moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
A Clinical Investigation (Multicentric, Single Arm, Prospective, Stratified) to Confirm the Ability of MammoWave (Microwave Mammogram) in Breast Lesions Detection
Trial ID: NCT05300464
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-01-07
Completion date: 2027-01-31
Sponsor: Umbria Bioengineering Technologies
Conditions: Breast Cancer
Interventions: Clinical investigation medical device class IIa marked CE
Locations: IRCCS Policlinico San Martino; Ospedale San Giovanni Battista - USL Umbria 2; Az. San. Univ. Giuliano Isontina (ASU GI); Hospital Universitario de Toledo
Countries: Italy; Italy; Italy; Spain
Enrollment: 600
Age range: 18 Years - None
Sex: FEMALE

Summary:
This is a multicentric, single arm, prospective, stratified by breast density clinical investigation to confirm the ability of the microwave mammogram 'MammoWave' to detect breast lesions.

MammoWave is a innovative medical device, class IIa marked CE, which uses microwaves instead of ionazing radiation (x-ray) for breast lesions. Specifically MammoWave employs a novel technique wich generates images by processing very low power (\<1mW) microwave.

The MammoWave exam takes few minutes for breast and is performed with the patient lying in a confortable facing down position.

MammoWave is safe to be used at any age, in any condition (pregnancy, specific illness and for unlimited number of times.

Eligibility criteria:
Inclusion Criteria:

* Signed Informed consent Form
* Women
* Adult ≥ 18 years old
* Having a radiologist study output obtained using mammography within the last month. Such radiologist study output may be integrated with ultrasound and /or magnetic resonance imaging output if deemed necessary by the responsible investigator and when available. The radiological output will be integrated with histological one if deemed necessary by the responsible investigator and when available.
* Patients willing to comply with study protocol and recommendations

Exclusion Criteria:

* Patients who belong to any vulnerable group
* Patients with implanted electronic devices
* Patients who have undergone biopsy less than one week before MammoWave scan
* Patients with nipple piercings (unless they are removed prior to examination)
* Pregnancy or breastfeeding

Outcomes:
MammoWave sensitivity (number of 'true positive' results)
The Effectiveness of AI-assisted Masticatory Muscle Training on Oral Hygiene, Masticating, and Swallowing in Patients With Schizophrenia
Trial ID: NCT07154407
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-09-11
Completion date: 2027-12-31
Sponsor: Kaohsiung Medical University Chung-Ho Memorial Hospital
Conditions: Schizophrenia Disorder
Interventions: AI-assisted Mastisting Training App
Enrollment: 100
Age range: 20 Years - None
Sex: ALL

Summary:
Dysphagia seems to be quite common and potentially severe in schizophrenia, which may lead to acute asphyxia or pneumonia. Dysphagia in schizophrenia could be associated with drug-induced Parkinsonism, dystonia, tardive dyskinesia, dry mouth, excessive saliva, and other complications. Inadequate oral hygiene may lead to the accumulation of plaque, which can cause oral diseases and consequently result in tooth loss. This could be one of the significant factors affecting impaired masticating and swallowing abilities.

An experimental study with random assignment will be adopted. The participants from 2 hospitals will be assigned to two groups: experimental group (n=50), and control group (n=50). The experimental group will receive 'AI-assisted Masticatory Muscle Training' sessions, each lasting 20 minutes, before each of their three daily meals. The plaque index, Winkle tongue-coating index, dry mouth, repetitive saliva swallowing, saliva flow rate, biting force, tongue pressure, oral frailty, RSST, DDK, and oral care behaviors were assessed at baseline, as well as during the 3-month follow-ups.

Eligibility criteria:
Inclusion Criteria:

* Psychiatric Outpatient Ward Schizophrenia Diagnosed patient with 20 years or above
* Without Violence or suicidal tendencies
* With a self-owned smartphone
* Able to stabilise the IOPI machine while testing
* Able to communicate

Exclusion Criteria:

* Severe psychiatric symptoms
* With medical history which affects oral hygiene and masticating function severely (such as stroke, oral cavity-related cancer, periodontitis)
* Severe cognitive function with a score of less than 16 marks in the MoCA Test
* Indicated with Motor Neuron Disease
* Joining multiple Intervention Experiments simultaneously

Outcomes:
Anterior tongue strength; Posterior tongue strength; Masticatory Performace; Oral Hygiene; Swallowing Performance; Oral Care Behaviours; Cognitive Function
Artificial Intelligence-Guided Relaxation Interventions for Reducing Anxiety and Enhancing Coping Strategies in Patients Undergoing Hemodialysis
Trial ID: NCT07522944
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-03-29
Completion date: 2026-05-30
Sponsor: Alexandria University
Conditions: Renal Insufficiency, Chronic; Anxiety; Relaxation Therapy; Maintenance Hemodialysis; Stress, Psychological
Interventions: AI-Guided Relaxation Platform; Standard Hemodialysis Nursing Care
Locations: Faculty of Nursing, Alexandria University; Faculty of Nursing, Alexandria University
Countries: Egypt; Egypt
Enrollment: 60
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to evaluate whether a relaxation program guided by Artificial Intelligence (AI) can help reduce anxiety and improve coping mechanisms for patients receiving maintenance hemodialysis. Patients undergoing dialysis often face significant psychological stress and physical discomfort. This research uses a randomized controlled trial (RCT) with a parallel-group, pretest-posttest controlled design to compare patient well-being before and after using the AI-guided intervention.

Participants will engage with an AI system designed to provide personalized relaxation techniques during their dialysis sessions. The study uses a mixed-methods approach. Quantitative: Researchers will use standardized scales to measure changes in anxiety levels, depression, coping strategies, and perceived relaxation. Qualitative: Researchers will conduct interviews with participants to understand their personal experiences, how they engaged with the AI technology, and how it influenced their ability to manage the stress of their treatment.

Eligibility criteria:
Inclusion Criteria:

Adult patients aged 18 years or older Confirmed diagnosis of end-stage renal disease (ESRD) receiving maintenance hemodialysis for at least 3 months to ensure treatment stability Conscious, oriented, and able to communicate effectively Documented baseline anxiety at a mild-to-moderate level confirmed on initial screening using the GAD-7 or HADS prior to enrollment Adequate visual and auditory functions sufficient to interact with the AI-guided relaxation platform Willing to participate and provide written informed consent prior to enrollment

Exclusion Criteria:

History of epilepsy, chronic vertigo, or seizure disorders, as AI or VR visual stimuli may trigger adverse episodes Severe cognitive impairment or diagnosed psychiatric disorders such as dementia or schizophrenia that prevent comprehension of or adherence to study instructions Acute medical complications or hemodynamic instability occurring during the dialysis session Severe visual impairment or blindness precluding use of the digital relaxation interface Skin infections or injuries on the face or head preventing comfortable use of a headset where applicable Currently enrolled in any other psychological or relaxation intervention study

Outcomes:
Change in Generalized Anxiety Level
Cross-sectional, Multi-center Study Comparing Diagnostic Performance Between the CAD EYE System and the Physician on Histological Prediction of Colonic Polyps in Screening of Colorectal Cancer by Colonoscopy
Trial ID: NCT04921488
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-10-21
Completion date: 2023-01-25
Sponsor: University Hospital, Bordeaux
Conditions: Colonoscopy; Colorectal Neoplasms; Intestinal Polyps; Deep Learning
Interventions: screening colonoscopy
Locations: CHU d'Angers; Hopital Américain; Hôpital Saint Antoine; Hôpital Européen Georges Pompidou; Centre Hospitalier Universitaire de Bordeaux
Countries: France; France; France; France; France
Enrollment: 194
Age range: 18 Years - None
Sex: ALL

Summary:
Artificial Intelligence (AI) to predict the histology of polyps per colonoscopy, offers a promising solution to reduce variation in colonoscopy performance. This new and innovative non-invasive technology will improve the quality of screening colonoscopies, and reduce the costs of colorectal cancer screening. The aim of the study is to performed a cross-sectional, multi-center study evaluating the diagnostic performance of the CAD EYE automatic characterization system for the histology of colonic polyps in colorectal cancer screening colonoscopy.

Eligibility criteria:
Inclusion Criteria:

* Patients over 18 years of age with indication for colonoscopy as part of a screening colonoscopy, after a positive immunological test, and/or for personal or family history of colon cancer (before 60 years), personal history of colonic adenoma.
* Patient with at least one polyp detected, resect and removed during colonoscopy, for histological analysis

Exclusion Criteria:

* Guardianship or protection,
* pregnancy,
* not fluent in French or illiterate,
* lack of health care

Outcomes:
Estimation of the sensitivity of the automatic characterization system CAD EYE.
Multimodal Deep Learning Models for Predicting Recurrence Pattern in Hepatocellular Carcinoma: A Multicenter Retrospective Development and Validation Study
Trial ID: NCT07030842
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-08-25
Completion date: 2024-07-30
Sponsor: Tongji Hospital
Collaborators: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Conditions: Hepatocellular Carcinoma (HCC)
Interventions: liver resection
Locations: Tongji Hospital
Countries: China
Enrollment: 579
Age range: None - None
Sex: ALL

Summary:
This retrospective observational study aims to evaluate whether artificial intelligence (AI) models can predict aggressive recurrence in patients who underwent liver resection for early-stage hepatocellular carcinoma (HCC). The main question it seeks to answer is:

Can deep learning models combining preoperative MRI, postoperative pathology slides, and clinical data accurately identify HCC patients at high risk of aggressive recurrence after surgery?

To answer this, the investigators will analyze existing medical data (preoperative MRIs, postoperative whole-slide images, and clinical records) from 579 patients across two medical centers. All data will be anonymized before analysis, and no additional interventions are required from participants.

This study may help clinicians stratify high-risk patients who could benefit from closer surveillance or adjuvant therapies

Eligibility criteria:
Inclusion Criteria:

* Patients who underwent curative liver resection (R0) for pathologically confirmed primary HCC
* BCLC stage 0-A at diagnosis
* Availability of preoperative contrast-enhanced MRI performed within 1 month before surgery
* Availability of postoperative H\&E-stained whole slide images (WSIs) with adequate tumor representation
* Complete clinical follow-up data (minimum 2 years if no recurrence)

Exclusion Criteria:

* R1/R2 resection (micro/macroscopically positive margins)
* Missing or poor-quality preoperative MRI (motion artifacts/insufficient contrast enhancement)
* Received neoadjuvant or adjuvant therapy (to avoid treatment confounding)
* Incomplete follow-up (loss to follow-up or missing recurrence status)
* Non-curative procedures (e.g., palliative resection)

Outcomes:
Aggressive Recurrence Pattern
Effect of the Automatic Surveillance System on Surveillance Rate of Patients with Gastric Premalignant Lesions
Trial ID: NCT06039917
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-09-10
Completion date: 2029-12-31
Sponsor: Renmin Hospital of Wuhan University
Collaborators: Shanghai Pudong Hospital; The Eighth Hospital of Wuhan; Jinjiang Municipal Hospital, Shanghai Sixth People's Hospital Fujian Campus
Conditions: Artificial Intelligence; Surveillance
Interventions: AI based automatic surveillance (AS) system (ENDOANGEL-AS); Manually remind the patients
Locations: Jinjiang Municipal Hospital, Shanghai Sixth People's Hospital Fujian Campus; The Eighth Hospital of Wuhan; Renmin Hospital of Wuhan University; Shanghai Pudong Hospital
Countries: China; China; China; China
Enrollment: 1460
Age range: 18 Years - None
Sex: ALL

Summary:
In this study, we proposed a prospective study about the effect of the automatic surveillance system on surveillance rate of patients with gastric premalignant lesions. The enrolled patients were divided into group A with intelligent surveillance system, group B with manual reminder, and group C with natural state. The surveillance among the three groups will be compared.

Eligibility criteria:
Inclusion Criteria:

* Male or female aged 18 years or older who undergo upper endoscopy.

Exclusion Criteria:

* 1)No contact information or invalid contact information.
* 2\) The surveillance interval cannot be determined according to the surveillance guidelines, including no upper gastrointestinal pathology, therapeutic endoscopy or with history of previous gastrectomy, esophagectomy, or ESD, no dysplasia degrees, no biopsy sites, non-epithelial lesions, duodenal lesions, ulcer and so on.
* 3\) Needless for surveillance or others.
* 4\) High-grade intraepithelial neoplasia or cancer of the esophagus or stomach.
* 5\) Low-grade intraepithelial neoplasia of the esophagus and Barrett's esophagus.
* 6\) High-risk diseases or other special conditions for which the patient is deemed unsuitable for clinical trials by the investigator.

Outcomes:
On-time Surveillance Rate
A Post-Neoadjuvant Treatment MRI Based AI System to Predict Pathologic Complete Response for Patients With Rectal Cancer: A Multicenter, Prospective Clinical Study
Trial ID: NCT04278274
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-02-08
Completion date: 2023-03-31
Sponsor: Sixth Affiliated Hospital, Sun Yat-sen University
Collaborators: Sir Run Run Shaw Hospital; The Third Affiliated Hospital of Kunming Medical College.
Conditions: Rectal Cancer
Interventions: artificial intelligence prediction system; the radiologists
Locations: the Sixth Affiliated Hospital of Sun Yat-sen University; The Third Affiliated Hospital of Kunming Medical College; Sir Run Run Shaw Hospital
Countries: China; China; China
Enrollment: 205
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
In this study, investigators seek for a better way to identify the potential pathologic complete response (pCR) patients form non-pCR patients with locally advanced rectal cancer (LARC), based on their post-neoadjuvant treatment Magnetic Resonance Imaging (MRI) data.

Previously, a post neoadjuvant treatment MRI based radiomics AI model had been constructed and trained. Here, the predictive power of this artificial intelligence system and expert radiologist to identify pCR patients from non-pCR LARC patients will be compared in this prospective, multicenter, back-to-back clinical study

Eligibility criteria:
Inclusion Criteria:

* pathologically diagnosed as rectal adenocarcinoma
* defined as clinical II-III staging (≥T3, and/or positive nodal status) without distant metastasis
* receive neoadjuvant chemoradiotherapy or chemotherapy
* pre- and post-neoadjuvant treatment MRI data obtained
* receive total mesorectum excision (TME) surgery after neoadjuvant therapy and get the pathologic assessment of tumor response

Exclusion Criteria:

* with history of other cancer
* insufficient imaging quality of MRI to delineate tumor volume or obtain measurements (e.g., lack of sequence, motion artifacts)
* not completing neoadjuvant chemotherapy or chemoradiotherapy
* tumor recurrence or distant metastasis during neoadjuvant treatment
* not undergoing surgery resulting in lack of pathologic assessment of tumor response

Outcomes:
The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of AI prediction system and expert radiologists in prediction tumor response
Development and Real-World Validation of an Intelligent Interpretation Model for Claudin18.2 Protein Expression in Digestive System Tumors
Trial ID: NCT07274579
Status: ENROLLING_BY_INVITATION
Study type: OBSERVATIONAL
Start date: 2025-10-01
Completion date: 2028-08
Sponsor: Nanfang Hospital, Southern Medical University
Collaborators: Qianfoshan Hospital
Conditions: Digestive Oncology
Locations: Nanfang Hospital, Southern Medical University; Qianfoshan Hospital
Countries: China; China
Enrollment: 2000
Age range: 18 Years - None
Sex: ALL

Summary:
The investigators plan to develop a deep learning-based automatic interpretation model for Claudin18.2(CLDN18.2) using the institution's and multiple other centers' extensive pathological resources of digestive system adenocarcinomas. This study will not only strictly follow the latest domestic expert consensus and standards, but also aims to address current pain points in manual interpretation. It seeks to provide technical support for standardizing, objectifying, and streamlining CLDN18.2 testing, thereby advancing the application of precision medicine in the diagnosis and treatment of digestive system diseases. The project has clear clinical necessity and broad application prospects.

Eligibility criteria:
Inclusion Criteria:

1. Age over 18 years.
2. Patients who underwent CLDN18.2 immunohistochemistry and H\&E staining.
3. Availability of complete pathology reports and clinical information.

Exclusion Criteria:

1.Patients with missing data or specimens not meeting quality control requirements for analysis.

Outcomes:
Area under ROC curve (AUC)
Smartwatch-Based Artificial Intelligence Model for Obstructive Sleep Apnea Prediction
Trial ID: NCT06792188
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-02-03
Completion date: 2026-12-31
Sponsor: Seoul National University Hospital
Conditions: Obstructive Sleep Apnea of Adult; Screening; Smart Watch
Interventions: Galaxy Watch 4, Samsung Electronics Co., Ltd., South Korea
Locations: Seoul National University Hospital
Countries: South Korea
Enrollment: 147
Age range: 22 Years - 85 Years
Sex: ALL

Summary:
This study aims to develop an artificial intelligence (AI) model for more accurately diagnosing obstructive sleep apnea (OSA) by collecting blood oxygen saturation and other health information during sleep using a smartwatch.

OSA is common but often underdiagnosed, and the gold-standard diagnostic test, polysomnography, is costly and time-consuming. Smartwatches can provide a variety of health data, such as sleep patterns, blood oxygen saturation, and heart rate, which can help detect key symptoms and signs of OSA.

By developing an AI model that uses smartwatch data to screen for OSA, this study seeks to offer a cost-effective and accessible diagnostic method, ultimately contributing to the early detection and improved treatment rates of OSA.

Eligibility criteria:
Inclusion Criteria:

* Men and women aged 22 to 85 years who visited Seoul National University Hospital with suspected sleep apnea due to symptoms such as snoring, apnea, or excessive daytime sleepiness.

Exclusion Criteria:

* Patients previously diagnosed with sleep apnea who are currently undergoing treatment (e.g., positive airway pressure \[PAP\] therapy, mechanical ventilation, oral appliances, or surgery).
* Patients with neuromuscular diseases or a history of chronic opioid medication use.
* Patients with severe insomnia that is not controlled by medication.
* Patients receiving supplemental oxygen therapy due to underlying conditions such as heart failure, chronic obstructive pulmonary disease, interstitial lung disease, hypoventilation syndrome, or stroke, or whose baseline oxygen saturation is less than 90%.
* Patients with implanted cardiac pacemakers, defibrillators, or other electronic devices.
* Patients inexperienced in using smartphones, apps, or smartwatches.
* Pregnant women.
* Patients unable or unwilling to provide written informed consent.

Outcomes:
Predictive Accuracy of the AI Model for Moderate-to-Severe Obstructive Sleep Apnea
Automated Plaque Characterization and Functional Analysis of Coronary CTA Based on OCT Images Using Artificial Intelligence
Trial ID: NCT05750082
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-04-12
Completion date: 2023-12-01
Sponsor: Harbin Medical University
Conditions: Coronary Artery Disease Acute Coronary Syndrome Myocardial Ischemia Plaque Characterization
Interventions: CCTA CT-FR CAG OCT
Locations: The Second Affiliated Hospital of Harbin Medical University
Countries: China
Enrollment: 100
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
This trial is a single-center, prospective, observational clinical study. All patients who have at least one coronary artery stenosis of 30%-90% in diameter ≥ 2mm confirmed by CCTA, and who are scheduled to undergo clinically indicated invasive coronary angiography (ICA) and optical coherence tomography (OCT) evaluation and/or treatment will be eligible for enrollment. We proposed a novel approach that integrates CCTA, ICA and OCT images to automatically measure plaque characterization and calculate CT-FFR using computational fluid dynamics (CFD) simulation and artificial intelligence deep learning.

Eligibility criteria:
Inclusion Criteria:

1. Age ≥18 years and able to understand the purpose of the study and sign the informed consent;
2. Patients who presented with stable angina pectoris or acute coronary syndrome;
3. Patients who meet the indications for coronary CT angiography, percutaneous coronary angiography and intravascular imaging;
4. Among those patients, patients who have at least one coronary artery stenosis of 30% - 90% in diameter ≥ 2mm confirmed by CCTA.

Exclusion Criteria:

1. Known pregnancy or breastfeeding at the time of enrollment;
2. Hemodynamic instability;
3. Allergy to contrast media or aspirin, adenosine etc.;
4. History of stroke or transient ischemic attack (TIA) within 12 months before surgery;
5. Known renal insufficiency (e.g. serum creatinine \>2.0mg/dL, or creatinine clearance ≤30 mL/min), or need for dialysis, or acute kidney failure (as per physician judgment);
6. Leukopenia (WBC\<4.0\*10\*9/L), thrombocytopenia (PLT\<100\*10\*9/L) or thrombocytopenia (PLT\>300\*10\*9/L);
7. Subjects who receiving oral or intravenous immunosuppressant therapy (other than inhaled steroids) or have an autoimmune disease (e.g., AIDS, SLE; except diabetes);
8. Any other factors that researchers consider not suitable for inclusion or completion of this study.

Outcomes:
By taking OCT results as the standard, evaluating the accuracy of automated plaque characterization and functional significance of coronary stenosis using CTA images computation.