moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
An Early Warning Model of Unfavorable Outcomes Following Endovascular Interventional Treatment of Intracranial Aneurysms Based on Medical Image Analysis and Deep Learning Algorithm
Trial ID: NCT06364293
Status: ENROLLING_BY_INVITATION
Study type: OBSERVATIONAL
Start date: 2024-01-01
Completion date: 2026-12-31
Sponsor: Beijing Tiantan Hospital
Collaborators: National Natural Science Foundation of China
Conditions: Intracranial Aneurysm
Interventions: Observational design does not include interventional behavior.
Locations: Beijing Tiantan hospital
Countries: China
Enrollment: 750
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
Endovascular treatment has become one of the primary treatment methods for intracranial aneurysms. The unfavorable outcomes during follow-up included aneurysm recurrence and long-term incomplete-occlusion, which would bring a high risk of rebleeding and retreatment. Previous studies have tried to predict the outcomes of aneurysms following endovascular treatment based on aneurysm characteristics including morphology, embolization packing degree, etc, but the conclusion was inconsistent. Hemodynamics of aneurysms and parent artery played a greater role in predicting outcomes following endovascular treatments. Investigators also found that the outcomes were determined by many factors, in which the demography, clinical indicators, treatment methods, and material selection can not be ignored, and the mechanism of unfavorable imaging outcomes should be explored using large samples of clinical cases and numerous variable parameters. The pre-experiment of investigators confirmed that artificial intelligence technology can meet the calculation requirements for deep mining and analysis of large sample data. This study aims to use the deep learning model to identify relevant risk factors and weights, establish a stable and accurate prediction model, then incorporate the prospective study to verify the model. The results will be very helpful in accurately predicting the adverse outcomes such as recurrence and long-term non-occlusion after endovascular treatment and help to improve the therapeutic strategy and avoid risk factors. Besides, the occurrence of ischemic or hemorrhagic complications during follow-up may affect the final follow-up outcome, so the analysis was included as one of the outcome events to evaluate the prognosis after intervention.

Eligibility criteria:
Inclusion Criteria:

* Clinical diagnosis of intracranial aneurysm；
* Endovascular intervention was performed；
* The age is more than 18 years and less than 80 years;
* At least one follow-up of imaging data must be digital subtraction angiography with a time interval of 12 months or more;
* The quality of image data can satisfy morphological measurement and hemodynamic calculation;
* The patient family members were informed and consented to participate in the study.

Exclusion Criteria:

* Dynamic aneurysms with cerebrovascular malformation;
* Dissecting, fusiform or thrombotic aneurysms;
* Follow-up images were not digital subtraction angiography or data quality can not meet the hydrodynamic analysis.

Outcomes:
Unfavorable imaging outcome
Leveraging Large Language Models (LLM) to Enhance Research Competency Among Undergraduate Nursing Students: A Novel Approach to Research Education
Trial ID: NCT07540078
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-08-13
Completion date: 2026-12
Sponsor: National University Health System, Singapore
Collaborators: Ministry of Education, Singapore
Conditions: Education; AI (Artificial Intelligence)
Interventions: LLM-Integration
Locations: National University of Singapore, Yong Loo Lin School of Medicine, Singapore, Singapore 117599
Countries: Singapore
Enrollment: 640
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this mixed method interventional study is to develop and test the effectiveness of integrating ChatGPT into the nursing research course to improve research competency among third-year undergraduate nursing students. The main questions it aims to answer is:

Will participants who undergo the LLM-integrated curriculum show an increase in research competency and attitudes compared to participants who did not undergo this curriculum.

Researchers will compare a students assessment grades, as well as their research competency and attitude, measured via the Research Competence Scale (R-Comp) and Revised Attitudes Towards Research scale (R-ATR) respectively. Research will determine whether the LLM-integrated curriculum could improve students understanding and attitudes towards research.

Eligibility criteria:
Inclusion Criteria:

* All year-three students in cohort years AY2025/26 and AY2026/27 who are enrolled in mandatory research course titled "NUR3202C: Research and Evidence-Based Healthcare"

Exclusion Criteria:

* NIL

Outcomes:
Research Competence questionnaire; Revised Attitudes Toward Research scale; Grades of research proposal
Leveraging AI to Transform Dietary Choices for Cardiovascular Health in Young Adults Experiencing Food Insecurity
Trial ID: NCT07108452
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-06
Completion date: 2026-12
Sponsor: University of California, Davis
Collaborators: International Food Policy Research Institute; Penn State University
Conditions: Healthy
Interventions: A mobile application that uses AI to deliver behavioral nudges that encourage healthy food choices.; Limited app version for dietary assessment only
Locations: University of California, Davis - Department of Nutrition
Countries: United States
Enrollment: 114
Age range: 18 Years - 24 Years
Sex: ALL

Summary:
The goal of this feasibility study is to determine the adherence, acceptability, and usability of the behavioral intervention of a mobile AI application and its effects on food choices and diet quality. A secondary goal is to assess whether the app's accuracy or feasibility differs by food security status. Study participants are UC Davis students aged 18-24 years. The main question it aims to answer is:

\- Does the gamified app version that delivers behavioral nudges have a higher adherence and acceptability rate, and does this translate to better dietary behaviors?

Participants will use either the intervention app (dietary assessment + gamification) or the control app (non-gamified, dietary assessment only) for six weeks to record all their food intake. Diet quality will be assessed at baseline and endline, and a Likert scale acceptability questionnaire will be administered at endline.

Eligibility criteria:
Inclusion Criteria:

* Willing to use their smartphones with app to record all foods consumed
* UC Davis student

Exclusion Criteria:

* eating disorders
* Participant in the validation study that tested the app's accuracy against weighed food records

Outcomes:
App acceptability by version assigned to participants; App's usability by version assigned to participants; App adherence rates by version assigned to participants
Improving Dermatology Access by Direct-to-Patient Teledermatology and Computer-Assisted Diagnosis
Trial ID: NCT05128006
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-11-07
Completion date: 2026-09-30
Sponsor: VA Office of Research and Development
Collaborators: VA Boston Healthcare System; Durham VA Health Care System; Providence VA Medical Center
Conditions: New Patient Use of Teledermatology Mobile App
Interventions: New patient teledermatology visit
Locations: San Francisco VA Medical Center, San Francisco, CA; Rocky Mountain Regional VA Medical Center, Aurora, CO; Atlanta VA Medical and Rehab Center, Decatur, GA
Countries: United States; United States; United States
Enrollment: 63200
Age range: 18 Years - None
Sex: ALL

Summary:
This study will examine access to dermatology care. The operational partner will implement a direct-to-new patient teledermatology clinical care pathway while the research will study the effects of that implementation, use data associated with deployment to develop and test an Artificial Intelligence system, and understand key stakeholders' attitudes regarding and readiness for remote dermatology care.

Eligibility criteria:
Inclusion Criteria:

* New patient referrals to dermatology at San Francisco, Decatur, and Aurora from 2021-2025

Exclusion Criteria:

* Patients who are not dermatology patients
* Dermatology patients who are not seen at San Francisco, Decatur, and Aurora
* Dermatology patients at San Francisco, Decatur, and Aurora who have visits only before 2021 or after 2025

Outcomes:
New patient in-person dermatology visits; Consult completion time; Appointment completion time; Travel distance for VA care; Third next available dermatology clinic appt completion time; New Patient Teledermatology encounters; Organizational Readiness to implement mobile teledermatology for direct new patient care; Readiness of Veterans to use mobile teledermatology for direct new patient care; Patient Satisfaction of different types of dermatologic care; Readiness of Veterans to use Artificial Intelligence for dermatologic care
Clinical Validation of Transrectal Multiparametric Ultrasound (PCaVision) for Detecting Clinically Significant Prostate Cancer: A European Head-to-Head Comparison With MRI in Primary Diagnosis and Active Surveillance Populations
Trial ID: NCT06935487
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-04-01
Completion date: 2027-05
Sponsor: Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
Collaborators: Angiogenesis Analytics
Conditions: Prostate Cancer
Interventions: transrectal ultrasound of prostate (TRUS) with AI software algorithm; MRI prostate
Locations: Institut Paoli- Calmettes; L'Institut Mutualiste Montsouris; University Klinikum Bonn; Martini-Klinik am UKE; Urologische Klinik München- Planegg; Università degli Studi di Foggia; University of Padua; Amsterdam UMC; Oslo University hospital Ullevål; Fundació Puigvert
Countries: France; France; Germany; Germany; Germany; Italy; Italy; Netherlands; Norway; Spain
Enrollment: 806
Age range: 18 Years - None
Sex: MALE

Summary:
The goal of this clinical trial is to learn if a new ultrasound-based imaging method (PCaVision) can accurately detect clinically significant prostate cancer in adult men (18 years and older) who are either undergoing initial evaluation or are already in active surveillance for prostate cancer. The main questions it aims to answer are:

* Does PCaVision detect clinically significant prostate cancer as accurately as MRI?
* Can some men safely avoid prostate biopsies based on PCaVision imaging results?

Researchers will compare PCaVision-guided biopsies to MRI-guided biopsies to see if PCaVision performs as well as MRI in identifying aggressive prostate cancers.

Participants will:

* Undergo both an MRI and a PCaVision ultrasound scan
* Receive targeted prostate biopsies based on any suspicious areas found in either scan
* Possibly have follow-up visits to monitor for biopsy-related side effects

Eligibility criteria:
Inclusion criteria:

1. male
2. 18 years of age or older
3. scheduled for evaluation by prostate MRI due to:

   * suspicious digital rectal examination (DRE) and/or
   * Elevated serum PSA levels, or as part of active surveillance (AS) follow-up
4. Have provided written informed consent

Exclusion criteria:

1. Active urinary tract infection or prostatitis
2. A history of cardiac right-to-left shunt
3. Allergy to sulphur hexafluoride or any other ingredient in the ultrasound contrast agent SonoVue
4. Current treatment with dobutamine
5. Severe pulmonary hypertension (pulmonary artery pressure \> 90 mmHg), uncontrolled systemic hypertension, or respiratory distress syndrome
6. Any other contraindication to MRI or 3D mpUS imaging
7. Inability to understand the language of the patient information (i.e., language barrier)
8. Previous treatment with focal therapy for prostate cancer (e.g., HIFU, cryotherapy, laser ablation, etc.)

Outcomes:
Number of Participants With Clinically Significant Prostate Cancer (csPCa) Detected by PCaVision-Targeted Biopsies Compared to MRI-Targeted Biopsies
The RADAR Study - Wearable-Based Dysglycemia Detection and Warning in Diabetes
Trial ID: NCT04689685
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-02-19
Completion date: 2022-03-28
Sponsor: Insel Gruppe AG, University Hospital Bern
Collaborators: ETH Zurich; University of St.Gallen
Conditions: Diabetes Mellitus, Insulin-Dependent; Diabetes
Interventions: Wearing smartwatch and continous glucose sensor
Locations: Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism
Countries: Switzerland
Enrollment: 40
Age range: 18 Years - None
Sex: ALL

Summary:
The study RADAR aims at developing a wearable based dysglycemia detection and warning system for patients with diabetes mellitus using artificial intelligence.

Eligibility criteria:
Inclusion Criteria:

* Informed consent as documented by signature
* Age ≥ 18 years
* Diabetes mellitus treated with multiple daily insulin injections (MDI) or continuous subcutaneous insulin infusion (CSII)

Exclusion Criteria:

* Smartwatch cannot be attached around the wrist of the patient
* Known allergies to components of the Garmin smartwatch or the Dexcom G6 system
* Pregnancy, intention to become pregnant or breast feeding
* Cardiac arrhythmia (e.g. atrial flutter or fibrillation, AV-reentry tachycardia, AV-block \> grade 1)
* Pacemaker or ICD (implantable cardioverter defibrillator)
* Treatment with antiarrhythmic drugs or beta-blockers
* Drug or alcohol abuse
* Inability to follow the procedures of the study, e.g. due to language problems, psychological disorders, dementia, etc. of the participant
* Physical or psychological disease likely to interfere with the normal conduct of the study and interpretation of the study results as judged by the investigator

Outcomes:
Accuracy of the RADAR model: Diagnostic accuracy of wearable based physiological data in detecting dysglycemia (glucose > 13.9mmol/L and glucose < 3.9 mmol/L) quantified as the area under the receiver operator characteristics curve (AUC-ROC)
Development of Machine Learning Models for the Prediction of Complications After Colonic, Colorectal and Small Intestine Anastomosis in Psychiatric and Non-psychiatric Patient Collectives (P-Study)
Trial ID: NCT05257863
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-05-01
Completion date: 2023-12-31
Sponsor: Dr. Med Anas Taha
Collaborators: University of Basel; University of Hamburg-Eppendorf
Conditions: Postoperative Complications; Psychosomatic Disorder; Psychiatric Disorder; Cancer; Diverticulitis; Morbus Crohn; Colitis Ulcerosa; Anastomotic Leak; Anastomotic Complication; Small Intestine Anastomotic Leak
Locations: University of Basel
Countries: Switzerland
Enrollment: 10000
Age range: 18 Years - None
Sex: ALL

Summary:
Our study aims to lay the basis for a predictive modeling service for postoperative complications and prolonged hospital stay in patients suffering from psychiatric diseases undergoing colorectal surgery.

Furthermore, we aim to investigate the impact of preoperative Risk factors, psychiatric and psychosomatic diseases on the outcomes of colorectal surgery and the complications after colorectal surgeries like anastomosis insufficiency via predictive modeling techniques

The service mentioned above will be publicly available as a web-based application

Eligibility criteria:
Inclusion Criteria:

* Colocolic, colorectal and small intestine anastomosis
* Neoplasia,
* Diverticulitis
* Mesenteric ischemia
* Iatrogenic or traumatic perforation
* Inflammatory bowel disease

Exclusion Criteria:

* Patients \<18 years
* Patients suffering from recurrent colorectal cancer bearing
* Peritoneal carcinomatosis or unresectable metastatic disease at the time of bowel resection and anastomosis will be excluded.
* Patients who cannot be followed up on for more than six weeks after surgery

Outcomes:
Anastomotic insufficiency/leakage; Complication after surgery/ Comprehensive Complication Index/ Clavian Dindo Score; Length of Hospital Stay (in Days); Intraoperative influid manangment; Development of a preoperative score for morbidity/mortality in colorectal surgery
A Randomized Controlled Trial on Artificial Intelligence-Powered Large Language Model-Assisted Doctor-Patient Preoperative Communication for Psychological Distress Alleviation and Healthcare Efficiency Improvement in Prostate Cancer Patients
Trial ID: NCT07082049
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-02-15
Completion date: 2028-06-01
Sponsor: Fudan University
Conditions: Prostate Cancer
Interventions: Prostate surgery; LLM-assisted communication; Routine preoperative communication
Locations: Fudan University Shanghai Cancer Center
Countries: China
Enrollment: 160
Age range: 18 Years - None
Sex: MALE

Summary:
The goal of this clinical trial is to evaluate the efficacy of large language model (LLM)-assisted communication on psychological distress alleviation and healthcare efficiency improvement in prostate cancer patients. The main questions it aims to answer are:

Does LLM-assisted communication reduce preoperative anxiety and negative emotion more effectively than standard care? Can LLM-assisted communication decrease clinician workload while maintaining communication quality?

Researchers will compare the intervention group (LLM-assisted communication) with the control group (standard communication) to see:

Whether LLM-assisted communication has greater reductions in patients' emotional distress scales and physiological stress metrics.

How it impacts clinician workload and communication time.

Participants will:

Undergo baseline assessments before communication, including a range of emotional scales and physiological metrics.

Receive clinician-reviewed LLM-generated materials and/or standard communication before surgery.

Complete assessments after preoperative communication.

Eligibility criteria:
Inclusion Criteria:

1. Newly diagnosed prostate adenocarcinoma confirmed by histopathology;
2. Age 18 years or older;
3. Must be a candidate for radical prostatectomy, including but not limited to laparoscopic radical prostatectomy (LapRP) and robot-assisted radical prostatectomy (RARP);
4. Ability to independently complete questionnaire filling and possess basic language comprehension ability;
5. Ability to understand and willingness to sign informed consent.

Exclusion Criteria:

1. A history of severe diagnosed mental illness (such as depression, anxiety disorder, insomnia);
2. A history of diagnosed cognitive dysfunction (such as intellectual disability, Parkinson's disease);
3. Requires emergency surgery (within 72 hours after diagnosis);
4. Has participated in other interventional clinical trials.

Outcomes:
Change from Baseline in the GAD-7 score (Generalized Anxiety Disorder 7-item Scale) after the Preoperative Communication between Two Groups.; Change from Baseline in the VAS-A (Visual Analogue Scale-Anxiety) after the Preoperative Communication between Two Groups.; Change from Baseline in the MCMQ score (Medical Coping Modes Questionnaire) after the Preoperative Communication between Two Groups; Change from Baseline in the PANAS score (Positive and Negative Affect Schedule) after the Preoperative Communication between Two Groups.
How Avatar Can Enhance Autobiographical Memory of Patients With Substance Use Disorder
Trial ID: NCT06806943
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-07-01
Completion date: 2024-12-15
Sponsor: Le.L.A.T. Lega Lotta Aids E Tossicodipendenza
Collaborators: Catholic University of the Sacred Heart
Conditions: Substance Use Disorder (SUD)
Interventions: avatar-based interactions
Locations: Le.L.A.T. Lega Lotta Aids E Tossicodipendenza
Countries: Italy
Enrollment: 42
Age range: 18 Years - None
Sex: MALE

Summary:
The goal of this study is to investigate whether the use of artificial intelligence platforms can promote the retrieval of specific positive autobiographical memories.

Eligibility criteria:
Inclusion Criteria:

* Clinical diagnosis of substance use disorder
* Admitted to the drug rehabilitation center for at least 3 months
* Aged 18 years or older

Exclusion Criteria:

* Previous neurological pathologies
* Previous cognitive deficits

Outcomes:
Change in Specific Autobiographical Memory Recall
Symbolic Regression Model To Predict Choledocholithiasis: Prospective Validation
Trial ID: NCT04410848
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2019-06-07
Completion date: 2020-07-31
Sponsor: Hospital Universitario Dr. Jose E. Gonzalez
Conditions: Common Bile Duct Calculi
Interventions: To determine the diagnostic of Choledocholithiasis with symbolic regression model
Locations: Hospital Universitario Dr. Jose E. Gonzalez, Universidad Autónoma de Nuevo León
Countries: Mexico
Enrollment: 200
Age range: 18 Years - None
Sex: ALL

Summary:
Choledocholithiasis refers to the presence of gallstones within the common bile duct. It is proposed to look for markers that help in the diagnosis and in differentiating between retained and migrated gallstones. The selection of patients is a very important aspect, due to the economic aspects and possible complications. Taking advantage of the development of technology, the improvement in computer systems, the use of artificial intelligence and a symbolic regression model that works to predict the presence of choledocholithiasis and provide evidence that clarifies the treatment of patients with this pathology, especially in this group where there is a bigger controversy.

Eligibility criteria:
Inclusion Criteria:

* Patients above 18 with clinical suspicion of choledocholithiasis by biliary-type pain, laboratory testing that reveals a cholestatic pattern of liver test abnormalities, biliary pancreatitis or dilated common bile duct

Exclusion Criteria:

* Patient with a history of cholecystectomy
* History of previous ERCP or surgery involving bile duct
* Patient who could not be followed
* Patient with other pathology that causes alteration of liver function test.

Outcomes:
To validate prospectively a symbolic regression model to predict choledocholithiasis
A Prospective Study of the Effectiveness of Speech-to-speech Voice-cloning Care (SVCC) to Improve ICU-acquired Anxiety for Critically Ill Patients
Trial ID: NCT06743321
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-08-01
Completion date: 2027-12-31
Sponsor: Peking Union Medical College Hospital
Conditions: Critical Care Nursing
Interventions: Speech-to-speech Voice-Cloning Care (SVCC)
Locations: Peking Union Medical College Hospital
Countries: China
Enrollment: 234
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this clinical trial is to learn the effect of Speech-to-speech Voice-Cloning Care (SVCC) on improving ICU-acquired anxiety for critically ill patients. The main question it aims to answer is:

* Can the use of participants' loved ones' voices by nurses in communication while providing care lead to better mental health and clinical outcomes?

Researchers will see if the implementation of SVCC can reduce anxiety and depression and improve clinical outcomes.

Participants will:

* Receive the SVCC intervention until the endotracheal tube (ETT) is removed.
* Keep a diary of delirium, the duration of mechanical ventilation, and ICU stays.

Eligibility criteria:
Inclusion Criteria:

* Expected mechanical ventilation time \> 24 hours;
* Expected ICU stay \> 72 hours;
* Language: Chinese;
* Richmond Agitation-Sedation Scale (RAAS) score ≥ -2 points;
* Hemodynamic stability.

Exclusion Criteria:

* Severe hearing impairment or worse (Grade 3 or higher according to WHO's Grades of hearing impairment);
* Disorders of consciousness or comprehension;
* Mental or psychological disorders that are being treated with medication;
* Head trauma or surgery resulting in an inability to wear earphones.

Outcomes:
ICU-acquired anxiety