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Artificial Intelligence to Solve the MissINg of Gastric Cancer (AIMING)

NCT06971471 · Status: NOT_YET_RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 6600

Last updated 2025-05-14

No results posted yet for this study

Summary

Our AIMING project comprises four core work packages (WPs): WP1. Nation-level randomized controlled trial; WP2. Development of an innovative AI tool; WP3. Novel microsimulation modelling; WP4. Patient inclusion.

The nation-level multi-center tandem randomized controlled trial (WP1) will contribute to a better understanding of how the real-time AI algorithm can reduce miss rate of early gastric cancer and dysplasia during gastroscopy. Moreover, the innovation project will contribute to development of a novel AI tool (WP2) that can stratify the risk of gastric cancer by identifying in vivo precancerous conditions. Furthermore, a microsimulation modelling will allow us to predict how the use of AI can prevent gastric cancer and affect cost and patients' burdens. The assessment of the balance between benefits and harms is quite crucial especially for this type of medical device because the value of innovative tools is sometimes overestimated due to stakeholders' enthusiasm (WP3). Finally, we will take care of patients' perspective throughout the study project by including patient organization in both WP1, 2, and 3 (WP4).

Conditions

Interventions

DEVICE

Integration of Artificial Intelligence (AI) assistance to screening gastroscopy

Two novel deep learning systems, namely one for endoscopy and one for pathology, will be trained and validated for the diagnosis of gastric atrophy and metaplasia, including extension and severity. Both of the algorithms will be validated against the cases not used for the training phases. Approximately, the partition will be 5 to 1. The benefit and harm of AI-assistance for early diagnosis of gastric cancer will be simulated by developing a Markov model on the natural history of gastric cancer from dysplasia to early and advanced cancer, as well as by the impact of a GS on its natural history. This will also simulate the potential effect of lead- and length-time bias. These data will be incorporated in the simulation model in order to include them in the decision-making process on whether AI-assistance for gastric cancer detection should be or not recommended to health systems.

Sponsors & Collaborators

  • Istituto Clinico Humanitas

    lead OTHER

Study Design

Allocation
RANDOMIZED
Purpose
PREVENTION
Masking
NONE
Model
PARALLEL

Eligibility

Min Age
60 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-06-10
Primary Completion
2027-06-30
Completion
2028-06-30

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06971471 on ClinicalTrials.gov