moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Using Artificial Intelligence (AI) - Enhanced Social Robots to Improve Children's Healthcare Experiences
Trial ID: NCT06556329
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-08-15
Completion date: 2025-05-15
Sponsor: University of Alberta
Collaborators: University of Toronto
Conditions: Social Robots for Procedural Distress
Interventions: Robot (Intervention)
Locations: Stollery Children's Hospital; The Hospital for Sick Children
Countries: Canada; Canada
Enrollment: 95
Age range: 5 Years - 11 Years
Sex: ALL

Summary:
Children experience pain and distress in clinical settings every day. The negative consequences of unaddressed pain can be both short-term (e.g. fear, distress, inability to perform procedures) and long-term (e.g. needle phobia, anxiety).

In previous small studies, a humanoid robot has been used to deliver cognitive-behavioural therapy during needle procedures. The results of these early studies have been positive, showing high acceptance among children as well as promising initial clinical results. However, these studies all had critical technical limitations: the robot was remotely operated and used purely scripted behaviour with limited Artificial Intelligence support. This reduced the potential to offer personalized support to children. In this project, the study team aims to address this limitation by developing and evaluating a clinically relevant and responsive artificial intelligence-enhanced social robot.

Eligibility criteria:
Inclusion Criteria:

1. child aged 5-11 years
2. requires IVI
3. will receive topical anesthetic for IVI

Exclusion Criteria:

1. medically unstable (CTAS 1, requiring immediate IV insertion)
2. unconscious or not fully alert
3. visual or auditory impairments
4. neurocognitive delays or mental health issues precluding safe interaction with the robot intervention
5. sensory impairment to pain (ex. spina bifida)
6. parental or child language barrier precluding the ability to understand and complete study assessments, in the absence of a native language translator
7. not accompanied by legal guardian
8. previous enrolment in this clinical trial or in the co-design or usability studies
9. other reason at the discretion of the clinical staff

Outcomes:
Self-reported pain level; Self-reported distress level
A Multicenter Clinical Study on AI-Powered Precision Decision-Making Management for Pancreatic Diseases Using Contrast-Enhanced CT
Trial ID: NCT07439757
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-03-01
Completion date: 2029-10-31
Sponsor: Changhai Hospital
Collaborators: The First Affiliated Hospital with Nanjing Medical University; The Affiliated People's Hospital of Ningbo University; The Second Affiliated Hospital of Jiaxing University; Shanghai Changzheng Hospital; Xinhua Hospital, Shanghai Jiao Tong University School of Medicine; Shengjing Hospital; Shanghai Fourth People's Hospital Tongji University; The First Affiliated Hospital of Medical School of Zhejiang University; Shanghai Fudan University Cancer Center
Conditions: Pancreatic Cancer; Diagnose Disease; IPMN, Pancreatic; Pancreatic Cystic Lesions; Chronic Pancreatitis; Pancreatic Neuroendocrine Tumor; Acute Pancreatitis (AP)
Interventions: Diagnosis by Artificial Intelligence model
Locations: Changhai Hospital
Countries: China
Enrollment: 2000
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
This multicenter clinical trial evaluates an artificial intelligence (AI) system designed to assist in the diagnosis and management of pancreatic diseases. Using contrast-enhanced CT scans, the study compares the AI's recommendations against the decisions of experienced clinicians to verify the system's accuracy and safety in a real-world setting. Patients are categorized into three management groups: Intervention (surgery/treatment), Intensive Surveillance (close monitoring), or Routine Surveillance (standard follow-up). The primary goal is to determine if the AI system can reliably classify patients, reduce the risk of missing malignant lesions, and prevent unnecessary surgeries, thereby improving clinical decision-making for pancreatic conditions.

Eligibility criteria:
Inclusion Criteria:

* Clinically suspected pancreatic disease.
* Scheduled to undergo contrast-enhanced CT.
* Signed informed consent form indicating agreement to participate.

Exclusion Criteria:

* History of pancreatic surgery.
* Contraindications to contrast-enhanced CT, including known hypersensitivity to iodinated contrast media or severe renal/hepatic dysfunction.
* Suboptimal image quality affecting diagnosis.
* Concurrent participation in another interventional clinical trial.
* Unsuitability for participation as determined by the investigator, including but not limited to: pregnancy or lactation, severe psychiatric disorders or cognitive impairment, significant comorbidities that may interfere with study results or patient safety.

Outcomes:
Classification accuracy
PREDiction of Different Variants of Sleep Stages for the Diagnosis Support of Chronic Insomnia and Epilepsy
Trial ID: NCT07547501
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-06
Completion date: 2027-03
Sponsor: Assistance Publique - Hôpitaux de Paris
Collaborators: Idiap Research Institute, Switzerland
Conditions: Chronic Insomnia; Epilepsy; Sleep Disorders
Enrollment: 1500
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
The objective of this study is to develop and validate deep learning algorithms for automated sleep stage and sub-stage classification using overnight polysomnography data. The models will be trained and evaluated on at least three independent datasets to ensure generalizability.

\- Primary Outcome Measure : Accuracy of deep learning-based sleep stage classification compared to expert manual scoring (\>80% target agreement), evaluated across multiple polysomnography datasets including AP-HP (Assistance Publique - Hôpitaux de Paris) data.

This is a retrospective, observational study.

Eligibility criteria:
Inclusion Criteria

* Patients with chronic insomnia and/or epilepsy who underwent polysomnography in a neurophysiology or neurology setting under the responsibility of Pr Navarro between 01 September 2011 and 31 December 2024.
* Age ≥18 and ≤65 years at the time of the polysomnography recording.

Exclusion Criteria

* Severe psychiatric disorder, including decompensated psychotic disorder, manic episode, or major depressive episode with melancholic features.
* Use of continuous positive airway pressure (CPAP) therapy during the night of recording.
* Patient refusal or documented opposition to data use.

Outcomes:
Prediction accuracy of sleep stages and sub-stages
Radiomics and Image Segmentation of Urinary Stones by Artificial Intelligence
Trial ID: NCT06412900
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-05-21
Completion date: 2028-03-28
Sponsor: Oslo University Hospital
Conditions: Urinary Stone; Renal Colic; Obstruction Ureter; Urosepsis; Urolithiasis; Ureter Stone; Kidney Stone
Locations: Oslo University Hospital, Aker
Countries: Norway
Enrollment: 522
Age range: 18 Years - None
Sex: ALL

Summary:
Kidney stone disease causes significant morbidity, and stones obstructing the ureter can have serious consequences. Imaging diagnostics with computed tomography (CT) are crucial for diagnosis, treatment selection, and follow-up. Segmentation of CT images can provide objective data on stone burden and signs of obstruction. Artificial intelligence (AI) can automate such segmentation but can also be used for the diagnosis of stone disease and obstruction.

In this project, the aim is to investigate if:

Manual segmentation of CT scans can provide more accurate information about kidney stone disease compared to conventional interpretation.

AI segmentation yields valid results compared to manual segmentation. AI can detect ureteral stones and obstruction or predict spontaneous passage.

Eligibility criteria:
Inclusion Criteria:

* Referral for CT due to episode of renal colic and clinical suspicion of urinary stone disease or
* Referral for CT due to new episode of pain in patient with known urinary stone disease
* Age ≥ 18 years

Exclusion Criteria:

* Referral for control CT of asymptomatic patients with known urinary stone disease
* Referral for control CT after treatment
* Referral for control CT for spontaneous passage of stone.
* Lack of informed consent for any reason.

Outcomes:
Comparison of stone diameter from manual segmentation with radiology report; Comparison of AI-segmentation of stones (DICE-score) with manual segmentation; Prospective performance (diagnostic accuracy) of AI detection of ureteral stone (compared to radiology report (gold standard)
Validation of the Blood Pressure Monitoring Function of a Fingertip Photoplethysmography
Trial ID: NCT06104501
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-10-20
Completion date: 2024-05-31
Sponsor: RadiRad Co., Ltd.
Collaborators: Hualien Tzu Chi General Hospital
Conditions: Blood Pressure
Interventions: RadiHeart
Enrollment: 170
Age range: 20 Years - 99 Years
Sex: ALL

Summary:
High blood pressure is a major risk factor for cardiovascular diseases, stroke, and kidney disorders. Accurate blood pressure monitoring is crucial for the diagnosis, treatment, and prevention of complications related to high blood pressure. In recent years, due to the rapid development of wearable devices and mobile technology, wearable blood pressure monitors have gradually become a non-invasive and convenient method for blood pressure monitoring. However, the accuracy of these devices has not been fully established. This study aims to validate the performance of RadiHeart (an application program) in blood pressure measurement.

Eligibility criteria:
Inclusion Criteria:

The subjects should be aged 20 or older, with an equal gender distribution of 50% male and 50% female. From this group, 85 subjects with reasonable data values will be selected, with a requirement that at least 30% of them are male and at least 30% are female. Additionally, they must meet the following blood pressure conditions:

1. At least 5% (5 individuals) of the participants should have reference systolic blood pressure readings ≥ 160 mmHg (21.33 kPa).
2. At least 20% (17 individuals) of the participants should have reference systolic blood pressure readings ≥ 140 mmHg (18.66 kPa).
3. At least 5% (5 individuals) of the participants should have reference systolic blood pressure readings ≤ 100 mmHg (13.33 kPa).
4. At least 5% (5 individuals) of the participants should have reference diastolic blood pressure readings ≤ 60 mmHg (8.0 kPa).
5. At least 20% (17 individuals) of the participants should have reference diastolic blood pressure readings ≥ 85 mmHg (11.33 kPa).
6. At least 5% (5 individuals) of the participants should have reference diastolic blood pressure readings ≥ 100 mmHg (13.33 kPa).

Exclusion Criteria:

1. Pregnant.
2. Having any of the following conditions:

   * Arrhythmia
   * Medical assessment indicates that poor blood circulation would affect the blood pressure data collection.
   * Involuntary hand movements that would affect the blood pressure data collector.
   * Nail polish on the light sensor area of the fingertip oximeter.
   * Other conditions as determined by a physician that make the individual unsuitable for participation in the trial.

Outcomes:
Difference in blood pressure (mean); Difference in blood pressure (standard deviation)
Evaluation of a Combined PGHD-AI Intelligent Model for the Nutritional Assessment and Intervention of Patients After Radical Surgery for Pancreatic Cancer.
Trial ID: NCT07167264
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-07-16
Completion date: 2026-05-31
Sponsor: Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine
Conditions: Pancreatic Cancer
Interventions: PGHD's AI intelligent model(APP)
Locations: Shanghai General Hospital
Countries: China
Enrollment: 200
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
This study will collect patient PRO (physical strength, pain, defecation, appetite, weight, etc.) data through the APP, use corpus collection cards, facial photography and other technologies to collect PGHD characteristic phenotypes, and then combine artificial intelligence technology to train and cultivate agents (agents) to carry out joint offline routine follow-up of patients after radical pancreatic cancer resection to evaluate the feasibility of nutritional risk assessment intervention. Thus, the feasibility of artificial intelligence prediction of health status is verified, and an efficient follow-up tool and nutritional support evaluation plan are provided for the management of pancreatic cancer patients throughout the course of the disease, so as to improve the treatment prognosis and quality of life of pancreatic cancer patients.

Eligibility criteria:
Inclusion Criteria:

1. Age: 18-80 years old, and voluntarily signed the informed consent form to participate in the study;
2. Meet the clinical and pathological diagnostic criteria for pancreatic cancer (ICD-10:C25);
3. After radical surgery, fully recovered from surgery at randomization;
4. ECOG PS score 0-1 ;
5. Have objective conditions to complete follow-up and examination during the study process.

Exclusion Criteria:

1. Patients or their families are willing to use the APP;
2. Current history or past history of metastatic or locally recurrent pancreatic cancer;
3. Combined with other serious organ dysfunctions (such as severe liver and kidney insufficiency, heart failure, etc.);
4. Women who are currently pregnant or lactating;
5. History of other tumor diseases in the past;
6. Other conditions that the study deems unsuitable for participation in the study, including but not limited to: patients with serious or uncontrollable medical conditions, safety risks, interference with the interpretation of study results, and impact on study compliance.

Outcomes:
The incidence and/or improvement rate; The progression free survival; The overall survival time
Demonstration of the Medical Interest of the VisioCyt® Artificial Intelligence Test
Trial ID: NCT05176145
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-04-05
Completion date: 2025-05-31
Sponsor: Vitadx
Collaborators: FONDATION FORCE POUR L'INNOVATION ET LA RECHERCHE EN SANTE
Conditions: Non-muscle Invasive Bladder Cancer (NMIBC)
Interventions: VisioCyt®
Locations: Hôpital Académique ERASME; CHU Angers; CHU de Caen; CHU de Clermont Ferrand; CHU Grenoble; CHU Nantes; Hôpital Bichat-Claude Bernard,; Hôpital Pitié-Salpétrière, APHP; Clinique la Croix du Sud; CHU de Rennes; CHU Strasbourg; Hopital Foch; CHU Toulouse; CHU Tours; Pr Maria Jose Ribal
Countries: Belgium; France; France; France; France; France; France; France; France; France; France; France; France; France; Spain
Enrollment: 400
Age range: 18 Years - None
Sex: ALL

Summary:
European, multicenter clinical trial with VisioCyt®, an in vitro diagnostic medical device. VisioCyt® is an innovative solution for the early diagnosis of bladder cancer.

Eligibility criteria:
Inclusion Criteria:

* Age ≥18 years old and autonomous
* Patient understanding national language well and able to understand the protocol.
* Patient information and informed consent signature before the start of the study
* Patients under surveillance of Non-muscle invasive bladder cancer (NMIBC) at high and very high risk of progression, whatever the stage on the basis of a histological result. The diagnosis (primary or recurrence) should be less than or equal to 24 months (the last histology result confirming a bladder cancer with high or very high risk of recurrence is taken into account).

Exclusion Criteria:

* Patients who have had total bladder resection or bladder reconstruction
* Age \<18 years old
* Person deprived of liberty or under guardianship (including curatorship)
* Bladder cancer outside of urothelial carcinoma
* Associated high urinary tract carcinoma
* Kidney transplant patient (BK virus)
* Pelvic radiotherapy patient (prostate cancer)
* Patients with or under surveillance of a muscle-infiltrating bladder tumor

Outcomes:
Performance of VisioCyt® test vs conventional cytology
Development of an Artificial Intelligence-based Incident Prediction Algorithm to Improve Cancer Patient Care and Patient Safety
Trial ID: NCT04531995
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-08-03
Completion date: 2025-12
Sponsor: Cankado GmbH
Conditions: Cancer
Interventions: CANKADO PRO-React Onco
Locations: Onkologische Praxis Moers; Ev. Krankenhaus Bethesda Praxis für gynäkologische Onkologie; Schwerpunktpraxis für Hämatologie und Onkologie; Hämatologisch-Onkologische Schwerpunktpraxis - Novum medicum
Countries: Germany; Germany; Germany; Germany
Enrollment: 166000
Age range: 18 Years - None
Sex: ALL

Summary:
The OMCAT Register aims to provide learning databases in cancer comprising both PRO data using PRO-React and "ground truth" (outcome data verified by the physician during patient examinations). Intelligent learning and knowledge engineering procedures will utilize this PRO data to provide high-quality event prediction algorithms. The ground-truth data enables so-called "supervised learning" techniques of artificial intelligence, because predicted events can be verified with a high level of certainty from ground-truth data.

Eligibility criteria:
Inclusion Criteria:

* Signed informed consent
* Age ≥ 18 years
* Diagnosed with cancer
* Prescribed CANKADO PRO-React Onco

Exclusion Criteria:

* Lack of consent to study participation or lack of patient's ability to consent
* Enrolled in this trial within a further treatment

Outcomes:
Health Status; Complaints/Symptoms; Presence or Absence of SAEs; Presence or Absence of dosis reductions; Presence or Absence of treatment interruptions; Presence or Absence of disease progression; Presence or Absence of disease regression; Presence or Absence of death
Research on the Application of Artificial Intelligence (AI) Assisted Chest Wall Tumor Resection Combined With Personalized 3D Preformed Chest Wall Defection Reconstruction System in Chest Wall Tumor Surgery
Trial ID: NCT06978075
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-07-01
Completion date: 2028-12-31
Sponsor: Wu Weiming
Conditions: Chest Wall Tumor; Reconstruction Surgery
Interventions: Chest wall tumor resection by the artificial intelgent assistent
Enrollment: 50
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
Chest wall tumors should be completely resection as much as possible while malignant chest wall tumors should be extensively resection. If not completely resection, it will recur in the short time and affect the patient's survival. At present, the surgical resection range mainly relies on preoperative imaging examination and the experience of the surgeon. It lacks precise guidance. This can easily lead to incomplete resection. In addition, the reconstruction materials required for reconstruct the excised chest wall defection are often generated in a standardized manner, lacking intraoperative adjustability. To address this clinical issue, we plan to carry out the research on the application of artificial Intelligence (AI) assisted chest wall tumor resection combined with personalized 3D preformed chest wall defection reconstruction system in chest wall tumor surgery.

Eligibility criteria:
Inclusion Criteria:

1. 18-70 years old, male or female not limited
2. Anesthesia ASA score I-II
3. Malignant tumor of soft tissue in the chest
4. Malignant tumors of ribs, rib cartilage, and sternum
5. Tumors with uncertain or unknown properties of ribs, rib cartilage, and sternum
6. Giant benign tumors of ribs, rib cartilage, and sternum
7. The preoperative examination results indicate that the tumor has not undergone distant metastasis
8. Willing to participate in the research and sign the informed consent form

Exclusion Criteria:

1. Patients with distant metastasis detected during preoperative examination
2. Inoperable tumor
3. During the examination, it was discovered that the patient had another type of malignant tumor present
4. ECOG 4
5. Suffering from active or chronic fungal/bacterial/viral infections
6. History of allergy to anesthesia related drugs
7. Heart and lung dysfunction, liver and kidney dysfunction, inability to tolerate surgery
8. Patients with mental disorders who are unable to cooperate with treatment

Outcomes:
the tumor status of surgical margin
Screening Tool Artificial Intelligence-based for Predicting the Genetic Risk of BREAST Cancer (STAR-BREAST)
Trial ID: NCT07070128
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-08-20
Completion date: 2026-08-11
Sponsor: AstraZeneca
Conditions: Breast Neoplasms
Locations: Research Site; Research Site; Research Site
Countries: Brazil; Brazil; Brazil
Enrollment: 800
Age range: None - None
Sex: FEMALE

Summary:
It is a retrospective observational study that will include female patients aged 18 years or older, who were treated between 2017 and 2024, in both public and private institutions, and identified as at high genetic risk by breast specialists and referred to a geneticist. The artificial intelligence-based tool to be used in this study is developed by the startup WeConecta, which will collect data via WhatsApp about the patients' family cancer history with the aim of predicting the genetic risk of developing breast cancer.

Eligibility criteria:
Inclusion Criteria:

* female patients,
* aged 18 years or older,
* identified as high genetic risk by breast surgeons,
* referred to a geneticist, and
* who agree to participate in the study.

Exclusion Criteria:

* male patients,
* absence of complete information in the medical records,
* patients unaware of their biological family history, and
* patients who do not agree to participate in the study.

Outcomes:
Agreement between AI tool results and genetic test results
Encouraging Flu Vaccination Among High-Risk Patients Identified by a Machine-Learning Model of Flu Complication Risk
Trial ID: NCT04323137
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-09-21
Completion date: 2021-09-21
Sponsor: Geisinger Clinic
Collaborators: National Institute on Aging (NIA)
Conditions: Influenza; Vaccination; Health Promotion; Health Behavior; Risk Reduction
Interventions: Risk reduction; Medical records-based recommendation; Algorithm-based recommendation
Locations: Geisinger
Countries: United States
Enrollment: 117649
Age range: 17 Years - None
Sex: ALL

Summary:
The purpose of the current study is to test different interventions to determine the most effective way to promote flu vaccine uptake in a high-risk population identified by an "artificial intelligence" (AI) or machine learning (ML) algorithm. The specific aims are:

1. Evaluate the effect on flu vaccination rates of informing health-system patients who are identified by an ML analysis of EHR data to be at high risk for flu complications that they are at high risk with either (a) no additional explanation, (b) an explanation that this determination comes from an analysis of their medical records, and (c) the additional explanation that an AI or ML algorithm made this determination.
2. Evaluate the effects of the same three interventions on diagnoses of flu in the same patients.

Eligibility criteria:
Inclusion Criteria:

* Current Geisinger patient at the time of study
* Falls in the top 10% of patients at highest risk, as identified by the flu-complication risk scores of Medial's machine learning algorithm (which operates on coded EHR data)
* May limit inclusion to patients that are under Geisinger primary care, depending on algorithm performance of patients who have non-Geisinger PCPs

Exclusion Criteria:

* Has contraindications for flu vaccination
* Has opted out of receiving communications from Geisinger via all of the modalities being tested

Outcomes:
Flu Vaccination Rate; Flu Vaccination Rate by Risk Level; High Confidence Flu Diagnosis Rate