AI to diagnose and predict outcomes of lung nodules using smartphone photos
Artificial Intelligence for Pathology Diagnosis and Prognosis Prediction of Lung Nodule Using Smartphone Photos
This will test whether an AI model can use smartphone photos of removed lung nodules to diagnose the pathology and predict prognosis for adults undergoing surgery for lung nodules.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 600 (estimated) |
| Ages | 20 Years to 75 Years |
| Sex | All |
| Sponsor | Anhui Provincial Hospital Government |
| Locations | 3 sites (Hefei, Anhui and 2 other locations) |
| Trial ID | NCT07098884 on ClinicalTrials.gov |
What this trial studies
This observational study will collect smartphone photos of resected lung nodule specimens and link those images to standard pathological diagnoses and clinical follow-up. Investigators will train a deep learning model on labeled images and then validate its performance for both pathology classification and prognosis prediction. Eligible participants are adults aged 20–75 who are scheduled for surgical removal of pulmonary lesions and have complete clinical information, while those with prior anti-tumor therapy are excluded. The work is being performed across three hospitals in China and focuses on a low-cost image input (smartphone photos) rather than specialized scanners.
Who should consider this trial
Good fit: Adults aged 20–75 who are scheduled for surgery to remove a pulmonary lesion seen on thin-section CT, have complete clinical records, and have not received prior anti-tumor therapy are ideal candidates.
Not a fit: Patients who are managed non-surgically, have incomplete clinical information, or have received preoperative anti-tumor therapy are unlikely to benefit from this approach.
Why it matters
Potential benefit: If successful, the AI could provide a rapid, low-cost tool to support pathology diagnosis and prognosis estimation from routine smartphone photos of specimens.
How similar studies have performed: AI approaches have shown promise for pathology slide images and radiology, but using smartphone photos of gross resected specimens is relatively novel and not yet widely validated.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: (1) Participants scheduled for surgery for radiological finding of pulmonary lesions from the preoperative thin-section CT scans; (2) Age ranging from 20-75 years. Exclusion Criteria: (1) Participants with incomplete clinical information; (2) Participants who have received anti-tumor therapy.
Where this trial is running
Hefei, Anhui and 2 other locations
- Anhui Provincial Hospital — Hefei, Anhui, China (Recruiting)
- The First Affiliated Hospital of Soochow University — Suzhou, Jiangsu, China (Recruiting)
- Ningbo First Hospital — Ningbo, Zhejiang, China (Recruiting)
Study contacts
- Study coordinator: Yifan Zhong, PhD
- Email: jiqingqing0229@126.com
- Phone: 0551-62283114
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.