Automated Reporting System for Biliopancreatic Endoscopic Ultrasound
Development and Prospective Multicenter Validation of an Automated Reporting System for Biliopancreatic Endoscopic Ultrasound
This study aims to develop and prospectively validate an automated reporting system to generate structured preliminary biliopancreatic EUS reports using information collected before and during EUS examinations. System development will be completed before prospective validation, and patients included in system development will be separate from those enrolled in prospective validation.
The prospective multicenter validation will enroll 420 adults undergoing EUS for suspected or confirmed biliopancreatic disease. Each patient will contribute only the first eligible EUS examination. The primary endpoint is the difference in clinical acceptance rates between system-generated preliminary reports and independently prepared manual preliminary reports. Non-inferiority will be assessed using a predefined margin.
A nested endosonographer-reporting substudy will also compare completion time and quality between final reports prepared by endosonographers with and without assistance of a system-generated preliminary report. The study will assess the ability of automated reporting to support accurate and efficient EUS reporting while routine clinical care remains unchanged.
Descripción general del estudio
Estado
Estado
Condiciones
Condiciones
Intervención / Tratamiento
Intervención / Tratamiento
Descripción detallada
This study aims to develop and validate an automated reporting system which will generate structured preliminary reports based on preprocedural clinical data, the endosonographer's speech during EUS, EUS videos, and EUS procedural records. System development will be completed before prospective validation, using a dataset separated from the prospective validation cohort.
The prospective multicenter validation will enroll 420 adults undergoing EUS for suspected or confirmed biliopancreatic diseases. Each patient will contribute only the first eligible EUS examination as the index EUS examination. For each index examination, the automated reporting system will generate a system-generated preliminary report, while the examining endosonographer will independently generate the manual preliminary report without AI assistance. Additionally, speech-only preliminary reports and video-only preliminary reports will be generated to explore the contribution of different data inputs.
Each report will be independently assessed by two EUS experts blinded to the report source. Disagreements between the two assessors will be adjudicated by a third expert. Significant clinical corrections are defined as corrections to errors or omissions that could affect lesion localization, diagnosis, or subsequent clinical management; word and format changes alone do not qualify as significant clinical corrections. Clinical acceptance rate is defined as the proportion of all reports generated for enrolled patients that were accepted without significant clinical corrections. The primary endpoint is the difference in clinical acceptance rates between the system-generated preliminary report and the manual preliminary report of the same patient.
Additionally, a predefined nested endosonographer-reporting substudy will use the same cases to compare completion time and quality between final reports prepared by endosonographers with and without assistance of a system-generated preliminary report. Reporting tasks will be randomly assigned to endosonographers. This substudy will evaluate whether the automated reporting system can improve reporting efficiency while maintaining final report quality.
Tipo de estudio
Tipo de estudio
Inscripción (Estimado)
Inscripción
Contactos y Ubicaciones
Estudio Contacto
Estudio Contacto
- Nombre: Zhen Li
- Número de teléfono: 86+18560086106
- Correo electrónico: qilulizhen@sdu.edu.cn
Ubicaciones de estudio
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Shandong
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Jinan, Shandong, Porcelana, 250012
- Qilu Hospital of Shandong University
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Contacto:
- Zhen Li
- Número de teléfono: 86+18560086106
- Correo electrónico: qilulizhen@sdu.edu.cn
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Criterios de participación
Criterio de elegibilidad
Criterio de elegibilidad
Edades elegibles para estudiar
Acepta Voluntarios Saludables
Géneros elegibles para el estudio
Método de muestreo
Población de estudio
Adults undergoing EUS for suspected or confirmed biliopancreatic disease at participating hospitals.
Descripción
Inclusion Criteria:
- Patients aged ≥ 18years;
- Patients undergoing EUS for suspected or confirmed biliopancreatic disease;
- Patients who voluntarily sign the informed consent.
Exclusion Criteria:
- Patients who are unable to complete standard scanning due to gastrointestinal obstruction or other reasons;
- Patients who underwent surgical treatment or anatomical alterations of the pancreas due to lesions in other thoracic and/or abdominal organs, as well as patients with congenital anatomical abnormalities;
- Patients undergoing EUS primarily for therapeutic purpose such as drainage.
Plan de estudios
¿Cómo está diseñado el estudio?
Detalles de diseño
- Modelos observacionales: COHORT
- Perspectivas temporales: PROSPECTIVE
Número de grupos/cohortes
Cohortes e Intervenciones
Grupo / CohorteGrupo / Cohorte |
Intervención / TratamientoIntervención / Tratamiento |
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Adults undergoing biliopancreatic endoscopic ultrasound
Adults undergoing EUS for suspected or confirmed biliopancreatic diseases will participate in this study. Each patient provides only the first eligible EUS examination for a prospective validation of the automated reporting system. |
The system that integrates preprocedural clinical data, the endosonographer's speech during EUS, EUS videos, and EUS procedural records to generate a system-generated preliminary report. The system-generated preliminary report is generated for research validation after the routine EUS examination and is compared with the endosonographer's independent manual preliminary report. The system does not determine the patient's clinical care. |
¿Qué mide el estudio?
Medidas de resultado primarias
Medidas de resultado primarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
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Difference in clinical acceptance rates between system-generated and manual preliminary reports
Periodo de tiempo: Preliminary reports from the index EUS examination; assessment within 60 days after EUS.
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The within-patient paired difference in clinical acceptance rates, calculated as the rate for system-generated preliminary reports minus that for independently written manual preliminary reports. A report is accepted if it requires no significant clinical corrections-corrections to errors or omissions that could affect lesion localization, diagnosis or clinical management. Failed or blank system-generated reports are classified as not accepted. Non-inferiority is established if the lower limit of the two-sided 95% confidence interval is greater than -5 percentage points. |
Preliminary reports from the index EUS examination; assessment within 60 days after EUS.
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Medidas de resultado secundarias
Medidas de resultado secundarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
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Completeness of the system-generated preliminary reports
Periodo de tiempo: Index EUS examination; report assessment within 60 days.
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In the prospective paired validation, completeness is the proportion of applicable, reference-confirmed items correctly documented in each system-generated preliminary report. Items cover actual examination extent, organ findings, lesion descriptions, and sampling records. |
Index EUS examination; report assessment within 60 days.
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Number of significant clinical report corrections
Periodo de tiempo: Index EUS examination; report assessment within 60 days.
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In the prospective paired validation, the number of significant clinical corrections required per system-generated preliminary report will be assessed. Reductions and additions relative to the independent manual preliminary report will also be reported. |
Index EUS examination; report assessment within 60 days.
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Reporting time of the system-generated preliminary report
Periodo de tiempo: During the first automated report-generation task for the index EUS examination, within 30 days after EUS.
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In the prospective paired validation, time is measured from submission of all required inputs-including preprocedural clinical data, the endosonographer's speech, EUS videos, and EUS procedure records-to availability of the first system-generated preliminary report. Failed generation will be reported separately. |
During the first automated report-generation task for the index EUS examination, within 30 days after EUS.
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Reporting time of the manual preliminary report
Periodo de tiempo: During the routine reporting session immediately after the index EUS examination, up to 60 days
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Description: In the prospective paired validation, time is measured from the endosonographer beginning routine report preparation to submission of the independent manual preliminary report, without AI assistance. |
During the routine reporting session immediately after the index EUS examination, up to 60 days
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Difference in final report completion time with versus without AI assistance
Periodo de tiempo: During the assigned reporting tasks for each index EUS examination, up to 60 days
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In the randomized endosonographer-reporting substudy, the within-case difference in total time to complete a final report is calculated as unassisted time minus assisted time. Total time includes review of source materials, report generation waiting time where applicable, writing, and verification. This outcome will be formally tested only if non-inferiority of the primary outcome is established. |
During the assigned reporting tasks for each index EUS examination, up to 60 days
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Quality of final reports in the endosonographer-reporting substudy
Periodo de tiempo: Final reports from the assigned reporting tasks for each index EUS examination; assessment within 60 days after EUS.
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In the randomized endosonographer-reporting substudy, the proportion of final reports accepted without significant clinical corrections will be assessed separately for reports completed with and without access to the system-generated preliminary report. Completeness and clinically important errors and omissions will also be reported. |
Final reports from the assigned reporting tasks for each index EUS examination; assessment within 60 days after EUS.
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Colaboradores e Investigadores
Patrocinador
Patrocinador
Colaboradores
Colaboradores
Publicaciones y enlaces útiles
Publicaciones Generales
- Figueroa RL, Zeng-Treitler Q, Kandula S, Ngo LH. Predicting sample size required for classification performance. BMC Med Inform Decis Mak. 2012 Feb 15;12:8. doi: 10.1186/1472-6947-12-8.
- Nam JM. Establishing equivalence of two treatments and sample size requirements in matched-pairs design. Biometrics. 1997 Dec;53(4):1422-30.
- Julious SA, Campbell MJ, Altman DG. Estimating sample sizes for continuous, binary, and ordinal outcomes in paired comparisons: practical hints. J Biopharm Stat. 1999 May;9(2):241-51. doi: 10.1081/BIP-100101174.
- You H, Tao X, Dong Z, Li Y, Huang L, Li J, Du H, Deng M, Wu Z, Tan X, Yang T, Liu J, Yu H. Artificial intelligence assistance narrows the experience gap in endoscopic reporting of gastric lesions: a prospective clinical trial. Surg Endosc. 2026 Feb;40(2):1651-1660. doi: 10.1007/s00464-025-12410-z. Epub 2025 Dec 4.
- Tango T. Equivalence test and confidence interval for the difference in proportions for the paired-sample design. Stat Med. 1998 Apr 30;17(8):891-908. doi: 10.1002/(sici)1097-0258(19980430)17:83.0.co;2-b.
- Jiang R, Chen B, Dong Z, Zeng X, You H, Li Y, Deng Y, Mu G, Wang J, Huang L, Li J, Cheng D, Zhou W, Yu H. Domain specific multimodal large language model for automated endoscopy reporting with multicenter prospective validation. NPJ Digit Med. 2026 Mar 28;9(1):394. doi: 10.1038/s41746-026-02569-7.
- Li X, Yao L, Wu H, Tan W, Zhou W, Zhang J, Dong Z, Ding X, Yu H. A deep learning-based, real-time image report system for linear EUS. Gastrointest Endosc. 2025 Jun;101(6):1166-1173.e11. doi: 10.1016/j.gie.2024.10.030. Epub 2024 Oct 19.
- Bonmati E, Hu Y, Grimwood A, Johnson GJ, Goodchild G, Keane MG, Gurusamy K, Davidson B, Clarkson MJ, Pereira SP, Barratt DC. Voice-Assisted Image Labeling for Endoscopic Ultrasound Classification Using Neural Networks. IEEE Trans Med Imaging. 2022 Jun;41(6):1311-1319. doi: 10.1109/TMI.2021.3139023. Epub 2022 Jun 1.
Fechas de registro del estudio
Fechas importantes del estudio
Inicio del estudio (Estimado)
Inicio del estudio
Finalización primaria (Estimado)
Finalización primaria
Finalización del estudio (Estimado)
Finalización del estudio
Fechas de registro del estudio
Enviado por primera vez
Enviado por primera vez
Primero enviado que cumplió con los criterios de control de calidad
Primero enviado que cumplió con los criterios de control de calidad
Publicado por primera vez (Actual)
Publicado por primera vez
Actualizaciones de registros de estudio
Última actualización publicada (Actual)
Última actualización publicada
Última actualización enviada que cumplió con los criterios de control de calidad
Última actualización enviada que cumplió con los criterios de control de calidad
Última verificación
Última verificación
Más información
Términos relacionados con este estudio
Palabras clave
Otros números de identificación del estudio
Otros números de identificación del estudio
- 2026SDU-QILU-10
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Información sobre medicamentos y dispositivos, documentos del estudio
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