUDY

Itonaga Masahiro 1, Mamoru Takenaka 2, Hideyuki Shiomi 3, Koh Kitagawa 4, Shuhei Shintani 5, Hirotsugu Maruyama 6, Ryota Sagami 7, Tsukasa Ikeura 8, Takeshi Ogura 9, Yusuke Ishida 10, Koichiro Mandai 11, Satoshi Sugimori 12, Yoshiki Imamura 13, Atsuhiro Masuda 14, Kenji Ikezawa 15, Atsushi Shimizu 16, Misako Nagai 4, Atsushi Nakai 2, Ryota Nakano 3, Masayuki Kitano 16

Affiliations

1 Wakayama Medical University, Wakayama City, Japan

2 Kindai University Faculty of Medicine, Osaka, Japan

3 Hyogo Medical University, Hyogo, Japan

4 Nara Medical University, Nara, Japan

5 Shiga University of Medical Science, Otsu, Japan

6 Osaka City University Graduate School of Medicine, Osaka, Japan

7 Oita University, Yufu, Japan

8 Kansai Medical University, Hirakata, Japan

9 Osaka Medical and Pharmaceutical University Hospital, Takatsuki, Japan

10 Fukuoka University Faculty of Medicine, Fukuoka, Japan

11 Kyoto Second Red Cross Hospital, Kyoto, Japan

12 Osaka City Genaral Hospital, Osaka, Japan

13 Ehime University Graduate School of Medicine, Ehime, Japan

14 Kobe graduate school of medicine, Kobe, Japan

15 Osaka International Cancer Institute, Osaka, Japan

16 Wakayama Medical University, Wakayama, Japan

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Abstract

Introduction

Preoperative biliary drainage (PBD) is commonly used in patients with malignant distal biliary obstruction (MDBO) to maintain performance status, prevent cholangitis and allow subsequent surgical or neoadjuvant treatment. Endoscopic retrograde cholangiopancreatography (ERCP) is the first-line approach, but in cases of failure, percutaneous transhepatic biliary drainage (PTBD) or endoscopic ultrasound-guided biliary drainage (EUS-BD) are considered. While EUS-BD has shown favourable outcomes compared to PTBD in the palliative setting, evidence comparing these modalities in the preoperative setting is limited.

Aims & Methods

The aim of this study was to compare the efficacy, safety, and surgical outcomes of PTBD and EUS-BD in patients with MDBO after failed ERCP. This retrospective multicenter study included patients who underwent PTBD or EUS-BD followed by pancreaticoduodenectomy (PD) for periampullary cancer at 15 referral centers in Japan between January 2012 and December 2021. Only patients who had failed or were not amenable to ERCP were included. The primary outcome was surgery-related adverse events (AEs), while secondary outcomes included drainage efficacy, recurrence patterns, disease-free survival (DFS), and overall survival (OS). Propensity score matching (PSM) was used to adjust for baseline differences.

Results

In total, 2350 patients received PBD before PD. Of the 79 patients in whom ERCP failed, 65 underwent PTBD and 14 underwent EUS-BD. After PSM, 13 matched pairs were analyzed. Technical and clinical success rates were comparable between groups. However, the EUS-BD group had a significantly higher internalization success rate (100% vs. 46%, P=0.005) and fewer drainage sessions required (median: 1 vs. 2, P=0.015). The EUS-BD group also had a significantly shorter hospital stay for drainage (11 vs. 17 days, P=0.042). Rates of drainage-related AEs tended to be lower in the EUS-BD group, although not statistically significant. Surgery-related outcomes, including operative time, intraoperative blood loss, R0 resection rate, postoperative hospital stay, and surgery-related AEs, were not significantly different between groups. No cases of needle tract seeding were observed. Median DFS and OS were comparable between groups (DFS: 14.2 vs. 16.8 months, P=0.22; OS: 17.9 vs. 27.9 months, P=0.167).

Conclusion

EUS-BD showed comparable surgical outcomes and long-term oncological efficacy to PTBD, while offering superior internalization success, fewer interventions, and a shorter preoperative hospital stay. These advantages make EUS-BD a favorable alternative to PTBD in the preoperative management of MDBO after failed ERCP, particularly in patients scheduled for neoadjuvant therapy.

Disclosure

Kitano M has received grants from Boston Scientific, Zeon Medical Inc., and Medicos Hirata Inc. Takenaka M, Shiomi H, Kitagawa K, Shintani S, Maruyama H, Sagami R, Ikeura T, Ogura T, Ishida Y, Mandai K, Sugimori S, Imamura Y, Masuda A, Ikezawa K,Shimizu A, Nakai A, Nagai M and Nakano R have no conflicts of interest or financial ties to disclose.

Event

UEG Week Berlin 2025

Topics

Endoscopy Hepatobiliary

Submission format

Abstract

Session

Endoscopic solutions to malignant biliary obstruction

Citation

United European Gastroenterology Journal 2025; 13 (Supplement 8)

Published

2025

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COMPARISON OF ENDOSCOPIC ULTRASOUND-GUIDED VERSUS PERCUTANEOUS BILIARY DRAINAGE FOR PREOPERATIVE MANAGEMENT OF MALIGNANT DISTAL BILIARY OBSTRUCTION AFTER FAILED ERCP: A MULTICENTER RETROSPECTIVE STUDY

Kenji Ikezawa

A NOVEL DEEP LEARNING MODEL FOR PREDICTING COLORECTAL ANASTOMOTIC DEHISCENCE: A PIONEER MULTICENTER TRANSATLANTIC STUDY

Manuel Limbert

POLYDEEP ADVANCE 3: MULTICENTRE RANDOMIZED CONTROLLED TRIAL COMPARING THE POLYDEEP-ASSISTED COLONOSCOPY WITH CONVENTIONAL COLONOSCOPY

Joaquín Cubiella

ARTIFICIAL INTELLIGENCE FOR COMPREHENSIVE PANCREATIC LESION PROFILING: MULTICENTER VALIDATION OF SOLID/CYSTIC LESION DETECTION/CHARACTERIZATION AND IPMN GRADING

Guilherme Macedo

Words of welcome

Julia Mayerle

Transglutaminase inhibition in CeD

Detlef Schuppan

UEG Presentation

Share via Email Share on Facebook Share on X Share on LinkedIn Share on Bluesky

A NOVEL DEEP LEARNING MODEL FOR PREDICTING COLORECTAL ANASTOMOTIC DEHISCENCE: A PIONEER MULTICENTER TRANSATLANTIC STUDY

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A NOVEL DEEP LEARNING MODEL FOR PREDICTING COLORECTAL ANASTOMOTIC DEHISCENCE: A PIONEER MULTICENTER TRANSATLANTIC STUDY

Miguel Mascarenhas-Saraiva 1, Francisco Mendes 1, André Martins dos Santos 2, António Miguel Martins Pinto da Costa 3, Filipa Fonseca 4, Eduardo Carvalho 5, Nilza Ramião 5, Guilherme Barbosa 5, Maisa Vasconcellos 6, Abdullah Bunaiyan 7, Maria Almeida 1, Joana Mota 1, Tiago Ribeiro 1, Miguel Martins 1, João Afonso 1, Pedro Cardoso 1, Francisca Pinheiro 2, Matheus Carvalho 3, Marcos Eduardo Lera dos Santos 3, Eduardo De Moura 3, Marley Feitosa 6, Ahsan Javed 7, Guilherme Macedo 1, Manuel Limbert 4

Affiliations

1 Centro Hospitalar Universitário de São João, Porto, Portugal

2 Faculdade de Medicina da Universidade do Porto, Porto, Portugal

3 Hospital das Clínicas da Faculdade de Medicina da USP, São Paulo, Brazil

4 Instituto Português de Oncologia IPO, Lisboa, Portugal

5 INEGI – Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal

6 Hospital das Clínicas da Faculdade de Medicina de USP - Ribeirão Preto, São Paulo, Brazil

7 Liverpool University Hospitals NHS Foundation Trust, Liverpool, United Kingdom

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Abstract

Introduction

Colorectal anastomotic dehiscence (CAD) is one of the most severe postoperative complications in colorectal surgery, significantly impacting patient morbidity and mortality. Current risk assessment methods rely on clinical and intraoperative factors, but no real-time predictive tool exists. This study aimed to develop an artificial intelligence model based on intraoperative recording of the anastomosis for CAD prediction.

Aims & Methods

A convolutional neural network (CNN) was trained using a dataset of annotated frames from colorectal surgery videos across three international high-volume centers: IPO Lisboa, Hospital das Clínicas de Ribeirão Preto, São Paulo, Brazil and Royal Liverpool University Hospital. The dataset included a total of 5,356 annotated frames from 25 patients, 2,007 frames classified as CAD-positive and 3,349 as CAD-negative. Four CNN architectures (EfficientNetB0, EfficientNetB7, ResNet50, and MobileNetV2) were tested. Performance was evaluated using sensitivity, specificity, accuracy, and area under the receiver operating characteristic (AUROC) curve. Heatmaps were generated to identify key regions influencing predictions.

Results

The best-performing model achieved an accuracy of 99.55%, AUROC of 99.60%, sensitivity of 99.21%, specificity of 100%, PPV of 100%, and NPV of 98.94%. The model reliably identified CAD-positive frames and provided visual explanations through heatmaps.

Conclusion

To our knowledge, this is the first AI model developed to predict CAD using intraoperative video analysis. Its accuracy suggests the potential to redefine surgical decision-making by providing real-time risk assessment. Further refinement with a larger dataset and diverse surgical techniques could enable intraoperative interventions to prevent CAD before it occurs, marking a paradigm shift in colorectal surgery.

Event

UEG Week Berlin 2025

Topics

Colorectal Endoscopy Surgery

Submission format

Abstract

Session

Lower GI: Surgical considerations

Citation

United European Gastroenterology Journal 2025; 13 (Supplement 8)

Published

2025

More Like This:

COMPARISON OF ENDOSCOPIC ULTRASOUND-GUIDED VERSUS PERCUTANEOUS BILIARY DRAINAGE FOR PREOPERATIVE MANAGEMENT OF MALIGNANT DISTAL BILIARY OBSTRUCTION AFTER FAILED ERCP: A MULTICENTER RETROSPECTIVE STUDY

Kenji Ikezawa

A NOVEL DEEP LEARNING MODEL FOR PREDICTING COLORECTAL ANASTOMOTIC DEHISCENCE: A PIONEER MULTICENTER TRANSATLANTIC STUDY

Manuel Limbert

POLYDEEP ADVANCE 3: MULTICENTRE RANDOMIZED CONTROLLED TRIAL COMPARING THE POLYDEEP-ASSISTED COLONOSCOPY WITH CONVENTIONAL COLONOSCOPY

Joaquín Cubiella

ARTIFICIAL INTELLIGENCE FOR COMPREHENSIVE PANCREATIC LESION PROFILING: MULTICENTER VALIDATION OF SOLID/CYSTIC LESION DETECTION/CHARACTERIZATION AND IPMN GRADING

Guilherme Macedo

Words of welcome

Julia Mayerle

Transglutaminase inhibition in CeD

Detlef Schuppan

UEG Presentation

Share via Email Share on Facebook Share on X Share on LinkedIn Share on Bluesky

POLYDEEP ADVANCE 3: MULTICENTRE RANDOMIZED CONTROLLED TRIAL COMPARING THE POLYDEEP-ASSISTED COLONOSCOPY WITH CONVENTIONAL COLONOSCOPY

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POLYDEEP ADVANCE 3: MULTICENTRE RANDOMIZED CONTROLLED TRIAL COMPARING THE POLYDEEP-ASSISTED COLONOSCOPY WITH CONVENTIONAL COLONOSCOPY

Pedro Davila-Piñón 1, Astrid Irene Díez Martín 1, Cristina Regueiro Expósito 1, Alba Nogueira-Rodríguez 2, Florentino Fdez-Riverola 2, Miguel Reboiro-Jato 2, Daniel Glez-Peña 2, Rubén Domínguez-Carbajales 3, Luisa De Castro Parga 4, Jesús Daniel Fernández de Castro 5, Pablo Vega 5, Miguel Telmo Galovart Araguas 4, Santiago Soto 5, Sara Alonso Lorenzo 4, Alfonso Martinez Turnes 4, Noel Pin Vieito 5, Sara Zarraquiños Martínez 5, David Remedios 5, Cristina Alejandra Sánchez Gómez 5, Raquel Souto Rodríguez 6, Franco Baiocchi 5, María José Iglesias-Varela 5, Coral Tejido Sandoval 5, Alejandro Ledo 6, Laura Rivas Moral 5, Natalia Garcia Morales 4, Antonio Rodríguez 4, Manuel Puga 5, María Belén Castiñeira-Domínguez 6, Nereida Fernández Fernández 4, Arantxa Germade-Martínez 4, Enrique González 6, Indhira Miosotis 6, Romina Fernández Poceiro 4, Eloy Sanchez Hernandez 5, Hugo López-Fernández 2, Joaquín Cubiella 7

Affiliations

1 Research Group in Gastrointestinal Oncology, Ourense, Spain|||Galicia-Sur Public Biomedical Foundation, Ourense, Spain

2 Universidade de Vigo, Ourense, Spain|||SING Research Group, Ourense, Spain

3 Complexo Hospitalario Universitario de Ourense, Ourense, Spain

4 Hospital Álvaro Cunqueiro de Vigo - CHUVI, Vigo, Spain

5 Hospital Universitario de Ourense, Ourense, Spain

6 Hospital Montecelo - CHUP, Pontevedra, Spain

7 Research Group in Gastrointestinal Oncology, Ourense, Spain|||Hospital Universitario de Ourense, Ourense, Spain

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Abstract

Introduction

Computer-aided diagnosis (CAD) systems are widely implemented in the medical field for the analysis of medical images. PolyDeep is a CAD system that has demonstrated its efficacy in lesion detection.

Aims & Methods

The aim of this study is to determine whether PolyDeep increases the adenoma detection rate (ADR) in the context of expert endoscopists participating in a regional, population-based colorectal cancer screening program in Spain. PolyDeep Advance 3 (NCT05513261) is a multicentric, randomized controlled clinical trial. For the primary endpoint, we aimed to assess whether the ADR is superior in PolyDeep-assisted colonoscopy compared to conventional colonoscopy. As secondary endpoints, we aimed to determine whether PolyDeep-assisted colonoscopy improves the polyp detection rate (PDR), serrated lesion detection rate (SLDR), advanced serrated lesion detection rate (ASLDR), and advanced adenoma detection rate (AADR). We included participants who underwent colonoscopy for screening (i.e. those with a positive faecal immunochemical occult blood test – FIT+) or for surveillance after resection of advanced adenomas. We compared both groups using the chi-square and t-Student test.

Results

Between November 2023 and May 2024, we recruited 857 patients, of whom 30 were excluded. The baseline characteristics of the 827 patients included in the analysis were similar (male: 59.3%, screening colonoscopy: 66.7%, mean age: 61.7 ± 6.5 years, withdrawal time: 10:49 ± 04:32 minutes, colorectal cancer incidence: 2.8%). However, there were significant differences between both groups in the Boston Bowel Preparation Scale (conventional colonoscopy: 7.7 ± 1.3 vs. PolyDeep-assisted colonoscopy: 7.4 ± 1.3, p = 0.01). Regarding ADR, we did not find statistically significant differences between conventional and PolyDeep-assisted colonoscopy (57.7% vs. 60.8%, p = 0.4). Similarly, for the secondary endpoints, there were no significant differences in PDR (68.7% vs. 69.6%, p = 0.8), SLDR (32.0% vs. 29.4%, p = 0.5), ASLDR (5.3% vs. 5.1%, p = 1.0), or AADR (24.5% vs. 23.6%, p = 0.8). In Table 1, we described lesion detection rates between both groups by indication (i.e., FIT+ or surveillance).

	Screening		p	Surveillance		p
	Conventional colonoscopy	Assisted colonoscopy	p	Conventional colonoscopy	Assisted colonoscopy	p
ADR	61.8%	65.4%	0.4	50.0%	51.1%	0.9
PDR	72.1%	72.1%	1.0	62.5%	64.1%	0.9
SLDR	30.5%	28.2%	0.6	34.7%	32.1%	0.7
ASLDR	5.9%	6.8%	0.8	4.2%	1.5%	0.3
AADR	34.6%	32.1%	0.6	5.6%	5.3%	1.0

Table 1: Lesion detection rates in both groups by indication.

Conclusion

In the context of expert endoscopists PolyDeep did not increase the ADR.

Event

UEG Week Berlin 2025

Topics

Endoscopy Colorectal

Submission format

Abstract

Session

Detecting polyps with the help of AI

Citation

United European Gastroenterology Journal 2025; 13 (Supplement 8)

Published

2025

More Like This:

COMPARISON OF ENDOSCOPIC ULTRASOUND-GUIDED VERSUS PERCUTANEOUS BILIARY DRAINAGE FOR PREOPERATIVE MANAGEMENT OF MALIGNANT DISTAL BILIARY OBSTRUCTION AFTER FAILED ERCP: A MULTICENTER RETROSPECTIVE STUDY

Kenji Ikezawa

A NOVEL DEEP LEARNING MODEL FOR PREDICTING COLORECTAL ANASTOMOTIC DEHISCENCE: A PIONEER MULTICENTER TRANSATLANTIC STUDY

Manuel Limbert

POLYDEEP ADVANCE 3: MULTICENTRE RANDOMIZED CONTROLLED TRIAL COMPARING THE POLYDEEP-ASSISTED COLONOSCOPY WITH CONVENTIONAL COLONOSCOPY

Joaquín Cubiella

ARTIFICIAL INTELLIGENCE FOR COMPREHENSIVE PANCREATIC LESION PROFILING: MULTICENTER VALIDATION OF SOLID/CYSTIC LESION DETECTION/CHARACTERIZATION AND IPMN GRADING

Guilherme Macedo

Words of welcome

Julia Mayerle

Transglutaminase inhibition in CeD

Detlef Schuppan

UEG Presentation

Share via Email Share on Facebook Share on X Share on LinkedIn Share on Bluesky

ARTIFICIAL INTELLIGENCE FOR COMPREHENSIVE PANCREATIC LESION PROFILING: MULTICENTER VALIDATION OF SOLID/CYSTIC LESION DETECTION/CHARACTERIZATION AND IPMN GRADING

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ARTIFICIAL INTELLIGENCE FOR COMPREHENSIVE PANCREATIC LESION PROFILING: MULTICENTER VALIDATION OF SOLID/CYSTIC LESION DETECTION/CHARACTERIZATION AND IPMN GRADING

Miguel Mascarenhas-Saraiva 1, António Miguel Martins Pinto da Costa 2, Belén Agudo Castillo 2, Francisco Mendes 1, Jessica Widmer 3, Uzma D. Siddiqui 4, Tiago Ribeiro 1, Miguel Martins 1, Pedro Cardoso 1, Joana Mota 1, Maria Almeida 1, João Afonso 1, Grace E. Kim 4, Daniel De la Iglesia-García 2, Ana Pérez González 2, Carlos Esteban Fernández-Zarza 2, Ana Garcia Garcia de Paredes 5, Maria Moris 6, Matheus Ferreira de Carvalho 7, Marcos Eduardo Lera dos Santos 8, João Ferreira 9, Khoon Sheng Kok 10, Tamas Gonda 11, Joan B. Gornals 12, Mason Adams 13, Jack Di Palma 13, André Martins Dos Santos 14, Filipe Vilas-Boas 1, Pedro Moutinho-Ribeiro 1, Susana Lopes 1, Mariano Ganzalez- Haba Ruiz 2, Eduardo Guimarães Hourneaux de Moura 7, Guilherme Macedo 1

Affiliations

1 Centro Hospitalar Universitário de São João, Porto, Portugal

2 Hospital Universitario Puerta de Hierro Majadahonda, Madrid, Spain

3 NYU Langone Health, New York, United States

4 Center for Endoscopic Research and Therapeutics (CERT) and Advanced Endoscopy Training, University of Chicago, Chicago, United States

5 Hospital Universitario Ramon y Cajal, Madrid, Spain

6 Hospital Universitario Marqués de Valdecilla, Santander, Spain

7 Hospital das Clínicas da Faculdade de Medicina da USP, São Paulo, Brazil

8 Hospital das Clínicas da Faculdade de Medicina da USP, São Paulo, Brazil|||Hospital São Luiz Jabaquara, São Paulo, Brazil

9 Faculty of Engineering of the University of Porto, Porto, Portugal

10 Royal Liverpool Hospital, Liverpool, United Kingdom

11 Columbia University, New York, United States

12 Hospital Universitari De Bellvitge, Bellvitge Biomedical Research Institute (idibell), Barcelona, Spain

13 University of South Alabama, Mobile, United States

14 Faculdade de Medicina da Universidade do Porto, Porto, Portugal

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Abstract

Introduction

Endoscopic ultrasound (EUS) is the gold standard for evaluating pancreatic lesions, allowing for real-time imaging and biopsy sampling. Nevertheless, the differentiation between solid pancreatic lesions — such as pancreatic ductal adenocarcinoma (PDAC) and pancreatic neuroendocrine tumors (PNETs) — as well as cystic lesions is often troublesome. Specifically, identifying intraductal papillary mucinous neoplasms (IPMNs) with varying degrees of dysplasia is crucial for guiding clinical management. However, the diagnostic accuracy of current methods remains suboptimal. This multicenter study aimed to develop and validate convolutional neural networks (CNNs) to characterize solid and cystic pancreatic lesions and stratify IPMNs into high-grade dysplasia/carcinoma (HGD/C) and low-grade dysplasia (LGD).

Aims & Methods

This multicenter study included EUS images from 12 centers in Spain, Brazil, the United Kingdom and the United States of America. Solid lesion differentiation (PDAC vs. PNET) was achieved using a CNN trained on 49034 images from 95 patients (n=69 for PDAC and n=26 for PNET), with diagnoses confirmed via EUS-guided fine-needle biopsy or surgical specimen analysis. A second CNN was trained on 79395 images from 82 patients (n=64 for mucinous cystic lesions and n=18 for non-mucinous cystic lesions). Finally, a third model used the subset of IPMNs (n=51046 frames, 30 patients) to distinguish between HGD/C (n=5 patients) and LGD (n=25 patients). Histological confirmation was used as the gold standard. Performance metrics included sensitivity, specificity, accuracy, and area under the precision-recall curve (AUC-PR). To comprehensively determine the model's performance, we evaluated multiple thresholds for the confidence score and the IoU, between 0.45 and 0.55 with a step of 0.02 ([0.45, 0.47, 0.50, 0.52, 0.55]). The datasets were split into training (70%), validation (20%), and testing (10%) subsets.

Results

For solid lesions, the model differentiated PDAC from PNETs with an accuracy of 91.8%, with sensitivities of 96.2% and 97.8%, respectively. The AUC-PR was 0.982 for PDAC and 0.977 for PNET. Regarding cystic lesions the model detected mucinous cystic lesions with a sensitivity of 97.5%, an overall accuracy of 89.7% and an AUC-PR of 0.977. When the subset of IPMNs was specifically analyzed, the model distinguished IPMNs with HGD/C from those with LGD with a sensitivity of 97.5%, specificity of 94.3%, and an overall accuracy of 89.8%. The AUC was 0.988.

Conclusion

This is the first multicenter, transatlantic AI study aiming to provide a holistic approach to both solid and cystic pancreatic lesions, focusing on those with a significantly higher malignant potential. Our models achieved high accuracy in identifying and differentiating these conditions. Furthermore, to our knowledge this is the first integrative model to include the ability to stratify IPMNs into LGD and HGD/C, which has significant clinical implications, potentially reducing unnecessary surgeries and ensuring timely intervention for high-risk patients. The inclusion of data from diverse regions reduces demographic bias and enhances model generalizability. Real-time implementation of these solutions could significantly improve decision-making in EUS and optimize patient outcomes.

Event

UEG Week Berlin 2025

Topics

Digestive Oncology Pancreas

Submission format

Abstract

Session

Prediction and prognosis of pre-malignant and malignant pancreatic lesions

Citation

United European Gastroenterology Journal 2025; 13 (Supplement 8)

Published

2025

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Affiliations

1 Klinikum der LMU München-Grosshadern, München, Germany

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Abstract

Event

UEG Week Berlin 2025

Topics

Immunology

Session

Opening Plenary

Citation

United European Gastroenterology Journal 2025; 13 (Supplement 8)

Published

2025

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Share via Email Share on Facebook Share on X Share on LinkedIn Share on Bluesky

This content is part of Gutflix. Log in with your myUEG account, or create one free, to watch it.

Not sure what you can access? Learn more about account types.

Transglutaminase inhibition in CeD

Detlef Schuppan 1

Affiliations

1 Medical University Mainz - Institute of Translational Immunology, Medical University Mainz ; Mainz/D, Mainz, Germany

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Abstract

Event

UEG Week Berlin 2025

Topics

Immunology Nurses Small Intestine & Nutrition

Session

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Citation

United European Gastroenterology Journal 2025; 13 (Supplement 8)

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