This is a multicenter, international study. The study includes 2 phases, namely, the development and test phases. In the development phase, we will collect deidentified static images and videos for the initial development of the AI algorithm. These data will be used to develop the CADx tool. In the test phase, we will validate the performance of the CADx tool using an image-based benchmark test.

Data will be collected prospectively (2023-2025) and retrospectively (2000-2024). Even though there is a chronological difference between these approaches, both follow the same inclusion and exclusion criteria for patient inclusion (Textbox 1). Data collection will be conducted as part of patients’ usual care and will not affect their treatment pathway. Data collected retrospectively will be used exclusively for the development of the CADx tool, while the data collected prospectively will be used for either the development or validation of the CADx tool.

The development of a 5-class CADx (classifying nonneoplastic lesions, low- and high-grade adenomas, SSLs, and cancers) tool requires a larger number of images than the current binary CADx classification system. As a basis for the dataset, fully anonymized data from existing image libraries established in previous studies by the investigators in Japan will be used [15,16]. The library contains >100,000 image frames collected in Japan, of which at least 10,000 images will be used for this study.

To add further diversity and volume, we will include data from another 10 European hospitals in Norway, the United Kingdom, Poland, Denmark, Sweden, Germany, and Spain. The inclusion of at least 7000 lesions from these countries is planned (Table 1), with paired pathological diagnoses obtained through resection or biopsy.

In total, 17,000 lesions will be used for machine learning. Among these, we aim to include as many T1 cancers as possible (at least 2000 lesions) to optimize the AI’s discrimination ability for this important category. Combining data from Eastern and Western populations contributes not only to greater lesion variability but also to increased ethnic diversity, both of which are indispensable for a high-performance AI model.

In the retrospective data collection, we will collect only 2 types of information, namely, endoscopic images or videos and the corresponding pathology. On the other hand, in the prospective data collection, we will also record patient demographic data and detailed polyp-level data, including sex, age, colonoscopy indication; patient ethnicity; and polyp location, size, and morphology.

A semisupervised learning methodology will be used to deliver a reliable 5-class CADx model. This process includes the following steps.

The semisupervised CADx model should be activated only when a lesion is detected. Therefore, a conventional computer-assisted detection (CADe) algorithm, developed as part of preliminary work (a YOLO version 3–based fully supervised learning model trained on 56,668 images), will be integrated into the final model [16].

To provide an interpretable output from the CNN, gradient-weighted class activation mapping (Grad-CAM) will be used (Figure 3). This method generates a heat map image in which the pixels that are important for the prediction are highlighted. Physicians can identify the mislocalization of the algorithm when the highlighted pixels are located outside the polyp. The predicted probability will also be shown for transparency. All machine learning activities will be conducted collaboratively by King’s College, Showa University, Nagoya University, and Universitat Autònoma de Barcelona, all of which have extensive experience in developing deep learning–based prediction models for colonoscopy [11,20].

Proof-of-concept validation will be performed using 250 previously unseen lesions evenly distributed across the 5 diagnostic categories to enable controlled, class-balanced benchmarking of multiclass discrimination. The performance will be evaluated primarily using sensitivity and specificity for invasive cancer detection, while overall multiclass accuracy across the 5 diagnostic categories will be reported as a secondary feasibility metric.

No clinical data will be collected on a paper format; all data are entered directly into a password-protected database enabled through the REDCap (Vanderbilt University) web-based application hosted by King’s College London University. This database will only be accessible to the trained research staff. All data entered into the database are all deidentified. If required, patient information will be accessed through the password-protected electronic health record maintained as part of the participant’s routine medical care.

Through this multicenter cohort study, we will generate a large, fully deidentified database of still images and videos. The intention is to promote further studies on AI and colonoscopy. Researchers outside this study will be able to use the open data source, which can result in diverse and innovative ideas to answer several research questions. In this database, we will include still images and videos from 1000 diverse colorectal lesions. No personally identifiable information will be included.

Findings from this study will be disseminated through peer-reviewed publications, international gastroenterology conferences, and engagement with professional societies. Where appropriate, results will be shared with regulatory bodies and the clinical guidelines development team to inform evidence-based adoption.

For the prospective data collection, eligible patients will be identified approximately 7 days before their procedure. The patient information leaflet and consent form will be sent to patients by post, email, or telephone message, according to their preference. A member of the research team will see the patient on the day of the procedure to speak to the patient and address any concerns, before obtaining the written informed consent. For the retrospective data collection, if a pre-existing consent is not available for stored images to be used for research purposes, a text will be sent to all patients informing them about the research, and an opportunity will be given to the patient to opt out within a 2-week period. Each hospital will obtain ethics approval from its research ethics committee before data collection starts. In the United Kingdom, ethics approval was granted by Health and Care Research Wales (24/LO/0176).

A proof-of-concept study will be performed when the prototype AI algorithm has been developed. The study involves benchmark testing using 250 randomly selected, previously unseen endoscopic images from 250 lesions with paired pathological diagnoses and diagnostic assessments by 10 expert endoscopists and 10 nonexpert endoscopists. The 5 categories will be equally represented, with 50 lesions selected from each category. The threshold for a successful algorithm has been set at 85%, as the maximum reported accuracy achieved by physicians for multiclass classification of colorectal polyps is approximately 80% [21].

This study is designed to develop and validate a real-time, multiclass CADx system capable of characterizing colorectal lesions into clinically relevant categories. The anticipated primary outcome is the demonstration that a 5-class classification framework can achieve robust diagnostic performance across a diverse, multinational dataset while addressing key limitations of existing binary CADx models. By incorporating low- and high-grade adenomas, hyperplastic polyps, SSLs, and early invasive cancer into a unified framework, the proposed system is expected to provide more granular and clinically actionable information at the point of care.

In addition, the integration of CADe and explainability tools is anticipated to support reliable lesion localization and enhance clinicians’ understanding of model outputs. Together, these components aim to facilitate safe and clinically appropriate application of AI-assisted diagnosis in routine colonoscopy.

Most commercially available and investigational CADx systems currently focus on binary classification of neoplastic vs nonneoplastic lesions [22], primarily to support the “resect and discard” or “leave in situ” strategies for diminutive polyps [23]. Although these approaches have demonstrated high accuracy in controlled settings [22], they provide limited guidance for more complex lesions, particularly SSLs and early cancers.

Recent studies exploring multiclass classification have shown promise but have often been constrained by small datasets, limited lesion diversity, or single-region training cohorts [24]. In contrast, our study builds on the emerging literature by incorporating a large, ethnically diverse dataset from multiple geographical regions and by including substantial representation of different lesion types. The use of semisupervised contrastive learning also extends prior work by reducing dependence on fully annotated datasets, addressing a recognized barrier to scalable AI development in endoscopy.

Furthermore, although previous CADx studies have primarily focused on diagnostic accuracy [8,10], fewer have systematically addressed interpretability and clinical integration [22]. The inclusion of Grad-CAM represents an important step toward aligning algorithmic output with clinician reasoning and guideline expectations.

A major strength of this approach is the use of a large, ethnically diverse, multinational dataset sourced from both Eastern and Western populations. This design directly addresses concerns regarding limited generalizability and dataset bias that have constrained prior CADx systems. The inclusion of a substantial number of SSLs and early colorectal cancers (T1 cancers) further strengthens the model’s relevance, given the recognized diagnostic challenges associated with these entities. Methodologically, the use of semisupervised contrastive learning represents an important advance, allowing effective use of unlabeled data and reducing reliance on exhaustive expert annotation—an acknowledged bottleneck in medical AI development.

Integration of a CADe module ensures that diagnostic predictions are contextually triggered by lesion localization, while the incorporation of Grad-CAM enhances model interpretability. This focus on explainability is particularly important for clinician trust, governance, and future regulatory acceptance of AI-enabled diagnostic systems.

Several limitations merit consideration. Proof-of-concept validation is image-based and does not fully replicate real-time procedural conditions, including motion artifacts, workflow pressures, and human-AI interaction.

Second, although expert consensus annotation is used, interobserver variability remains an inherent challenge in optical diagnosis and may influence model training. Finally, as this is a development and validation study, downstream effects on clinical decision-making, surveillance intervals, and patient outcomes cannot be assessed within the scope of the current protocol.

These limitations address the need for cautious interpretation of the findings and highlight that clinical effectiveness must be established through subsequent prospective studies.

Future work will focus on prospective real-time validation in routine clinical settings, with emphasis on workflow integration, user interaction, and safety monitoring. Additional research will be required to assess long-term outcomes, including changes in surveillance adherence, resource use, and cost-effectiveness.

This study is the first to develop a 5-class AI algorithm for real-time histological prediction of colorectal polyps, advancing beyond the binary classification used in current CADx systems. By enabling more detailed risk stratification, the tool aims to support accurate treatment decisions, including onward referral to specialized centers, and guide appropriate surveillance intervals. Trained on a large, ethnically diverse dataset from 8 countries, the algorithm is designed to be generalizable and applicable across varied clinical settings. This proof-of-concept study has the potential to enhance the utility of CADx in routine endoscopic practice by providing real-time, personalized pathology predictions to support clinical decision-making.