moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Deep Learning Diagnostic and Risk-stratification for Idiopathic Pulmonary Fibrosis and Chronic Obstructive Pulmonary Disease in Digital Lung Auscultations
Trial ID: NCT05318599
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-04-01
Completion date: 2024-10-31
Sponsor: Pediatric Clinical Research Platform
Collaborators: University Hospital, Geneva; Swiss Federal Institute of Technology; Hôpital du Valais
Conditions: Lung; Disease, Interstitial, With Fibrosis; Pulmonary Disease, Chronic Obstructive; Artificial Intelligence
Interventions: Lung auscultation; Lung ultrasound; Quality of Life's questionnaires; Pulmonary functional tests
Locations: Centre Hospitalier du Valais Romand
Countries: Switzerland
Enrollment: 160
Age range: 18 Years - None
Sex: ALL

Summary:
Idiopathic pulmonary fibrosis (IPF), non-specific interstitial pneumonia (NSIP), and chronic obstructive pulmonary disease (COPD) are severe, progressive, irreversibly incapacitating pulmonary disorders with modest response to therapeutic interventions and poor prognosis. Prompt and accurate diagnosis is important to enable patients to receive appropriate care at the earliest possible stage to delay disease progression and prolong survival.

Artificial intelligence (AI)-assisted digital lung auscultation could constitute an alternative to conventional subjective operator-related auscultation to accurately and earlier diagnose these diseases. Moreover, lung ultrasound (LUS), a relevant gold standard for lung pathology, could also benefit from automation by deep learning.

Eligibility criteria:
Inclusion Criteria:

* Written informed consent
* age \> 18 years old.
* patients with already-diagnosed IPF (group 1) prior to the consultation (index) date.
* patients with already-diagnosed NSIP (group 2) prior to the consultation (index) date.
* patients with already-diagnosed COPD (group 3) prior to the consultation (index) date.
* Control subjects must be followed-up at the pulmonology outpatient clinic for:

  1. obstructive sleep apnoea.
  2. occupational lung diseases (miners, chemical workers, etc.).
  3. pulmonary nodules (considered benign after 2 years).

Exclusion Criteria:

* patients who cannot be mobilized for posterior auscultation.
* patients known for severe cardiovascular disease with pulmonary repercussion.
* patients known for a concurrent, acute, infectious pulmonary disease (e.g., pneumonia, bronchitis).
* patients known for asthma.
* patients known or suspected of immunodeficiency, alpha-1-antitrypsin deficit, and or under immunotherapy.
* patients with physical inability to follow procedures.
* patients with inability to give informed consent.

Outcomes:
To differentiate ILD from control subjects based on digital lung sounds recordings and LUS.; Predictive performance of the DeepBreath algorithm to stratify ILD severity based on human digital lung sounds recordings and LUS (i.e. physiological parameters) compared to grading scales.; Performance of the DeepBreath algorithm to subcategorize ILD by discriminating digital lung sounds recordings and LUS (i.e. physiological parameters).
Non-Invasive Artificial Intelligence-Based Platform MonIToring Program (NIP IT!)
Trial ID: NCT05196087
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-07-20
Completion date: 2027-06
Sponsor: University Health Network, Toronto
Collaborators: Princess Margaret Hospital, Canada
Conditions: Breast Cancer; Melanoma; Gastrointestinal Neuroendocrine Tumor
Locations: Princess Margaret Cancer Centre
Countries: Canada
Enrollment: 500
Age range: 18 Years - None
Sex: ALL

Summary:
Patients who have undergone curative treatment may be at risk of relapse. This study will collect, annotate, and sequence biospecimens (blood, stool, and tissue) from patients across different tumor types to detect molecular residual disease (MRD) before metastases become radiographically or clinically detectable. This will allow for early cancer interception, and hopefully prolong relapse-free survival across tumor types.

Eligibility criteria:
Inclusion Criteria:

1. Patients with histological confirmation of a solid tumor.
2. Patients must have early stage or locally advanced disease that is planned for or have undergone curative treatment.
3. Patient must be ≥ 18 years old.
4. All patients must have signed and dated an informed consent form.

Exclusion Criteria:

None

Outcomes:
Change from Baseline in ctDNA collected from biospecimens
Performance of AI Models in the Clinical Pathology Diagnostic Workflow: A Multicenter, Prospective, Randomized Controlled Trial
Trial ID: NCT07408167
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-03-01
Completion date: 2029-12-31
Sponsor: Nanfang Hospital, Southern Medical University
Conditions: Pathology Foundation Model
Interventions: AI model; Control
Locations: The First Hospital Affiliated to AMU SOUTHWEST HOSPITAL; Nanfang Hospital, Southern Medical University; The First Affiliated Hospital of Zhengzhou University
Countries: China; China; China
Enrollment: 2060
Age range: 18 Years - 100 Years
Sex: ALL

Summary:
The investigators plan to conduct a multicenter, prospective, randomized controlled trial to systematically evaluate the incremental value of pathology-based artificial intelligence (AI) models in a pan-disease diagnostic workflow. The study will primarily compare interpretation using an AI-assisted platform with conventional independent slide reading in terms of diagnostic accuracy (e.g., AUC), reading efficiency (e.g., diagnostic time), diagnostic report quality, diagnostic confidence (Likert scale), and pathologists' satisfaction with the AI model. Investigators will also assess superiority among less experienced (junior) pathologists and non-inferiority among more experienced (senior) pathologists. Successful completion of this project will provide high-level prospective evidence to support standardized deployment, quality control, and broader implementation of pathology AI in clinical practice. This trial may also evaluate the potential benefits and risks of using AI tools in medical research.

Eligibility criteria:
Pathologists:

Inclusion Criteria:

1. Voluntarily provide written informed consent.
2. Age ≥ 20 years.
3. Have completed at least 1 year of training in pathological diagnosis.

Exclusion Criteria:

1. Individuals with reading difficulties or a reading disorder.
2. Unwilling to participate in this study.

Patients:

Inclusion Criteria:

1. Voluntarily provide written informed consent.
2. Age ≥ 18 years.
3. Have available digital pathology images and relevant clinical information.

Exclusion Criteria:

1. Missing data or data quality not meeting the requirements for analysis.
2. Deemed unsuitable for participation by the investigator.
3. Unwilling to participate in this study.

Outcomes:
Area under ROC curve (AUC)
内镜超声穿刺胰腺实性占位细胞涂片快速染色后全玻片扫描及人工智能诊断：一项前瞻性、多中心研究
Trial ID: NCT06824909
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-12-31
Completion date: 2027-06-30
Sponsor: Ruijin Hospital
Collaborators: Second Affiliated Hospital of Soochow University; Fudan University; Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine; The Third Xiangya Hospital of Central South University; Shanghai 10th People's Hospital; Affiliated Hospital of Jiangnan University; Jiangyin People's Hospital
Conditions: Pancreatic Disease
Interventions: ROSE-AI diagnostic system
Locations: Ruijin Hospital, Shanghai Jiaotong University School of Medicine; Department of Gastroenterolog, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine
Countries: China; China
Enrollment: 1500
Age range: 18 Years - None
Sex: ALL

Summary:
The objective of this observational study is to investigate whether the self-developed whole slide scanning and artificial intelligence diagnostic system for pancreatic solid lesion puncture cytopathology (hereinafter referred to as the "Zhiying Shunxi" ROSE-AI diagnostic system) can promptly and accurately diagnose solid pancreatic lesions (SPLs). The main question it aims to answer is:

By utilizing optical imaging technology to capture RGB images of Diff-Quik stained smears from pancreatic punctures, can the development of artificial intelligence algorithms assist in differentiating solid pancreatic space-occupying diseases (such as pancreatic ductal adenocarcinoma, pancreatic neuroendocrine tumors, and non-neoplastic benign lesions)?

Researchers will compare the diagnoses of SPLs made by the ROSE-AI system with the actual pathological diagnoses of the SPLs themselves to determine whether the ROSE-AI system can effectively diagnose SPLs.

Eligibility criteria:
Inclusion Criteria:

* A dated and signed informed consent form A commitment to abide by the research procedures and cooperate throughout the entire study Subjects aged 18 and above, regardless of gender Diagnosis or suspicion of a solid pancreatic space-occupying lesion based on imaging studies (B-mode ultrasound, CT, or MRI)

Exclusion Criteria:

* Unable or refusing to sign the informed consent form Unable to suspend anticoagulation/antiplatelet therapy Pregnant or lactating Having a mental illness or other medical conditions that are unsuitable for undergoing FNA/B biopsy Presence of coagulation disorders (PLT \< 50 × 10\^3/μl, INR \> 1.5) Pancreatic cystic lesions Non-diagnostic EUS-FNA/B specimens Having less than 8 microscopic fields of interest (ROI) in the digital pathology images of the entire Diff-Quik smear slide

Outcomes:
Accuracy
Evaluation of ArTificial Intelligence System (Gi-Genius) for adenoMa dEtection in Lynch Syndrome. A Randomized, Parallel, Multicenter, Controlled Trial. TIMELY Study.
Trial ID: NCT04909671
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-09-13
Completion date: 2023-12-01
Sponsor: Hospital Clinic of Barcelona
Collaborators: European Society of Gastrointestinal Endoscopy
Conditions: Lynch Syndrome
Interventions: Gi Genius, Medtronic; White light standart colonoscopy
Locations: María Pellisé. MD. PhD.; Oswaldo ortiz
Countries: Spain; Spain
Enrollment: 404
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of this study is to assess if artificial intelligence aid colonoscopy colonoscopy is superior to conventional colonoscopy for the detection of adenomas during surveillance colonoscopy in patients with Lynch syndrome.

Eligibility criteria:
Inclusion Criteria:

* Age ≥18 years with a proven pathogenic germline variant in one of the MMR genes (MLH1, MSH2, MSH6) or Epcam deletion who are under surveillance colonoscopy.

Exclusion Criteria:

* Previous history of total colectomy,
* Concomitant inflammatory bowel disease,
* Inability or refuse to sign the informed consent,
* Previous colonoscopy \< 12 months.
* Inadequate bowel preparation.
* Incomplete procedure.
* PMS2 mutation carriers

Outcomes:
Mean number of adenomas per colonoscopy
Triple Cardiovascular Disease Detection With an Artificial Intelligence-enabled Stethoscope
Trial ID: NCT05987670
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-10-25
Completion date: 2025-12-23
Sponsor: Imperial College London
Collaborators: Imperial College Health Partners
Conditions: Heart Failure; Heart Valve Diseases; Atrial Fibrillation; Heart Murmurs; Congestive Heart Failure; Heart Failure With Reduced Ejection Fraction
Interventions: AI-stethoscope
Locations: NHS North West London Integrated Care System
Countries: United Kingdom
Enrollment: 200
Age range: 18 Years - None
Sex: ALL

Summary:
Heart failure (HF) is a condition in which the heart cannot pump blood adequately. It is increasingly common, consumes 4% of the UK National Health Service (NHS) budget and is deadlier than most cancers. Early diagnosis and treatment of HF improves quality of life and survival. Unacceptably, 80% of patients have their HF diagnosed only when very unwell, requiring an emergency hospital admission, with worse survival and higher treatment costs to the NHS. This is largely because General Practitioners (GPs) have no easy-to-use tools to check for suspected HF, with patients having to rely on a long and rarely completed diagnostic pathway involving blood tests and hospital assessment.

The investigators have previously demonstrated that an artificial intelligence-enabled stethoscope (AI-stethoscope) can detect HF in 15 seconds with 92% accuracy (regardless of age, gender or ethnicity) - even before patients develop symptoms. While the GP uses the stethoscope, it records the heart sounds and electrical activity, and uses inbuilt artificial intelligence to detect HF.

The goal of this clinical trial is to determine the clinical and cost-effectiveness of providing primary care teams with the AI-stethoscope for the detection of heart failure. The main questions it aims to answer are if provision of the AI-stethoscope:

1. Increases overall detection of heart failure
2. Reduces the proportion of patients being diagnosed with heart failure following an emergency hospital admission
3. Reduces healthcare system costs

200 primary care practices across North West London and North Wales, UK, will be recruited to a cluster randomised controlled trial, meaning half of the primary care practices will be randomly assigned to have AI-stethoscopes for use in direct clinical care, and half will not. Researchers will compare clinical and cost outcomes between the groups.

Eligibility criteria:
Inclusion Criteria:

* Primary care practices that care for adult patients and have the ability to request natriuretic peptide blood testing
* Primary care practices within the NIHR North West London Clinical Research Network or Betsi Cadwaladr University Health Board.

Exclusion Criteria:

* Poor WiFi and/or mobile data connectivity within primary care consulting rooms
* No face-to-face patient consultations

Outcomes:
Incidence of heart failure (co-primary); Ratio of route to diagnosis of heart failure (co-primary) between emergency and community-based pathways
Artificial Intelligence Versus Manual Planning in Patients Undergoing Robot-assisted Pedicle Screw Internal Fixation: a Prospective Controlled Study
Trial ID: NCT06416631
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-05-25
Completion date: 2026-05-01
Sponsor: Beijing Jishuitan Hospital
Conditions: Surgery
Interventions: artificial intelligence based screw planning; manually screw planning
Enrollment: 100
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this clinical trial is to learn if the artificial intelligence technology helps to improve the efficiency in robot assited spinal surgery. The main questions it aims to answer are:

Does the AI technology shorter the mannual planning time of screw trajectories？ Does the AI technology affect the surgical accuracy？ Researchers will compare the artificial intelligence technology to the conventional mannual planning in robotic surgery.

Participants who met inclusion criteria and do not have any exclusion criterion will be randomized to artificial intelligence or mannual planning group.

Eligibility criteria:
Inclusion Criteria:

* Admission to one of the participating centers；
* Need for and start of robotic spinal surgery
* Patients had complete medical records and imaging data;

Exclusion Criteria:

* Age less than 18 years;
* Patients with severe comorbidities;
* Patients diagnosed with tumor diseases;
* Inability to carry out the intervention (mental of physical conditions that limited participation);
* Patients with morbid obesity (body mass index \> 40);
* Missing medical records and imaging data;
* Patients with suspected or confirmed pregnancy;
* Patients participating in another RCT with the same clinical endpoint, or interventions possibly compromising the primary outcome;
* No informed consent.

Outcomes:
The accuracy of screw positioning or placement
Establishing the Use of DARWEN AI Technology in Ophthalmology and Identifying Predictors of Treatment Intervals in nAMD and RVO
Trial ID: NCT06495203
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-11-02
Completion date: 2024-02-29
Sponsor: Bayer
Conditions: Neovascular Age-related Macular Degeneration; Retinal Vein Occlusion
Interventions: anti-VEGF treatment
Locations: Bayer
Countries: Canada
Enrollment: 1350
Age range: 18 Years - None
Sex: ALL

Summary:
This is an observational study in which data already collected from people with the eye disorders below are studied. In observational studies, only observations are made without participants receiving any advice or any changes to healthcare.

Neovascular age-related macular edema (nAMD): an eye disorder caused by the lack of oxygen in the retina. The lack of oxygen leads to the increase of a protein called vascular endothelial growth factor (VEGF). VEGF causes new, weak blood vessels to grow. These vessels can leak fluid or blood into the central part of the retina at the back of the eye (macula). This leads to blurring or a blind spot in the central (straight ahead) vision needed for reading or threading a needle.

Retinal vein occlusion (RVO): an eye disorder where a blood vessel that carries blood away from the retina (vein) becomes blocked. The blocked vein causes a lack of oxygen in the retina which leads to the increase of VEGF and then vision disturbances.

These eye disorders can be treated with a type of medicine called anti-vascular endothelial growth factor (anti-VEGF). Anti-VEGF treatment helps control the growth of new blood vessels in the eye and is given via injection into the eye.

DARWEN AI is a system using artificial intelligence (AI) technology. It has been proven that DARWEN AI can be used to extract data accurately and efficiently in multiple disease areas, including breast cancer, lung cancer, ambulatory care diseases and skin disorders.

The main purpose of this study is to validate if DARWEN AI can extract and sort through information from participants' health records accurately to identify those who need more frequent injections and those who do not.

The secondary purpose of this study is to use DARWEN AI to describe the factors influencing treatment frequencies in participants with nAMD or influencing treatment discontinuation in participants with RVO.

The data will come from the participants' information stored in EyeDoc Electronic Medical Records (EMR) at St. Joseph Health Care in London. Data collected are from January 1, 2009, to May 31, 2023.

Eligibility criteria:
Inclusion Criteria:

* All patients with the diagnosis of nAMD or RVO (Branch or Central) who are patients of Dr. Tom Sheidow listed in the EyeDoc EHR with available patient records from Jan 1, 2009 to May 31, 2023.

Exclusion Criteria:

* Not applicable.

Outcomes:
Validation of DARWEN™ AI.
Evaluation of the Efficacy of Diagnostic Support Algorithms in Chest X-rays - LungAnalysis (LuAna): LuAna Stepped Wedge Trial
Trial ID: NCT06686251
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-01-05
Completion date: 2026-12-01
Sponsor: Hospital Israelita Albert Einstein
Conditions: Consolidation; Lung Injury; Pleural Effusion; Pneumothorax; Cardiomegaly; Edema Lung
Interventions: App LuAna
Locations: FEAS Curitiba
Countries: Brazil
Enrollment: 1470
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to evaluate whether the use of AI as a physician support tool is associated with an increase in the detection rate of chest radiographic findings in adults with respiratory complaints, compared to diagnosis performed exclusively by doctors, without AI support. This is a cluster-randomized clinical trial, following the stepped wedge design, and adhering to the guidelines of the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT). In this study, the Diagnostic Support Solution for Chest X-rays - LungAnalysis (LuAna), developed by the Hospital Israelita Albert Einstein (HIAE) within the PROADI-SUS Banco de Imagens, was used.

The clinical trial will be conducted in multiple centers with a diverse population from the public health system, to ensure that the algorithms are validated across a broad demographic profile. The expected benefits are significant, providing greater security for patients, increasing doctors' confidence in interpreting chest X-rays, promoting efficiency and cost savings for healthcare services, and offering promising prospects for other AI applications in imaging diagnostics.

Eligibility criteria:
Inclusion Criteria:

* Non-reported chest X-rays (XRts) of individuals aged over 18 years.
* Individuals images with respiratory complaints.
* Chest X-rays taken during the presence of these respiratory symptoms or while being followed up for respiratory disease.
* Chest X-rays taken on any X-ray machine.
* Chest X-rays that include at least one frontal view of the chest.

Exclusion Criteria:

* Those whose chest X-ray was performed due to a history of trauma, pre-operative risk assessment, lung cancer screening, or exclusively for verifying the correct positioning of a peripheral intravenous catheter (PICC).
* Chest X-rays with technical quality below the minimum required for proper interpretation and diagnosis.
* Cases without at least one frontal view.
* X-rays printed on regular paper.

Outcomes:
Detection rate
Echocardiographic Analysis by Artificial Intelligence in Hypertension
Trial ID: NCT06680986
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-06-01
Completion date: 2024-12-31
Sponsor: Hospices Civils de Lyon
Conditions: Arterial Hypertension
Interventions: Clinical cardiac parameters (Arterial pressure, number of anti-hypertensive medications, and the NT-proBNP measurements)
Locations: Hopital Lyon Sud
Countries: France
Enrollment: 200
Age range: 18 Years - None
Sex: ALL

Summary:
The objective of this study is to develop artificial intelligence (AI) tools to characterize cardiac remodelling in arterial hypertension through the analysis of routine imaging data (echocardiography) coupled with patient data. More precisely, this study will make it possible (i) to represent in a relevant way the spectrum of the cardiac repercussions of a population affected by hypertension to better characterize the progress of the pathology, in particular vis-à-vis the more ambiguous subjects to be characterized ("grey" area); (ii) to develop tools for the automatic quantification of cardiac function (segmentation and robust monitoring of the myocardium during the cardiac cycle, for the dynamic analysis of the shape and global deformation of the heart) and therefore to extract descriptors richer in cardiac function than those currently used in clinical routine.

Eligibility criteria:
Inclusion Criteria Patients who underwent a comprehensive blood pressure assessment during hospitalization in the Cardiologic Federation at Croix Rousse Hospital and Lyon Sud Hospital between January 2018 and December 2021.

Exclusion Criteria Patients who are minors. Patients under curatorship or guardianship.

Outcomes:
Arterial Blood Pressure; Number of Antihypertensive Medications; NT-proBNP Concentration