moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Cutting-edge IMAGING Technologies to Improve the SAFEty and the Sustainability of LUNG Cancer Screening and the Accuracy of Non-invasive Lung Nodules Characterization
Trial ID: NCT06963515
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-06-20
Completion date: 2028-06-20
Sponsor: IRCCS San Raffaele
Conditions: Lung Cancer
Interventions: Magnetic Resonance Imaging
Enrollment: 250
Age range: 18 Years - 100 Years
Sex: ALL

Summary:
Low-dose computed tomography (LDCT) is used in individuals at high risk of developing lung cancer (smokers over 50 years of age), as it allows for the identification of pulmonary nodules, which, in a small percentage of cases, may represent early-stage lung cancer. However, according to LUNG-RADS guidelines, individuals undergoing screening must repeat multiple LDCT scans, as the comparison between successive LDCT scans enables the assessment of existing nodules' progression and the identification of newly developed pulmonary nodules. This results in cumulative exposure to ionizing radiation, increasing the risk of radiation-induced cancers.

This study addresses, through the implementation of new imaging techniques utilizing the latest and most advanced technological innovation (high-field 3T Magnetic Resonance Imaging (MRI) with artificial intelligence), the critical challenge of reducing radiation exposure in current LDCT-based screening programs, proposing the use of MRI as an alternative screening method to LDCT.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18 years
* Indication to first lung cancer screening with LDCT according to international guidelines
* Indication to repeated lung cancer screening with LDCT according to international guidelines

Exclusion Criteria:

* Age \< 18 years
* contraindication to MRI
* presence of metallic implants likely to hamper MRI image quality
* inability to hold breath for 15-20 seconds
* claustrophobia
* refusal to participate in the study

Outcomes:
the sensitivity and specificity for lung nodule detection of a Single Breath- Hold Lung non-contrast MRI scan (SBH-Lung-MRI), lasting less than 20 seconds, in comparison to LDCT
Precision-Medicine Diagnostic Support in Hospitalized Veterans With Acute Kidney Injury
Trial ID: NCT07217808
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2028-01-01
Completion date: 2029-06-30
Sponsor: VA Office of Research and Development
Collaborators: VA Tennessee Valley Health Care System; Vanderbilt University Medical Center
Conditions: Acute Kidney Injury (AKI)
Interventions: PRECISE-AKI Clinical Decision Support Tool
Locations: Tennessee Valley Healthcare System Nashville Campus, Nashville, TN
Countries: United States
Enrollment: 60
Age range: 18 Years - None
Sex: ALL

Summary:
Acute kidney injury (AKI) affects up to 20% of hospitalized Veterans and is strongly associated with morbidity and death. AKI is a diverse condition and timely and accurate diagnosis of the type of AKI is critical to begin appropriate therapies, especially those causes that require specific treatments beyond general supportive care. Yet, there are still significant gaps in the initial evaluation of AKI among hospitalized patients. Clinical decision support systems (CDSS) have shown promise to address these barriers, but most consist of simple alerting schemes and general care recommendations provided at a single point in time. The goal of this proposal is to develop and test the feasibility and usability of a rule-based and Artificial Intelligence-assisted precision CDSS tool (PRECISE-AKI) that can provide cognitive support to improve timely initial diagnostic evaluation of AKI.

Eligibility criteria:
Inclusion Criteria:

* Eligible providers will be those that have at least 4 weeks of inpatient ward team or consultation team activity during a 12-month period.
* Case inclusion criteria will consist of hospitalizations that meet criteria for Kidney Disease Improving Global Outcomes Stage 2 injury or persistent Stage 1 injury seen by eligible providers.

Exclusion Criteria:

* Ineligible providers will be those that have \< 4 weeks of inpatient ward team of consultative team activity during a 12 month period.
* Hospitalizations with non-persistent (\<48 hours) stage 1 injury or less.

Outcomes:
Rates of Appropriate Diagnostic Testing
Research of the Application of Pancreatic Cancer Screening Artificial Intelligence Model 'PANDAPro': A Single-Center,Realworld Clinical Trial
Trial ID: NCT06643715
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-11-18
Completion date: 2027-08-01
Sponsor: Zhejiang University
Collaborators: Lishui Municipal Central Hospital; Second Affiliated Hospital of Nanchang University
Conditions: Pancreatic Cancer
Interventions: PANDA Pro
Locations: the First Affiliated Hospital, School of Medicine, Zhejiang University
Countries: China
Enrollment: 100000
Age range: 18 Years - 90 Years
Sex: ALL

Summary:
The purpose of this study is to build upon the previously developed deep learning-based non-contrast CT pancreatic cancer screening model, PANDA. The model will first undergo training and enhancement, followed by external validation across multiple centers. Subsequently, a large-scale real-world validation will be conducted at Zhejiang University's First Affiliated Hospital , the study will be divided into two rounds. In the first round, the performance of the PANDA model and the upgraded PANDA Pro model will be compared on consecutive retrospective real-world CT scans. In the second round, physicians will record the PANDA Pro results in real time to identify potential pancreatic lesions that may have been clinically missed. By leveraging clinical big data across different scenarios at Zhejiang University's First Affiliated Hospital, the study aims to validate the model's role in prompting and supplementing the diagnosis of PDAC in clinical practice, thereby laying the foundation for large-scale opportunistic screening of PDAC.

Eligibility criteria:
Inclusion Criteria:

* Subjects who have undergone chest and/or abdominal CT scans at outpatient clinics, inpatient departments, or physical examination centers;
* Age at the time of the scan between 18-90 years old, with no restriction on gender;

Exclusion Criteria:

* Chest CT scans that do not cover the pancreas;
* Non-contrast CT scans performed in emergency settings;
* Patients who have undergone thoracic/abdominal surgeries affecting or altering the anatomical display of the pancreas (e.g., post-esophageal, gastric, pancreatic, vascular surgeries, or post-ERCP);
* Non-standard scans (e.g., hands placed on either side of the body or abdomen, severe respiratory motion artifacts, perfusion contamination, etc.);
* CT scans ordered by hepatobiliary and pancreatic surgeons or oncologists;
* Patients referred to a higher-level hospital due to a pancreatic mass found during local hospital examination;
* Patients who, for personal reasons, did not follow up with pancreatic cancer diagnosis or treatment at the hospital, or were lost to follow-up midway;
* Patients with concurrent malignancies in other locations or those undergoing comprehensive cancer treatment for malignant tumors;
* Imaging reports made by radiologists without referring to AI during the image interpretation;
* Patients who underwent enhanced CT, MRI, or PET-CT examinations concurrently.

Outcomes:
Detection efficiency of doctors in pancreatic cancer assisted by PANDA Pro
Clinical Efficacy Evaluation of a Computer-aided Colonoscopy as Compared With the Standard Colonoscopy.
Trial ID: NCT05240625
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-03-01
Completion date: 2025-12-31
Sponsor: National Taiwan University Hospital
Collaborators: Changhua Christian Hospital; Shin Kong Hospital; Fu Jen Catholic University Hospital; aetherAI Co., Ltd
Conditions: Colon Adenoma; Colorectal Cancer
Interventions: "aetherAI" Computer-aided Polyp Detection (CADe) Systems for Colonoscopy; Standard colonoscopy
Locations: National Taiwan University Hospital
Countries: Taiwan
Enrollment: 1500
Age range: 40 Years - 80 Years
Sex: ALL

Summary:
Colonoscopy is clinically used as the gold standard for detection of colorectal cancer (CRC) and removal of adenomatous polyps of the colon and rectum. Evidence has shown that CRC could be prevented by colonoscopic removal of adenomatous polyps. Despite the success of colonoscopy in reducing cancer-related deaths, there exists a disappointing level of adenomas missed at colonoscopy. In recent years, emerging artificial intelligence (AI) and computer-aided detection (CADe) technology has been shown to improve ADR. Based on a meta-analysis, ADR was demonstrated to be significantly higher in the CADe groups than in the standard colonoscopy groups, representing a relative risk of 25.2%.

In this study, performance of colonoscopy with or without aid of CADe will be compared in terms of quality indicators. The adenoma detection rate (ADR), which is the proportion of average-risk patients undergoing screening colonoscopy in whom an adenoma is found, is regarded as a robust measure of colonoscopy performance quality that correlates with subsequent cancer risk. Thus, ADR is taken as the primary outcome of this study. The target population includes individuals who are undergoing screening, diagnostic, or surveillance colonoscopy.

Eligibility criteria:
Inclusion Criteria:

* Subjects who have given signed informed consent form
* Informed consensus has been obtained that endoscopic resection should be performed if a lesion is found
* Subjects who are scheduled for screening or diagnostic colonoscopy for colorectal cancer (CRC) or surveillance colonoscopy for post-polypectomy follow-up

Exclusion Criteria:

* Subjects with any of the following prior history or current conditions:

  1. Contraindications to colonoscopy
  2. Inflammatory bowel disease (IBD)
  3. Colorectal cancer (CRC)
  4. Familial adenomatous polyposis (FAP)
  5. Colonic stenosis
  6. Severe organ failure (cirrhosis of Child C, heart failure of ACC / AHA stage D)
  7. Active gastrointestinal (GI) Bleeding
  8. Pregnancy
  9. Prior colorectal surgery, including colonic or rectal resection (except for appendectomy, surgery on the anus, and polypectomy)
  10. Undergo colonoscopy within 3 years
* Subjects with any of the following conditions per the investigator's judgement:

  1. High suspicion of IBD, CRC, and FAP.
  2. High risk of bleeding after endoscopic treatment, and difficult management of anticoagulation or antiplatelet medication.

Outcomes:
Adenoma detection rate
Research on the Auxiliary Diagnosis and Treatment System of Digestive Endoscopy Based on Artificial Intelligence: An Efficacy Study of Artificial Intelligence-assisted Colonic Polyp Detection System
Trial ID: NCT05941689
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-07-25
Completion date: 2023-09-30
Sponsor: Xiangya Hospital of Central South University
Conditions: Adenoma Colon
Interventions: AI-assisted colonoscopy
Locations: Xiangya Hospital Central South University; Loudi Central Hospital
Countries: China; China
Enrollment: 1906
Age range: 18 Years - 85 Years
Sex: ALL

Summary:
This is a randomized controlled multicenter clinical trial of computer-aided detection (CADe) system for the adjuvant diagnosis of intestinal polyps/adenomas ever conducted in a Chinese population. In addition, this study will evaluate the effect of CADe system on adenoma detection of endoscopists under fatigue.

Eligibility criteria:
Inclusion Criteria:Patients who need to undergo colonoscopy. -

Exclusion Criteria:Patients with CRC, inflammatory bowel disease, previous colonic resection, and antithrombotic therapy precluding polyp resection were excluded.

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Outcomes:
adenoma detection rate; withdrawal time compliance rate
Artificial Intelligence Based Models for Primary Sjögren's Syndrome Diagnosis Using Laboratory Data: A Chinese Multicenter Retrospective Study
Trial ID: NCT06982482
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2013-01-01
Completion date: 2025-01-01
Sponsor: The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School
Conditions: Primary Sjögren&#39;s Syndrome (pSS)
Locations: Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University
Countries: China
Enrollment: 27432
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
The goal of this observational study is to develop and validate artificial intelligence (AI)-driven models for improving the diagnosis of Primary Sjögren's Syndrome (PSS) using routine laboratory test data. The main question it aims to answer is:

Can AI-based algorithms accurately diagnose Primary Sjögren's Syndrome by analyzing laboratory test results, and do they outperform traditional diagnostic criteria in Chinese populations?

Researchers will retrospectively analyze anonymized clinical records and laboratory data (e.g., autoantibody levels, inflammatory markers) from patients with suspected or confirmed PSS across multiple medical centers in China. No new interventions will be administered, as the study utilizes existing historical data to train and validate the AI models. The performance of AI algorithms will be compared with current diagnostic standards (e.g., ACR/EULAR criteria) in terms of sensitivity, specificity, and clinical utility.

Eligibility criteria:
Inclusion Criteria:

* Patients with clinician-diagnosed primary Sjögren's syndrome (pSS) meeting the 2016 ACR/EULAR or 2002 ACEG classification criteria (objective oral/ocular dryness, positive anti-SSA/Ro antibodies, or focal lymphocytic sialadenitis on biopsy).
* Control groups: Individuals with non-pSS autoimmune diseases (e.g., rheumatoid arthritis, systemic lupus erythematosus) or non-autoimmune conditions (e.g., dry eye/sicca symptoms without systemic autoimmunity).

Exclusion Criteria:

* Pregnancy, breastfeeding, with a clear diagnosis of other autoimmune diseases, severe infection and malignant tumors.
* Not newly diagnosed in any of the hospitals.
* Without any available laboratory tests.

Outcomes:
Diagnostic Accuracy of AI Models for Primary Sjögren's Syndrome (pSS)
PET/MRI Artificial Intelligence Reconstruction Algorithm AIR Recon DL Image Quality Evaluation and Clinical Study
Trial ID: NCT06856096
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-02-10
Completion date: 2027-01-15
Sponsor: Xijing Hospital
Conditions: Parkinson&#39;s Syndrome
Interventions: PET/MRI
Locations: Department of Nuclear Medicine,Xijing Hospital, Fourth Military Medical University, Xi'an, China, Xi'an, Shaanxi Province
Countries: China
Enrollment: 65
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
PET/MRI artificial intelligence reconstruction algorithm AIR Recon DL image quality evaluation and clinical study

Eligibility criteria:
Inclusion Criteria:

* 1\. Adults between the ages of 18 and 75 who are autonomous; 2. persons with good compliance; 3. those who are mentally alert and able to move around on their own; 4. Those who agree to participate in this clinical trial and sign the informed consent form.

Exclusion Criteria:

* 1\. Persons who do not have full capacity for civil behaviour; 2. Women during pregnancy and breastfeeding; 3. other implants, prostheses, foreign bodies and patches with electronic implants such as pacemakers, insulin pumps and cochlear implants that are not suitable for MRI; 4. patients with claustrophobia; 5. those who, in the opinion of the investigator, are not suitable for participation in this clinical trial.

Outcomes:
Signal to Noise Ratio; Contrast to Noise Ratio
Establishment, Verification and Clinical Application of Chinese Version of Surgical Risk Assessment System
Trial ID: NCT05754268
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-01-01
Completion date: 2024-12-31
Sponsor: luyun
Conditions: Risk Assessment; Risk Factors; Machine Learning; Artificial Intelligence; Postoperative Complications; Operative Surgical Procedure
Interventions: Chinese version of the surgical risk assessment system
Locations: Yiheng Ju
Countries: China
Enrollment: 10000
Age range: None - None
Sex: ALL

Summary:
The goal of this observational study is to establish and verify the Chinese version of surgical risk assessment system and explore its clinical application. The main questions it aims to answer are: The process of establishing a Chinese version of surgical risk assessment system; What is the accuracy of the system; How can the system be used in clinic; How does this system compare with other systems (such as NSQIP). Participants will comprehensively collect the general information, examination and pathological information of the patients, using machine learning and artificial intelligence methods for data processing. Finally, the Chinese version of the surgical risk assessment system will be established. After the system is established, investigators will evaluate the accuracy of the system and compare it with other related systems.

Eligibility criteria:
Inclusion Criteria:

* Preoperative colonoscopy and biopsy confirmed gastric and colorectal cancer.
* Laparotomy or laparoscopic radical resection of gastric or colorectal cancer.
* No postoperative complications such as pneumonia, venous thrombosis, cardiovascular and cerebrovascular accidents occurred before operation.
* There was no serious organ dysfunction before operation.
* Complete auxiliary examination and the availability of examination and examination data were carried out before operation.

Exclusion Criteria:

* Complicated with malignant tumors of other organs.
* Unresectable tumors.
* Distant metastasis.
* Severe organ system dysfunction before operation.
* Patients with non-operative treatment.

Outcomes:
The occurrence of complications
Usability and Clinical Effectiveness of an Interpretable Deep Learning Framework (VAE-MILP) Using Counterfactual Explanations and Layerwise Relevance Propagation Framework for Post-Hepatectomy Liver Failure Prediction
Trial ID: NCT06031818
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-12-10
Completion date: 2024-03-15
Sponsor: Maastricht University
Collaborators: First Affiliated Hospital, Sun Yat-Sen University
Conditions: Post-hepatectomy Liver Failure; Hepatocellular Carcinoma; Artificial Intelligence
Interventions: The explanation of deep learning framework (VAE-MLP) , including counterfactual explanations and layerwise relevance propagation; The model prediction; The model prediction and the explanation of deep learning framework (VAE-MLP) , including counterfactual explanations and layerwise relevance propagation
Locations: The First Affiliated Hospital of Sun Yat-Sen University
Countries: China
Enrollment: 80
Age range: None - None
Sex: ALL

Summary:
The goal of this in-silico clinical trial is to learn about the usability and clinical effectiveness of an interpretable deep learning framework (VAE-MLP) using counterfactual explanations and layerwise relevance propagation for prediction of post-hepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC). The main questions it aims to answer are:

* To investigate the usability of the VAE-MLP framework for explanation of the deep learning model.
* To investigate the clinical effectiveness of VAE-MLP framework for prediction of post-hepatectomy liver failure in patients with hepatocellular carcinoma.

In the usability trial the clinicians and radiologists will be shown the counterfactual explanations and layerwise relevance propagation (LRP) plots to evaluate the usability of the framework.

In the clinical trial the clinicians and radiologists will make the prediction under two different conditions: with model explanation and without model explanation with a washout period of at least 14 days to evaluate the clinical effectiveness of the explanation framework.

Eligibility criteria:
Inclusion Criteria:

1. patients with treatment-naive and resectable HCC;
2. performance status Eastern Cooperative Oncology Group (PS) score 0-1.

Exclusion Criteria:

1. liver resection was not performed;
2. pathological diagnosis of non-HCC;
3. failure in liver stiffness measurement defined as the elastography color map was less than 75% filled or interquartile range (IQR)/median \> 30%;
4. immune-active chronic hepatitis indicated by an elevation of alanine aminotransferase (ALT) levels ≥ 2×upper limit of normal (ULN);
5. obstructive jaundice or dilated intrahepatic bile ducts with a diameter of \>3 mm;
6. hypoalbuminemia, hyperbilirubinemia, or coagulopathy not related to the liver.

Outcomes:
Clinical effectiveness of the explanation framework
A Prospective, Randomized, Controlled, Crossover Study of Artificial Intelligence-Assisted Multi-Dimensional Staging and Treatment Decision-Making for Hepatocellular Carcinoma
Trial ID: NCT07538882
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-04-13
Completion date: 2026-05-20
Sponsor: Beijing Tsinghua Chang Gung Hospital
Collaborators: Affiliated Hospital of Hebei University; Meng Chao Hepatobiliary Hospital of Fujian Medical University; Zhongnan Hospital; Xingtai People's Hospital; Second Affiliated Hospital of Xi'an Jiaotong University; Xinan hospital of Army Medical University
Conditions: Hepatocellular Carcinoma (HCC)
Interventions: Unassisted Independent Evaluation; AI-Assisted Evaluation
Locations: Beijing Tsinghua Changgung Hospital
Countries: China
Enrollment: 108
Age range: 18 Years - None
Sex: ALL

Summary:
The precise treatment of primary hepatocellular carcinoma (HCC) highly depends on accurate disease staging (CNLC, TNM, BCLC) and scientific treatment decision-making, which necessitate the integration of both imaging and clinical baseline data. This study prospectively recruits HCC patients and clinical physicians across different hospital tiers to evaluate the clinical value of a self-developed artificial intelligence (AI) model in assisting multi-dimensional comprehensive assessment and treatment decision-making. Utilizing a Multi-Rater Multi-Case (MRMC) crossover balanced design, the study compares the accuracy of clinical evaluations performed by physicians under "unassisted (without AI)" versus "AI-assisted" conditions. A key focus is to explore whether AI can significantly enhance the comprehensive assessment capabilities of physicians in primary/secondary care hospitals, thereby prospectively reducing diagnostic and therapeutic heterogeneity across different institutional levels.

Eligibility criteria:
Inclusion Criteria:

* Age \>= 18 years.
* Patients prospectively presenting with suspected or newly diagnosed primary hepatocellular carcinoma (HCC) later confirmed by pathology or meeting the China Liver Cancer (CNLC) guidelines.
* Complete baseline clinical data acquired during the prospective enrollment period, including complete history of present/past illness, ECOG PS score, comprehensive laboratory tests (liver function, coagulation, tumor markers such as AFP, etc.), and baseline abdominal contrast-enhanced CT.
* Patients (or their legal representatives) must provide written informed consent for their clinical data to be used in this trial.

Exclusion Criteria:

* Patients with secondary (metastatic) liver cancer or concurrent severe malignancies of other systems.
* Patients who fail to complete the required baseline imaging or laboratory tests, preventing accurate staging calculation (e.g., missing data for Child-Pugh score).
* Patients who have previously received anti-tumor therapies for liver cancer prior to enrollment.

Outcomes:
Improvement in Overall Accuracy