moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Deep Learning to Summarize Findings in Dental Panoramic Radiographs
Trial ID: NCT04894201
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-05-07
Completion date: 2023-05
Sponsor: National Taiwan University Hospital
Conditions: Radiography; Digital; Dental Diseases; Dentist-Patient Relations
Interventions: Dental Panoramic Radiographs
Locations: National Taiwan University Hospital
Countries: Taiwan
Enrollment: 1000
Age range: 20 Years - 100 Years
Sex: ALL

Summary:
In this work, the investigators study the application of artificial intelligence systems on dental panoramic images for dental findings. An artificial intelligence system will be learned on an publicly available panoramic image dataset, and test against the investigators' local patient cohort as external test data. The investigators hypothesize the performance would be similar, if not identical to on the public data, and that the investigators' AI system is generalizable.

Eligibility criteria:
Exclusion Criteria:

* patients under 20 or with primary teeth
* patients with non-removable metal accessory above neck, such as tongue ring, nose ring etc.
* patients with mandible or maxilla deformation

Outcomes:
Area Under Curve for Receiver Operating Characteristics of Clinical Findings
Dynamic Follow-up of Factors Influencing Implant Success and Models for Predicting Implant Outcomes
Trial ID: NCT06029751
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2017-01-01
Completion date: 2025-12-31
Sponsor: The Dental Hospital of Zhejiang University School of Medicine
Conditions: Implant Site Reaction
Interventions: No intervention
Locations: The Stomatologic Hospital, School of Medicine, Zhejiang University
Countries: China
Enrollment: 1000
Age range: 18 Years - None
Sex: ALL

Summary:
Nowadays, artificial intelligence technology with machine learning as the main means has been increasingly applied to the oral field, and has played an increasingly important role in the examination, diagnosis, treatment and prognosis assessment of oral diseases. Among them, machine learning is an important branch of artificial intelligence, which refers to the system learning specific statistical patterns in a given data set to predict the behavior of new data samples \[8\]. Machine learning is divided into two main categories: Supervised learning and Unsupervised learning. Whether there is supervision depends on whether the data entered is labeled or not. If the input data is labeled, it is supervised learning. Unlabeled learning is unsupervised. Supervised learning is a kind of learning algorithm when the correct output of the data set is known. Because the input and output are known, it means that there is a relationship between the input and output, and the supervised learning algorithm is to discover and summarize this "relationship". Unsupervised learning refers to a class of learning algorithms for unlabeled data. The absence of label information means that patterns or structures need to be discovered and summarized from the data set.

Eligibility criteria:
Inclusion Criteria:

* Patients aged 18 years and above;
* 1-5 years after implantation;
* Implantation torque \> 35N·cm;
* Signed informed consent.

Exclusion Criteria:

* Contraindications of general implantation surgery;
* Have received head and neck radiation therapy;
* Past or current treatment with bisphosphonates;
* Do not cooperate with the interviewer.

Outcomes:
Mean Bone Level of dental implant
The Implementation of Computer-aided Detection in an Initial Endoscopy Training Improves the Quality Measures of Trainees' Future Colonoscopies
Trial ID: NCT06623331
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-01-01
Completion date: 2024-03-31
Sponsor: Jagiellonian University
Conditions: Quality Indicators, Health Care; Colonoscopy Diagnostic Techniques and Procedures; Artificial Intelligence (AI)
Interventions: AI-enhanced endoscopy training; Conventional endoscopy training
Locations: Jagiellonian University
Countries: Poland
Enrollment: 6000
Age range: 18 Years - None
Sex: ALL

Summary:
Computer-aided detection (CADe) based on artificial intelligence (AI) may improve colonoscopy quality. An increasing number of young endoscopists are trained in an AI environment. However its impact on trainees' future outcomes remains unclear. The study aimed to evaluate the quality indicators of endoscopists trained in an AI environment compared to those trained conventionally.

Eligibility criteria:
Inclusion Criteria:

* adult participants who underwent a colonoscopy for various reasons performed by specific endoscopists that were assessed in terms of quality indicators

Exclusion Criteria:

* a history of bowel resection
* confirmed inflammatory bowel disease
* suspicion of polyps or cancer in other imaging tests
* suspicion of familial adenomatous polyposis

Outcomes:
Serrated polyp detection rate (SDR); withdrawal time; Cecal intubation rate (CIR); Adenoma Detection Rate (ADR); Advanced adenoma detection rate (AADR); Adenoma per colonoscopy score (APC)
Development and Implementation of Model-based Systems for Professional Football Teams, Aimed at Optimizing Health and Performance
Trial ID: NCT05872945
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2019-07-01
Completion date: 2024-07-30
Sponsor: RCD Mallorca SAD
Conditions: Electrocardiogram; Arrhythmias, Cardiac
Interventions: Electrocardiogram
Locations: RCD Mallorca SAD
Countries: Spain
Enrollment: 54
Age range: 18 Years - 45 Years
Sex: MALE

Summary:
LIST OF PLANNED ORIGINAL PUBLICATIONS

1. T wave inversion detection with machine learning to prevent sudden death in professional football players.
2. Machine learning applied to biological parameters for control and advisory in professional football players (Machine learning applied to biological parameters for control and advisory in professional football players.)
3. Machine learning applied to sport geolocation systems for injury prevention in professional football players.

Eligibility criteria:
Inclusion Criteria

• Healthy young and professional players of legal age who play their role in professional football teams.

Exclusion criteria:

* Players who are not a regular part of these professional teams.
* Players with known pathology.

Outcomes:
Waves Detection
Integrating Artificial Intelligence Into International Classification of Functioning, Disability, and Health Coding: Effectiveness of a Mobile Application for Patient Questionnaires
Trial ID: NCT07021781
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-11-25
Completion date: 2025-02-26
Sponsor: Tulip Medicine
Conditions: ICF; Rehabilitation; Artifical Intelligence; Application
Interventions: MedQuest mobile application; Traditional Paper-Based Questionnaires
Locations: "Green Clinic" LLC; "National Scientific Oncological Center" LLC
Countries: Kazakhstan; Kazakhstan
Enrollment: 185
Age range: 18 Years - None
Sex: ALL

Summary:
Mobile applications and artificial intelligence are increasingly integrated into medical practice, yet their impact on workflow optimization and diagnostic accuracy remains understudied. This study evaluates the effectiveness of the MedQuest mobile application in optimizing patient questionnaire processes and assesses the accuracy of AI-driven International Classification of Functioning, Disability and Health (ICF) coding in comparison to traditional clinician-based coding.

Eligibility criteria:
Inclusion Criteria:

1. Aged 18 and older.
2. Owned a smartphone (iOS or Android operating system) with internet access.
3. Deemed by the investigator to be able to understand and comply with the study requirements.
4. Provided a signed and dated written informed consent form, along with any necessary personal data processing permissions, before any examination procedures.

Exclusion Criteria:

1. Patients with severe cognitive impairments that would prevent them from understanding and completing the questionnaires independently
2. Patients with severe visual impairments that would interfere with their ability to use mobile applications
3. Patients who declined to participate after being informed about the study protocol

Outcomes:
Time required to complete and process questionnaire; Accuracy of AI-generated ICF coding compared to clinician coding
AI-generated Feedback in Social Robotic Virtual Patients
Trial ID: NCT07277829
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-01-27
Completion date: 2025-06-05
Sponsor: Ioannis Parodis
Collaborators: Region Stockholm; Karolinska Institutet
Conditions: Virtual Patient; Social Robots; Artificial Intelligence (AI); Large Language Model; Feedback; Medical History Taking
Interventions: AI post consultation feedback
Locations: Karolinska University Hospital
Countries: Sweden
Enrollment: 115
Age range: None - None
Sex: ALL

Summary:
The goal of this quasi-experimental educational study is to learn whether AI-generated post-consultation feedback in social robotic virtual patient interactions improves medical students' clinical performance in medical history-taking. The main question it aims to answer is:

Can AI-generated feedback integrated in an AI-enhanced social robotic virtual patient platform improve medical students' clinical performance in medical history taking?

Researchers will compare results from standardised examinations following the structure of an objective structured clinical examination (OSCE), of medical students performing virtual patient interactions with AI-generated post consultation feedback compared to medical students who have not received AI-generated feedback.

Participants will perform five virtual patient cases in rheumatology using an established virtual patient platform: the Social AI-enhanced Robotic Interface (SARI). After completion of each case, students participate in follow-up seminars with consultant rheumatologists to discuss the cases. After completion of all nine cases, students take part in a OSCE based examination to evaluate medical-history taking.

Eligibility criteria:
Inclusion Criteria:

* Sixth-semester medical students at Karolinska Institutet.
* Assigned to clinical rotations in rheumatology.
* Participating between January and June 2025.

Exclusion Criteria:

\- None.

Outcomes:
Total OSCE score
In-depth Characterization of Atriogenic Secondary Tricuspid Regurgitation Due to Atrial Fibrillation. Prevalence, Mechanisms, Advanced Quantification of Regurgitation Severity, and Relative Impact of Sinus Rhythm Restoration on the 3D Remodeling of Right Heart Chambers and Tricuspid Valve Apparatus
Trial ID: NCT05749107
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-12-02
Completion date: 2025-11-02
Sponsor: Istituto Auxologico Italiano
Collaborators: Ministry of Health, Italy
Conditions: Heart Valve Incompetence; Atrial Fibrillation, Persistent
Interventions: Echocardiography; Proteomic analyses
Locations: Istituto Auxologico Italiano, IRCCS; IRCCS Ospedale San Raffaele
Countries: Italy; Italy
Enrollment: 1050
Age range: 18 Years - None
Sex: ALL

Summary:
1050 patients with persistent/permanent atrial fibrillation (AF) will be studied using conventional and advanced (three-dimensional and deformation imaging) echocardiography. Patients with moderate/severe isolated secondary tricuspid regurgitation (STR) will undergo blood tests to assess their proteomic profile and cardiac CT to measure the tricuspid annulus geometry. The project will aim to 1. assess the prevalence of moderate/severe isolated STR in patients with AF; 2. identify the mechanisms associated with the development of moderate-severe STR in patients with AF; 3. identify the proteomic profile associated with significant growth of tricuspid valve leaflets as a mechanism to protect patients with AF from the development of moderate/severe STR; 4. evaluate the effects of the restoration of sinus rhythm on the severity of STR and the remodeling of the right heart cardiac structures (i.e. right ventricle, right atrium, and tricuspid valve apparatus).

Eligibility criteria:
Inclusion Criteria:

* age\> 18 years
* persistent/permanent atrial fibrillation
* good acoustic apical window
* left ventricular ejection fraction \> 50%
* pulmonary artery systolic pressure\< 50 mm Hg
* no more than mild mitral or aortic valve disease
* ability to attend clinical and echocardiographic follow-ups
* signature of the study informed consent

Exclusion Criteria:

* pregnancy
* implanted pace-maker or ICD
* previous heart valve surgery or transcatheter treatment
* previous heart transplant
* organic tricuspid regurgitation

Outcomes:
Incidence of either moderate or severe functional tricuspid regurgitation; To identify the mechanisms leading to moderate/severe STR during AF.; To evaluate the extent of the right heart structure remodeling occurring after the recovery of the sinus rhythm and its effects on STR severity.
Effects of a Postbiotic Supplementation on Gastrointestinal and Core Symptoms in Autism Spectrum Disorder: A Multicenter Randomized Controlled Trial
Trial ID: NCT06514573
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-05
Completion date: 2026-11
Sponsor: Istituto Superiore di Sanità
Collaborators: University of Rome Tor Vergata; Federico II University
Conditions: Autism Spectrum Disorder (ASD)
Interventions: Sodium Butyrate; Placebo (cornstarch)
Locations: Azienda Ospedaliera Universitaria Federico II; Policlinico Tor Vergata Hospital; Istituto Superiore di Sanità
Countries: Italy; Italy; Italy
Enrollment: 128
Age range: 3 Years - 6 Years
Sex: ALL

Summary:
Autism spectrum disorder (ASD) is a neurodevelopmental condition occurring in 1:77 Italian children. Several comorbidities are reported, including functional gastrointestinal disorders (FGIDs) present in up to 70% of patients. FGIDs are disorders resulting from a combination of symptoms affecting motility, hypersensitivity, and other functions, which are not caused by anatomic or organic origin and that impact the severity of ASD core symptoms and complicate the clinical management of ASD children, especially those who are non-verbal. Evidence reports gut microbiome (GM) remodelling in ASD children, and postbiotic butyrate, a GM-derived metabolite, attenuates FGIDs in children and restores social behavior in ASD mouse models. Clinical data on butyrate effects in ASD are still scanty. The present study investigates the therapeutic effects of a 16-week oral postbiotic supplementation on clinical/behavioral profiles, gastrointestinal disturbances, gut microbiome, and immune and inflammatory biomarkers in peripheral blood and fecal samples in children with ASD and FGIDs. Using the Machine Learning (ML) approach, a subset of artificial intelligence, this study also aims to identify predictive factors implicated in the effect of the postbiotic supplementation on FGIDs, important for prevention through modulation of the microbiota. The investigators expect that treating FGIDs will have an impact on the behavioral and core symptoms of ASD and the quality of life of children and their families.

Eligibility criteria:
Inclusion Criteria:

* children aged 3-6 years
* both sexes
* ASD diagnosis and presence of FGIDs (6-item Gastrointestinal Severity Index (6-GSI) \>7 from \> 3 months)

Exclusion Criteria:

* age 6 years
* uncertain FGIDs diagnoses
* FGIDs symptoms duration \<3 months
* concomitant presence of other chronic condition (adverse food reactions, metabolic disorders, infections)
* malformation and Gi or urinary tracts chronic diseases
* immunodeficiencies
* diabetes
* neurologic/cardiovascular/autoimmune diseases
* obesity
* malnutrition
* antibiotics and/or pre-/pro-/synbiotics use 6 months prior to enrollment
* last 12 months participation into other clinical trials

Outcomes:
Gastrointestinal symptom severity.
Testing the Effectiveness of the AI-Supporter in Reducing Urinary Tract Infections, Incontinence-associated Dermatitis and Caregiving Costs for Incontinence Patients
Trial ID: NCT06613503
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-07-22
Completion date: 2025-10-31
Sponsor: China Medical University Hospital
Conditions: Incontinence; Urinary Tract Infection; Incontinence-associated Dermatitis; Cost-effectiveness
Interventions: AI-supporter
Locations: Rom A Master List, Extracted From This Organization'S Records.
Countries: Taiwan
Enrollment: 60
Age range: 20 Years - None
Sex: FEMALE

Summary:
The "AI Supporter," an intelligent excretion management robot, leverages artificial intelligence-based vision recognition to autonomously detect and cleanse affected areas, followed by drying and changing the diaper, thereby reducing caregiver strain and enhancing care quality. This study aims to assess the efficacy of the "AI Supporter" in decreasing the incidence of urinary tract infections and incontinence-associated dermatitis among incontinent patients, in addition to exploring its cost-effectiveness.

Adopting an experimental (two groups) and longitudinal design, this research utilizes both convenience and random sampling strategies. The study anticipates recruiting 60 female subjects who have been confined to bed for more than three months with urinary and/or fecal incontinence. Participants will intermittently use the AI Supporter over a 14-day period. Measurement tools include routine urine analysis.

Eligibility criteria:
Inclusion Criteria:

* Participants must have been bedridden for at least 3 months and have urinary and/or fecal incontinence.
* Female participants aged over 20 years old.
* Participants must be capable of wearing the AI-supporter device during the study period.

Exclusion Criteria:

* Participants with severe skin conditions unrelated to incontinence.
* Participants with current urinary tract infections or incontinence-associated dermatitis at the time of enrollment.
* Participants who are unable to provide informed consent or have a legal representative to do so.

Outcomes:
white blood cells; Bacterial count
The ELFIE-HYPERTENSION Randomized Clinical Trial
Trial ID: NCT06242483
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-06-14
Completion date: 2026-04-30
Sponsor: Hospital Israelita Albert Einstein
Collaborators: Elfie Pte. Ltd.
Conditions: Hypertension
Interventions: Elfie Solution
Locations: Hosp. Universitário UFG; Hospital Municipal Aparecida de Goiânia; NF. Pacheco Cardiologia Humanizada; Rede AME Saúde; Hospital Universitário Pedro Ernesto; Clínica do Coração - JMF; Hospital Universitário São Francisco na Providência de Deus; Centro de Pesquisa Clínica de Guarulhos; Hospital Israelita Albert Einstein; CETRUS - Diagnostica LTDA; Elizabeth Do Espirito Santo Eireli; 354 Military Hospital; National Geriatric Hospital; District 4 Hospital; Gia Dinh hospital; Nguyen Tri Phuong Hospital
Countries: Brazil; Brazil; Brazil; Brazil; Brazil; Brazil; Brazil; Brazil; Brazil; Brazil; Brazil; Vietnam; Vietnam; Vietnam; Vietnam; Vietnam
Enrollment: 930
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
The ELFIE-HYPERTENSION randomized clinical trial is an academic-led, collaborative, pragmatic, randomized, open-label, parallel arm, multicenter, trial evaluating whether a digital health intervention based on the Elfie solution compared with usual care will reduce systolic blood pressure in individuals with hypertension.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18 years and ≤ 75 years
* Male and female patients
* Hypertension diagnosis based on the local guidelines criteria
* Uncontrolled blood pressure (systolic blood pressure ≥ 140 mmHg) on the screening visit
* Patients in whom medication adherence needs to be improved according to the healthcare provider clinical judgment
* Use of at least 1 anti-hypertensive medication
* Ownership of an active smartphone with operational system iOS (version 13.0 or later) or Android (version 8.0 or later) with access to the internet on 3G, 4G or 5G
* Ability to use a smartphone app

Exclusion Criteria:

* Refusal to provide written informed consent
* Current use of a health app with the aim to improve medication adherence and blood pressure control
* Illiteracy to read in the local language
* Known cognitive decline, dementia, or diagnosis of severe psychiatric conditions (depression, schizophrenia, bipolar disorder)
* Known use of illicit drugs or alcoholism
* Current participation in a cardiovascular randomized clinical trial
* Life expectancy \< 1 year

Outcomes:
Office systolic blood pressure (SBP)
CoronaWatch - Early Detection of Cardiovascular Risks in COVID-19 Via SmartWatch
Trial ID: NCT04376853
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-04-15
Completion date: 2022-05-20
Sponsor: University Hospital Heidelberg
Collaborators: The German Heart Foundation; Klaus Tschira Stiftung, Germany; Freunde und Förderer der Kardiologie Heidelberg; Germany
Conditions: COVID-19
Interventions: Apple Watch Series 5
Locations: Department III of Internal Medicine, University Hospital Heidelberg
Countries: Germany
Enrollment: 50
Age range: 18 Years - None
Sex: ALL

Summary:
In December 2019, a new viral disease called COVID-19 emerged. It is caused by the new corona virus SARS-CoV-2. It was initially described in the chinese city of Wuhan. In the following months, the disease developed into a pandemic, which is currently an immense international challenge.

So far, there is little scientific evidence on risk stratification, especially on the prognostic value of biomarkers (laboratory-chemical, clinical and digital) with regard to clinical deterioration of patients with COVID-19. Further scientific studies are needed to establish optimal risk stratification and early detection of clinical deterioration.

In this study, the investigators aim to observe patients with COVID-19 via SmartWatches on top of their clinical routine. The investigators aim to determine, whether the addition of SmartWatches enhances risk stratification, early detection of complications and prognostics in patients with COVID-19, who have cardiovascular diseases or receive medication with arrhythmogenic risk.

Eligibility criteria:
Inclusion Criteria:

* Diagnosis of COVID-19 (detection by PCR)
* Age ≥ 18
* The patient has understood the study design and the informed consent form and has signed and dated the informed consent form
* The patient has got a cardiovascular disease or therapy with potential cardiovascular complications

Outcomes:
Biomarker