moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Impact of Artificial Intelligence in Dysplasia Detection During Colonoscopy in Patients With Long-data Ulcerative Colitis: a Crossover Study
Trial ID: NCT05171634
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-05-05
Completion date: 2021-05-18
Sponsor: Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana
Collaborators: University of Valencia
Conditions: Ulcerative Colitis; Dysplasia
Interventions: DiscoveryTM; iSCAN
Locations: Antonio López-Serrano
Countries: Spain
Enrollment: 50
Age range: 18 Years - None
Sex: ALL

Summary:
Prospective clinical study that analyzes the efficacy of colonoscopy assisted by an artificial intelligence system (DiscoveryTM) compared to virtual chromoendoscopy with iSCAN in the detection of colon dysplasia in patients with long-standing Ulcerative Colitis.

Eligibility criteria:
Inclusion Criteria:

* Ulcerative Colitis confirmed by endoscopy and histology.
* Involvement 30% of the colonic surface (or any extent if concomitant with PSC).
* Duration of the disease \>7 years (or any duration if concomitant with PSC).

Exclusion Criteria:

* Personal history of colorectal cancer or high-grade dysplasia.
* Previous colectomy (partial or complete).
* Coagulopathy that prevents biopsies or polypectomy.
* Colonoscopy performed in the previous 6 months.
* Pregnant or nursing women.
* Inadequate bowel preparation.
* Presence of endoscopic Ulcerative Colitis activity.
* Colonic stenosis and incomplete colonoscopy.

Outcomes:
"Per lesion" dysplasia detection rate (DDR); "Per patient" dysplasia detection rate (DDR)
Wearable-based COVID-19 Markers for Prediction of Clinical Trajectories. The WAVE Study.
Trial ID: NCT04357834
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-10-22
Completion date: 2021-06-30
Sponsor: Insel Gruppe AG, University Hospital Bern
Collaborators: ETH Zurich
Conditions: COVID; COVID 19; SARS-CoV 2
Interventions: Equipment with smartwatch throughout hospital stay on the general ward
Locations: Emergency Department, University Hospital Bern, Inselspital
Countries: Switzerland
Enrollment: 46
Age range: 18 Years - 120 Years
Sex: ALL

Summary:
The aim is to develop a wearable-based ICU (intensive care unit) prediction algorithm for inpatients contracted with SARS-CoV-2. Inpatients with suspicion of COVID-19 or with confirmed SARS-CoV-2 infection will be included. The participants will be equipped with a smartwatch, which gathers physiological data throughout hospitalisation.

Eligibility criteria:
Inclusion Criteria:

* Informed consent as documented by signature
* Age \>= 18 years
* Suspicion of COVID-19 or patient tested positive for SARS-CoV-2
* Hospitalisation on the general ward

Exclusion Criteria:

* Smartwatch cannot be attached around the wrist of the patient
* Direct transfer from the emergency department or external institution to ICU (i.e. no hospitalization on the general ward)
* Known allergies to components of the smartwatch
* Rejection of ICU transfer in the patient decree

Outcomes:
Diagnostic accuracy of smartwatch data in predicting ICU requirement in COVID-19 contracted inpatients quantified as the area under the receiver operator characteristics curve (AUC ROC > 0.85).
Computer-based Skin Aging Simulation for Skin Cancer Prevention: A Pilot Study
Trial ID: NCT04532437
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-02-09
Completion date: 2021-03-19
Sponsor: University Hospital, Basel, Switzerland
Conditions: Skin Aging
Interventions: artificial intelligence-based skin aging simulation; electronic questionnaire 1; electronic questionnaire 2
Locations: Department of Dermatology, University Hospital Basel
Countries: Switzerland
Enrollment: 60
Age range: 18 Years - 35 Years
Sex: FEMALE

Summary:
This pilot study is to assess the knowledge, attitudes, and practices regarding skin cancer prevention in young adults and to determine the impact of artificial intelligence-based simulation of facial skin aging on their attitudes regarding skin cancer prevention.

Eligibility criteria:
Inclusion Criteria:

* healthy female volunteers
* written,Institutional Review Board (IRB)/ Independent Ethics Committee (IEC)-approved informed consent.

Exclusion Criteria:

* skin diseases requiring medical therapy

Outcomes:
Change in visual analogue scale (VAS) score regarding importance of UV- protection; Change in VAS score regarding rating of personal exposure to UV radiation
Conversational AI to Improve PeRiconception Care Access
Trial ID: NCT06618144
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-08-20
Completion date: 2025-09-21
Sponsor: University of Pennsylvania
Collaborators: Memora Health Inc; Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)
Conditions: Early Pregnancy Loss; Early Pregnancy
Interventions: Texting platform
Locations: University of Pennsylvania Health System
Countries: United States
Enrollment: 350
Age range: 18 Years - None
Sex: FEMALE

Summary:
The goal of this project, Conversational Artificial Intelligence (AI) to Improve PeRiconception Care Access (CIRCA), is to engage patients in care early in pregnancy (before prenatal care starts) and safely triage concerns to PEACE to reduce unnecessary emergency department visits.

Eligibility criteria:
Inclusion Criteria:

* Pregnant
* Seeking to establish care at the Hospital of the University of Pennsylvania OR seeking care in the Emergency Department or Perinatal Evaluation Center.

Outcomes:
Time to initiating care (days)
Colonoscopy Training With or Without Artificial Intelligence Among Endoscopy Residents: a Randomized Controlled Study
Trial ID: NCT07420309
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-02-01
Completion date: 2027-07-01
Sponsor: University of Thessaly
Conditions: Colonoscopy Training
Interventions: Artificial Intelligence in colonoscopy; Conventional colonoscopy training
Locations: General University Hospital of Larissa
Countries: Greece
Enrollment: 10
Age range: None - None
Sex: ALL

Summary:
Type of study: Randomized controlled study

Investigated Procedure: Artificial Intelligence Software for Polyp Detection in Colonoscopy training

Trial participants: Endoscopy trainees during their training with at least one year of training ahead and no previous structured exposure to CADe

Objectives:

Primary: Adenoma Miss Rate (AMR) in the end of the first year of training for each group and after the crossover.

Secondary: Further endoscopists-related parameters will be recorded and compared between the two arms: PDR, ADR, aADR, PMR, withdrawal time, CIR, AEs

The investigators will be asked, when possible, to follow-up the patients until they have their next colonoscopy or a diagnosis of CRC to assess for interval cancer.

Eligibility criteria:
Inclusion Criteria:

* Trial participants: Endoscopy trainees at any time of their training, with at least one year of training ahead without previous structured exposure to CADe during their training.

Recruited patient population: Adult patients undergoing diagnostic, screening, or surveillance colonoscopy will be included.

1. Provision of signed and dated informed consent form understand the risks and benefits of the study
2. Aged ≥18 years old
3. Able to read and write in the provided language.
4. Participants capable to provide written informed consent and comply with the trial protocol.

Exclusion Criteria:

* Trial participants: Endoscopy trainees after their basic training, during the last year of training or with systematic exposure to CADe.

Recruited patient population:

1. Patients with a known contraindication for biopsy, bowel obstruction or perforation,
2. Pregnant or lactating females
3. Known polyposis syndromes, or who had a history of inflammatory bowel disease (IBD), CRC, or colorectal surgery.
4. Any patient whose cecum is not reached and who were highly suspected of having polyposis syndromes, IBD, intestinal tuberculosis, or CRC.
5. Incomplete bowel preparation (segmental Boston Bowel Preparation Score \<2, or total \<6)

Outcomes:
Adenoma Miss Rate (AMR) at the end of the first year of training for each group and after the crossover.
Automated Echocardiographic Detection of Coronary Artery Disease Using Artificial Intelligence Methods
Trial ID: NCT06314295
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-03-11
Completion date: 2026-05-11
Sponsor: Beijing Hospital
Conditions: Coronary Artery Disease
Locations: Beijing Hospital
Countries: China
Enrollment: 1500
Age range: 18 Years - 90 Years
Sex: ALL

Summary:
The incidence rate and mortality of coronary artery disease are increasing year by year. Exploring non-invasive, accurate, and widely applicable methods to screen and diagnosis is of great significance. New ultrasound techniques, such as non-invasive myocardial work, have been proven to be superior to traditional ultrasound techniques in screening and diagnosis. However, diagnostic analysis based on ultrasound video images is time-consuming and subjective. The progress of artificial intelligence technology in fully automated quantitative evaluation of video images provides the possibility for computer-aided design screening and diagnosis. At present, the application of artificial intelligence in computer-aided design is a cutting-edge issue in the field of cardiovascular disease research. The application of artificial intelligence technology in the construction of computer-aided diagnostic models based on ultrasound video images is still in its early stages.

Eligibility criteria:
Inclusion Criteria:

* Patients with suspected coronary artery disease
* Patients plan to undergo coronary angiography

Exclusion Criteria:

* Patients with aortic valve stenosis
* Patients with aortic valve replacement surgery
* Patients with hypertrophic cardiomyopathy
* Patients with severe heart valve disease
* Patients with severe arrhythmia
* Patients with severe cardiomyopathy
* Patients with severe congenital heart disease
* The quality of ultrasound images is poor

Outcomes:
Different coronary angiography results
Human vs Machine: a Randomised Controlled Trial Comparing Traditional In-person Instruction, Artificial Intelligence Versus Virtual Reality for Learning Basic Critical Care Echocardiography
Trial ID: NCT06355557
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-04-04
Completion date: 2025-01
Sponsor: Tan Tock Seng Hospital
Conditions: Ultrasound
Interventions: AI enabled ultrasound system for self-directed learning; Simulator for self-directed learning; traditional with human instructors
Locations: Tan Tock Seng Hospital
Countries: Singapore
Enrollment: 66
Age range: 21 Years - None
Sex: ALL

Summary:
The aim of the study is to investigate if hands-on training for basic CCE with virtual reality simulators or guided by artificial intelligence is non-inferior to training by an experienced instructor.

Eligibility criteria:
Inclusion Criteria:

* Medical students will have limited clinical exposure to critical care echocardiography
* above the age of 21 years

Exclusion Criteria:

* prior attendance of a critical care echocardiography courses or
* refusal to participate in the study or complete both hands on sessions

Outcomes:
Improvement in image acquisition and structure identification at the end of 3 months.
AI-Supported, Context-Aware Digital Nudging Intervention to Reduce Ultra-Processed Food Consumption and Improve Dietary Sustainability Among Adolescents and Young Adults (STAY-UP)
Trial ID: NCT07514312
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-09-16
Completion date: 2028-06-30
Sponsor: Istanbul Gelisim University
Conditions: Ultra-Processed Food Consumption; Obesity Prevention; Dietary Behaviour; Unhealthy Diet; Non-Communicable Chronic Diseases
Interventions: Artificial intelligence-supported digital nudging
Enrollment: 1000
Age range: 12 Years - 25 Years
Sex: ALL

Summary:
This study aims to evaluate the effectiveness of an artificial intelligence (AI)-supported, context-aware digital nudging intervention designed to reduce ultra-processed food consumption and improve dietary sustainability among adolescents and young adults. The intervention utilizes real-time behavioral data, including image-assisted dietary logging and contextual information, to identify high-risk consumption moments and deliver personalized, non-coercive nudges. The study will assess changes in ultra-processed food intake, contextual consumption patterns, and sustainability-related dietary indicators.

Eligibility criteria:
Inclusion Criteria:

* Adolescents aged 12 to 25 years
* Ownership of a smartphone or regular access to a digital device
* Ability to use the digital intervention platform
* Willingness to provide informed consent (and parental consent where applicable)
* Regular consumption of ultra-processed foods at baseline

Exclusion Criteria:

* Presence of medical conditions requiring a specific therapeutic diet
* Participation in another dietary or behavioral intervention study
* Severe cognitive or psychological conditions that may impair participation
* Inability to use digital tools or applications

Outcomes:
Change in ultra-processed food consumption (servings per day)
A Comparative Study of AI Methods for Fetal Diagnostic Accuracy in Ultrasound
Trial ID: NCT06268392
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2024-02-15
Completion date: 2024-08-01
Sponsor: Copenhagen Academy for Medical Education and Simulation
Collaborators: Slagelse Hospital; Technical University of Denmark; Rigshospitalet, Denmark
Conditions: Fetal Growth Retardation; Fetal Macrosomia; Fetal Growth
Enrollment: 150
Age range: 18 Years - None
Sex: FEMALE

Summary:
This study serves as a supplemental investigation to the randomized controlled SCAN-AID study (NCT0632187). This study will evaluate and compare the fetal growth estimation outcomes of AI-supported groups, expert sonographers, and control groups using a secondary AI predictive model.

Eligibility criteria:
Pregnant women:

Inclusion Criteria:

* Singelton pregnant.
* Gestational age: 30-38 weeks
* Maternal age \< 40 years

Exclusion Criteria:

* Oligo hydramnion
* Severe fetal anomaly e.g. fetal heart anomaly, omphalocele etc.
* Severe fetal macrosomia or growth restriction.

Outcomes:
Fetal weight; Ultrasound fetal weight estimation
Validation of a Deep Learning Based Bowel Preparation Evaluation System: A Prospective, Multi-center, Cross-sectional Study
Trial ID: NCT04591145
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-10-09
Completion date: 2020-12-31
Sponsor: Renmin Hospital of Wuhan University
Collaborators: Hubei Hospital of Traditional Chinese Medicine; Wuhan Central Hospital; Wuhan Third Hospital; The General Hospital of Central Theater Command; The Third People's Hospital of Hubei Province; Wuhan Puren Hospital; Wuhan Pu-Ai Hospital; Tianyou Hospital Affiliated to Wuhan University of Science and Technology; Wuhan Red Cross Hospital
Conditions: Adenoma; Bowel Preparation
Interventions: Observational group
Locations: Renmin Hospital of Wuhan University
Countries: China
Enrollment: 1400
Age range: 18 Years - None
Sex: ALL

Summary:
A deep learning based system to calculate the proportion of Boston Bowel Prep Scale (BBPS) score of 0-1 during withdrawal phase has been constructed previously. This multi-center study is going to perform a prospective observational study to validate the threshold of the adequate proportion.

Eligibility criteria:
Inclusion Criteria:

* Male or female aged 18 years or above;
* Ability to read, understand and sign informed consent forms;
* The researchers believe that the subjects can understand the process of the clinical study and are willing and able to complete all the study procedures and follow-up visits to cooperate with the study procedures.

Exclusion Criteria:

* Patients with contraindications to colonoscopy (obstruction or perforation, severe acute inflammatory bowel disease, toxic megacolon, severe heart failure, severe heart failure, etc.);
* Patients with galactosemia;
* Pregnant or lactating women;
* Patients used lactulose, a stimulant, or laxative within 7 days;
* Patients refused to sign informed consent forms.

Outcomes:
Cleanliness assessment of different intestinal segment in the artificial intelligence system; Adenoma detection rate
Towards A Better Paradigm for Head and Neck Cancer Treatment Applying Artificial Intelligence: an International Cohort Study of Electronic Health Records. HNC-TACTIC.
Trial ID: NCT05117775
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-12-06
Completion date: 2024-12-31
Sponsor: Savana Research
Collaborators: Head and Neck Cancer International Group (HNCIG)
Conditions: Head and Neck Cancer
Interventions: No intervention - Just description and predictive models
Locations: Savan Research S.L
Countries: Spain
Enrollment: 10000
Age range: 18 Years - None
Sex: ALL

Summary:
This will be an international, multicenter, retrospective, observational, and data-driven study using secondary data captured in EHRs. The extraction of the data captured in the EHRs will be performed with SAVANA's EHRead®, an innovative data-driven system based on Natural Language Processing (NLP) and machine learning. For all patients, the Index Date is defined as the timepoint within the study period when they fulfill ALL inclusion criteria and no exclusion criteria. Follow-up comprises the period between Index Date and the last EHR available within the study period. Additional variable-specific time windows may be considered to optimize data collection.

Eligibility criteria:
Inclusion Criteria:

* Patients ≥18 years old.
* Patients diagnosed with HNSCC
* For selected exploratory analyses, patients diagnosed with nasopharynx, paranasal sinus, and salivary gland tumors.

Exclusion Criteria:

* Patients with follow-up of less than 6 months, except if deceased (any cause) in the 6 months after HNSCC diagnosis

Outcomes:
Predictive model based on dynamic risk stratification (DRS) for the risk of recurrence or disease progression; Predictive model based on dynamic risk stratification (DRS) aimed at identifying patients' features
THE EFFECT OF ARTIFICIAL INTELLIGENCE ASSISTED SPINE REHABILITATION ON GENERAL HEALTH, ANXIETY AND DISABILITY IN PATIENTS WITH CERVICAL DISC HERNIA
Trial ID: NCT06632028
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-10-04
Completion date: 2024-11-04
Sponsor: Okan University
Conditions: Herniated Intervertebral Disc
Interventions: artificial intelligence assisted spinal rehabilitation; conventional therapy
Locations: Iskenderun Gelişim Hastanesi
Countries: Turkey (Türkiye)
Enrollment: 42
Age range: 40 Years - 65 Years
Sex: ALL

Summary:
The aim of this study is to investigate the effect of artificial intelligence-assisted spinal rehabilitation in neck hernia compared to traditional methods.

Eligibility criteria:
Inclusion Criteria:

1. Being between 40-65 years old
2. Volunteering to participate in the study
3. No history of operated cervical disc herniation
4. Pain of 4 or more according to the numeric pain scale in the last 6 months

Exclusion Criteria:

1. Trauma or operation in the cervical region
2. Doing regular exercise
3. Regular use of psychiatric medication
4. Having known neurological, autoimmune diseases
5. Those who have a wound or infection in the area to be treated
6. Those with a pacemaker

Outcomes:
Informed consent form (Appendix 1), sociodemographic assessment form (Appendix 2), Numeric Pain Scale for pain severity (Appendix 3), Notham Health Profile Questionnaire for general health assessment (Appendix 4), Beck Anxiety Index for anxiety (Appendix
Effects of Close Collaboration With Parents Intervention on NICU Care Environments and Long-term Development of Preterm Infants: a Pilot Study
Trial ID: NCT07306000
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-12-22
Completion date: 2028-12-31
Sponsor: Turku University Hospital
Conditions: Preterm Infant Development; Preterm Infant Health
Interventions: Close Collaboration with Parents training
Locations: Nagano Children's Hospital; Kyoto University Hospital; Korea University Anam Hospital NICU
Countries: Japan; Japan; South Korea
Enrollment: 100
Age range: 1 Hour - 7 Days
Sex: ALL

Summary:
Close Collaboration with Parents intervention is an evidence-based educational intervention for the entire multi-professional staff of neonatal intensive care units (NICUs). The goal of the intervention is to strengthen partnership between staff and parents, enhance parental participation in infant care. There are no studies yet about the effects of the Close Collaboration with Parents intervention on the long-term neurodevelopment and socio-emotional development of very preterm infants and their interaction with their parents. Accordingly, a multicenter cluster randomized controlled trial is planned. Prior to initiating this large-scale study, it is essential to validate the measurement instruments. Therefore, a pilot study will be conducted to assess their feasibility and to determine the appropriate sample size.

Eligibility criteria:
Inclusion Criteria:

* preterm infants born below 32 weeks of gestation and/or weighing \< 1500 g who receive long-term follow-up at each study site.

Exclusion Criteria:

* the infant has any major anomalies
* the infants are triplets or higher order
* the infant's condition is critical and the survival is uncertain
* the parents cannot understand the informed consent form in Korean or in Japanese

Outcomes:
Auditory environment: parent and adult speech; Auditory environment: sound level and its variation; Sensitive care: sleep quality and quantity; Parents' presence and parent-infant skin-to-skin contact; Type of enteral feeding; Body weight