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AI Determine Malignancy of GGO on Chest CT

NCT06282068 · Status: ENROLLING_BY_INVITATION · Type: OBSERVATIONAL · Enrollment: 100

Last updated 2024-02-28

No results posted yet for this study

Summary

Research Objectives To use AI computer-aided detection software to assist physicians in reading CT scans of lung nodules, providing auxiliary diagnostic tools for medical decision-making. The software can mark nodule locations and related information during routine physician reading. This study will obtain prospective consent to use patient CT images for software reading and compare with clinical physician diagnosis, in order to enhance software training and improve recognition of lung lesions for early diagnosis and treatment.

Study Design Collect CT images of untreated lung nodules 4-30mm in size that are scheduled for surgery. No limits on age, gender, disease type, with image resolution \<2.5mm. AI and clinicians will judge nodule characteristics separately. Surgical resection followed by comparison with pathology reports will evaluate diagnostic accuracy.

Study Procedures A double-blinded method will be used. AI and physicians will record nodules as likely benign or malignant separately. After surgical resection, the lesions will undergo pathological staging and the diagnostic accuracy of both groups will be compared.

Expected Results Compare the diagnostic accuracy of AI and clinicians to improve AI training quality, achieve early diagnosis and treatment goals, and provide patients with better medical care quality.

Monitoring Method AI and clinicians will read separately, adhering to shared decision making without affecting patient access to diagnosis and treatment.

Keywords: lung nodules, early lung cancer, artificial intelligence, chest CT, minimally invasive surgery, lung image analysis software

Conditions

  • Lung Nodules, Early Lung Cancer, Artificial Intelligence, Chest CT, Minimally Invasive Surgery, Lung Image Analysis Software

Interventions

DIAGNOSTIC_TEST

AI computer-aided detection software

non-invasive

Sponsors & Collaborators

  • Chung Shan Medical University

    lead OTHER

Eligibility

Min Age
20 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-03-01
Primary Completion
2026-02-28
Completion
2026-02-28

Countries

  • Taiwan

Study Locations

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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 NCT06282068 on ClinicalTrials.gov