Clinical trial · Observational
Development of an Artificial Intelligence System for Intelligent Pathological Diagnosis and Therapeutic Effect Prediction Based on Multimodal Data Fusion of Common Tumors and Major Infectious Diseases in the Respiratory System Using Deep Learning Technology.
Research and Development of an Artificial Intelligence Technology System for Digital Pathological Diagnosis and Therapeutic Effect Prediction Based on Multimodal Data Fusion of Common Tumors and Major Infectious Diseases in the Respiratory System Using Deep Learning Technology.
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
To improve accurate diagnosis and treatment of common malignant tumors and major infectious diseases in the respiratory system, we aim to establish a large medical database that includes standardized and structured clinical diagnosis and treatment information such as electronic medical records, image features, pathological features, and multi-omics information, and to develop a multi-modal data fusion-based technology system for individualized intelligent pathological diagnosis and therapeutic effect prediction using artificial intelligence technology.
Conditions
Conditions (8)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Artificial Intelligence | — | UNRESOLVED | — |
| Covid19 | — | UNRESOLVED | — |
| Database | — | UNRESOLVED | — |
| Deep Learning | — | UNRESOLVED | — |
| Lung Cancer | Malignant Lung Neoplasm | CURATED_EXACT | 0.92 |
| Medical Informatics | — | UNRESOLVED | — |
| Pathology, Molecular | — | UNRESOLVED | — |
| Pulmonary Tuberculosis | — | UNRESOLVED | — |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (3)
- label
- Lung cancer group
- description
- Participants with lung cancer/pulmonary nodules
- label
- Pulmonary tuberculosis group
- description
- Participants with pulmonary tuberculosis
- label
- COIVD-19 group
- description
- Participants with COIVD-19
Primary outcomes (24)
- measure
- The outcome of clinical diagnosis of suspected patients with lung cancer/pulmonary nodular (Benign/Malignant nodule).
- timeFrame
- 2021-2024
- description
- The outcome of clinical diagnosis of patients with lung cancer/pulmonary nodular (Benign/Malignant nodule). ① Benign nodule ② Malignant neoplasm/nodule: squamous cell carcinoma, adenocarcinoma, small cell carcinoma, and large cell carcinoma.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
- Maximum age
- 90 Years
Show eligibility criteria text
Inclusion Criteria: 1. Participants with the clinical diagnosis of lung cancer, pulmonary tuberculosis, and COVID-19. 2. Participants that have signed informed consent. 3. Participants \>= 18 years old and \< 90 years old. 4. Participants with detailed electronic medical records, image records, pathological records, multi-omics information, and other important clinical diagnostic information. 5. Healthy participants with no clinical diagnosis of lung cancer, pulmonary tuberculosis, and COVID-19. Exclusion Criteria: 1. Participants \< 18 years old. 2. Participants with primary clinical and pathological data missing. 3. Participants lost to follow-up. 4. Participants with too poor medical image quality to perform segment and mark ROI accurately.
References
Publications (0)
Data not yet available