Clinical trials: Deep Learning Model, open to volunteers
Cancer Diagnosis, Liver Cancer, Adult
Raman Spectroscopy-Based Deep Learning Model for Early Pan-Cancer Early Diagnosis
The goal of this observational study is to explore whether a Raman-based, deep learning-assisted approach can be used to develop an effective method for early pan-cancer screening.
- Conditions
- Cancer Diagnosis, Liver Cancer, Adult, Cancer Screening, Colorectal Cancer (CRC)
- Trial name
- NCT06822413
- Testing
- No Interventions
- Where
- 4 sites in China
- Volunteers
- 600 people, healthy volunteers welcome
- Sponsor
- Second Affiliated Hospital, Zhejiang University, School of Medicine
Major Adverse Cardiovascular and Cerebrovascular Events (MACCE)
Prospective Validation of DL Model for Postoperative MACCE Prediction
This study aims to prospectively validate a deep learning model developed using retrospective data to predict major adverse cardiac and cerebrovascular events (MACCE) occurring within 30 days after non-cardiac surgery.
- Trial name
- NCT07054502
- Where
- South Korea
- Volunteers
- 7,000 people
- Sponsor
- Seoul National University Hospital
BIS, BIS-EEG
Deep Learning Framework for Continuous Depth of Anesthesia Forecasting
The integration of Artificial Intelligence (AI) in anesthesiology offers the potential to shift patient monitoring from reactive to predictive.
- Conditions
- BIS, BIS-EEG, Artifical Intelligence, Intraoperative
- Trial name
- NCT07536230
- Where
- Belgium
- Volunteers
- 115 people, healthy volunteers welcome
- Sponsor
- Universitair Ziekenhuis Brussel
Hepatocellular Carcinoma (HCC)
Hybrid Deep Learning Integrating Multimodal CEUS and Enhanced MRI to Optimize Early-Stage HCC Treatment Decisions
This study aims to address the issue of a lack of individualized basis for selecting liver resection (LH) or microwave ablation (MWA) in early-stage hepatocellular carcinoma (HCC) patients to reduce the early recurrence rate (≤2…
- Trial name
- HDL-CEUS-MRI (NCT07582419)
- Testing
- surgery, ablation
- Where
- China
- Volunteers
- 1,424 people
- Sponsor
- The First Hospital of Jilin University
Skin Lesions
Human-AI Uncertainty Callibration for Improved Skin Lesion Segmentation
The goal of this randomized controlled study is to compare the effect of a new, personalized uncertainty-aware decision model (FDM) to a standard image recognition model in improving the diagnostic accuracy while reducing…
- Trial name
- NCT07468357
- Testing
- Base Model, FDM
- Volunteers
- 50 people, healthy volunteers welcome
- Sponsor
- Copenhagen Academy for Medical Education and Simulation
Intracranial Aneurysm, CT Angiography
IDEAL Study: Blinded RCT for the Impact of AI Model for Cerebral Aneurysms Detection on Patients' Diagnosis and Outcomes
This study (IEDAL study) intends to prospectively enroll more than 6450 patients who will undergo head CT angiography (CTA) scanning in the outpatient clinic.
- Conditions
- Intracranial Aneurysm, CT Angiography, Deep Learning, Double Bind Interaction
- Trial name
- IDEAL (NCT06118840)
- Testing
- True-AI-integrated intracranial aneurysms diagnosis strategy
- Where
- 21 sites in China
- Volunteers
- 6,450 people, healthy volunteers welcome
- Sponsor
- Jinling Hospital, China
Osteoporotic Vertebral Compression Fractures
CT-Based Deep Learning for Differentiating Acute and Chronic Osteoporotic Vertebral Compression Fractures
Osteoporotic vertebral compression fractures are common in older adults and may present as either acute or chronic fractures. Correctly distinguishing acute from chronic fractures is clinically important because treatment…
- Trial name
- CT-DL-OVCF (NCT07306858)
- Testing
- No Intervention (Observational Study)
- Where
- China
- Volunteers
- 276 people
- Sponsor
- Xin Fan
Postmastectomy Lymphedema Syndrome, Breast Cancer Surgery Pain
Deep Learning for Musculoskeletal Complications in Breast Cancer
Survival after breast cancer has increased due to early diagnosis and advances in treatment methods. Musculoskeletal problems related to cancer and its treatment constitute a significant part of the daily practice of physiatrists…
- Conditions
- Postmastectomy Lymphedema Syndrome, Breast Cancer Surgery Pain, Osteoporosis Secondary, Shoulder Adhesive Capsulitis
- Trial name
- NCT07236658
- Testing
- physical examination
- Where
- Turkey (Türkiye)
- Volunteers
- 133 people
- Sponsor
- Ankara Etlik City Hospital
Unruptured Intracranial Aneurysm, Intracranial Aneurysm Rupture
Prospective Observational Cohort Study for Predicting Unruptured Intracranial Aneurysm Rupture Risk Using Blood Pressure Variability and 3D Structural Evolution
This is a single-center, prospective, longitudinal observational cohort study in adults with unruptured intracranial aneurysms (UIAs) who are managed by conservative observation rather than immediate surgery or endovascular…
- Trial name
- NCT07893574
- Where
- China
- Volunteers
- 2,000 people
- Sponsor
- Beijing Tiantan Hospital
Sepsis
Prediction of Rehospitalization Following a Sepsis Admission Using a Wearable Biopatch
The goal of this observational study is to learn about the utility of biopatches predicting 30-day readmissions in patients discharged from the hospital with sepsis.
- Trial name
- NCT05806762
- Testing
- BioIntellisense
- Where
- United States
- Volunteers
- 200 people
- Sponsor
- University of California, San Diego
Clear Cell Renal Cell Carcinoma, Prognostic Cancer Model
Contrast-enhanced CT-based Deep Learning Model for Preoperative Prediction of Disease-free Survival (DFS) in Localized Clear Cell Renal Cell Carcinoma (ccRCC)
This study aims to preoperatively predict DFS of patients with localised ccRCC using a deep learning prognostic model based on enhanced contrast CT images, validate it's predictive ability in multicentre data and compare it's…
- Conditions
- Clear Cell Renal Cell Carcinoma, Prognostic Cancer Model, Recurrent Renal Cell Cancer
- Trial name
- NCT06088134
- Where
- China
- Volunteers
- 800 people
- Sponsor
- Mingzhao Xiao
Artificial Intelligence (AI), Deep Learning
The Construction and Effect Verification of a Deep Learning-based Automated Semantic Segmentation Model for Medical Imaging
Hepatocellular Carcinoma(HCC) is a common disease in China, ranking as the fourth most prevalent malignant tumor and the third leading cause of cancer-related deaths in the country.
- Conditions
- Artificial Intelligence (AI), Deep Learning, Laparoscopic Surgery
- Trial name
- NCT06864702
- Testing
- Whether the patient received diagnosis and treatment at Zhujiang Hospital of Southern Medical University and retained medical images such as abdominal
- Where
- China
- Volunteers
- 220 people, healthy volunteers welcome
- Sponsor
- Zhujiang Hospital
HCC
Neoadjuvant Therapy in Patients With Resectable HCC Screened by a Multimodal Deep Learning Model
Primary liver cancer is one of the most common malignant tumors in the world, and about 80%~90% of primary liver cancers are pathologically characterized as hepatocellular carcinoma (HCC).
- Trial name
- NCT06420440
- Testing
- Hepatic arterial infusion chemotherapy, Lenvatinib, Tislelizumab
- Where
- 2 sites in China
- Volunteers
- 160 people
- Sponsor
- Chen Xiaoping
Papillary Thyroid Carcinoma
Multimodal Deep Learning for Lymph Node Metastasis in Thyroid Cancer
Papillary thyroid carcinoma (PTC) is the most common endocrine malignancy in clinical practice, accounting for approximately 85% of all thyroid malignancies.
- Trial name
- NCT07299318
- Testing
- not intervention
- Where
- China
- Volunteers
- 3,200 people
- Sponsor
- West China Hospital
Anterior Segment Diseases
Multimodal Deep Learning Model for Multi-task Diagnosis and Triage Suggestions of Ophthalmic Diseases
Accurate and comprehensive interpretation of anterior segment diseases from slit-lamp and smartphone photographs remains a clinical challenge due to the limited specificity and structure of existing Artificial Intelligence tools.
- Trial name
- NCT07447973
- Testing
- Multimodal Vision-language Model Diagnosis
- Where
- China
- Volunteers
- 2,000 people, healthy volunteers welcome
- Sponsor
- Guangdong Provincial People's Hospital
Pulmonary Nodules
Multicenter Prospective Validation of AI Models for Malignancy Risk Prediction in Pulmonary Nodules
This multicenter prospective diagnostic accuracy study will compare the performance of three artificial intelligence (AI) models (MVCS, LungDoc, and a United Imaging AI model) for predicting the malignancy risk of pulmonary…
- Trial name
- NCT07727122
- Where
- 5 sites in China
- Volunteers
- 3,000 people
- Sponsor
- Guangdong Provincial People's Hospital
Patient Readmission
Predicting Hospital Readmission for Surgical Patients Using Deep Learning Models With Smart Watch and Smart Ring Sensors Data
Hospital readmissions are an important measure of healthcare quality and safety. These events create a substantial burden for patients, families, and health systems because they may increase costs, extend recovery time, and lead…
- Trial name
- NCT07349901
- Where
- Brazil
- Volunteers
- 300 people
- Sponsor
- Getúlio Vargas University Hospital
Deep Learning, Head & Neck Squamous Cell Carcinoma
A Bidirectional Study of Individualized Postoperative Adjuvant Treatment Decision Model for Locally Advanced Head and Neck Squamous Cell Carcinoma Based on Multimodal Dynamic Data
Based on multimodal data, the investigators establish a dynamic deep learning model to conduct prognostic risk assessment for patients and recommend the 'most suitable' treatment/follow-up regimen after radical treatment…
- Trial name
- NCT07532928
- Where
- China
- Volunteers
- 571 people
- Sponsor
- Nanfang Hospital, Southern Medical University
HCC
Efficacy and Safety of Neoadjuvant Therapy in Patients With Resectable HCC Screened by a Multimodal Deep Learning Model.
Primary liver cancer is one of the most common malignant tumors in the world, and more than 90% of primary liver cancers are pathologically characterized as hepatocellular carcinoma (HCC).
- Trial name
- NCT06311916
- Testing
- HAIC + Tirelizumab +lenvatinib +liver resection, liver resection
- Volunteers
- 312 people
- Sponsor
- Chen Xiaoping
Gastric (Stomach) Cancer
Development of a Predictive Model for Gastric Cancer Peritoneal Metastasis and Cachexia Using BUB1 and Radiopathomics Data With Deep Learning
This clinical trial aims to develop a predictive model for gastric cancer (GC) peritoneal metastasis and cachexia by integrating BUB1 gene data with radiological and pathological data using advanced deep learning techniques.
- Trial name
- BUDDLE (NCT06858644)
- Testing
- BUB1-Integrated Deep Learning Model for Gastric Cancer Metastasis and Cachexia Prediction
- Volunteers
- 500 people
- Sponsor
- Qun Zhao