moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Using Artificial Intelligence for the Detection of Respiratory Diseases Associated With Pollution
Trial ID: NCT06512142
Status: ENROLLING_BY_INVITATION
Study type: OBSERVATIONAL
Start date: 2023-11-01
Completion date: 2025-03-31
Sponsor: University of Medicine and Pharmacy "Victor Babes" Timisoara
Conditions: COPD Asthma; Lung Neoplasm; Emphysema
Locations: "Victor Babes" University Hospital
Countries: Romania
Enrollment: 30000
Age range: 18 Years - None
Sex: ALL

Summary:
Generating a picture at the city level, through geospatial and temporal grouping a lung diseases associated or exacerbated by pollution (COPD, AB and neoplasm pulmonary or pleural) and correlating this data with pollution data.

Development of an accurate and prognostic HRCT imaging diagnostic tool, computer assisted in the mentioned pathology, by generating an algorithm capable of to detect early the follow-up tomographic imaging lesions, as well as to evaluate objective their speed of evolution.

Validation of the proposed algorithm by comparison with medical diagnosis.

Eligibility criteria:
Inclusion Criteria:

* Patients must be diagnosed with COPD and/or asthma and/or pulmonary neoplasm and/or secondary pulmonary, pleural or mediastinal determinations, suspected or confirmed, according to ICD-10.
* Patients will be included regardless of the type of hospitalization, continuous or day.
* The existence of freely expressed consent, carried out according to Ethics standards professional (from the observation sheet).

For stage 2

* The use of HRCT images, in DICOM format, with a maximum cup thickness of 1.5 mm and with an average of 250 cups; no image acquisition errors;
* Imaging monitoring at a time interval;.
* Respiratory function evaluation data available: spirometry +/- the factor of gaseous diffusion (DLco);

Exclusion Criteria:

For both stages:

\- Patients who do not have a stable real domicile (eg social cases) or are not from the Timis county

For stage 2

* Patients who do not have HRCT images available or cannot be followed.
* Patients who have insufficient quality HRCT images.
* Patients who have a history of lung surgery.
* Patients who have a history of allergies to contrast agents used in imaging HRCT.

Outcomes:
Correlation between lung diseases and pollution; Development of a diagnostic algorithm
A Multi-center Study on the Efficacy and Safety of Artificial Intelligence-assisted Navigation System for Biliopancreatic Endoscopic Ultrasonography
Trial ID: NCT04892329
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-05-12
Completion date: 2022-11-30
Sponsor: Renmin Hospital of Wuhan University
Conditions: Pancreatic Cancer
Interventions: Artificial intelligence assistant system
Locations: Union Hospital, Tongji Medical College, Huazhong University of Science and Technology; Renmin Hospital of Wuhan University
Countries: China; China
Enrollment: 285
Age range: 18 Years - None
Sex: ALL

Summary:
Pancreatic cancer is one of the most lethal cancers. Survival rates vary largely depending on the stage at which it is diagnosed. EUS is considered one of the most sensitive modalities for pancreatic cancer detection. To avoid a missed diagnosis of the pancreatic cancer, the continuity and integrity of EUS needs to be ensured as much as possible. The station approach in pancreatic EUS has been established as the standard scanning procedure. Complete anatomical scanning is helpful for the identification of standard stations, and its imaging findings can assist in the diagnosis of pancreatic lesions and guide patient treatment and prognosis. But EUS is highly operator-dependent and the learning curve is steep. In this study, we constructed a deep learning-based pancreatic scanning navigation system in EUS, which can assist in identifying important anatomical structures adjacent to the pancreas in real time. In order to improve the quality of EUS and reduce the missed diagnosis of pancreatic lesions.

Eligibility criteria:
Inclusion Criteria:

* All patients meeting the following criteria will be considered for participation in the study:

  1. Male or female aged 18 or above;
  2. EUS is needed to further clarify the characteristics of biliopancreatic diseases;
  3. Patients able to give informed consent were eligible to participate.
  4. Able and willing to comply with all study process.

Exclusion Criteria:

* All patients meeting the following criteria will not be considered for participation in the study:

  1. Has participated in other clinical trials, signed informed consent and was in the follow-up period of other clinical trials.
  2. Has participated in clinical trials of the drug and is in the elution period of the experimental drug or control drug.
  3. Drug or alcohol abuse or psychological disorder in the last 5 years.
  4. Patients in pregnancy or lactation.
  5. A history of Upper Gastrointestinal surgery.
  6. Patients with anatomical abnormalities of the upper gastrointestinal tract due to advanced neoplasia
  7. Patients in whom the presence of clearly defined vital anatomical structures cannot be observed
  8. Researchers believe that the patient is not suitable to participate in the trial.

Outcomes:
Missed scanning rate of adjacent important anatomical structures in pancreatic endoscopic ultrasonography
EvALuation of ECG Transmission and AI moDels Using Apple Watch ECGs and Symptoms Data Collected usiNg a Mayo iPhone App
Trial ID: NCT05324566
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-05-05
Completion date: 2024-07-09
Sponsor: Mayo Clinic
Conditions: Heart Failure; Arrhythmias, Cardiac; Health Care Utilization
Locations: Mayo Clinic Rochester
Countries: United States
Enrollment: 4163
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of the study is to determine if the Electrocardiograms (ECGs) and symptoms data obtained from an Apple Watch and transmitted to Mayo Clinic are of sufficient quality to guide a person's care.

Eligibility criteria:
Inclusion Criteria:

\- Using the Mayo patient iPhone app. (determined automatically via Mayo software).

Exclusion Criteria:

\- Inability to provide informed consent.

Outcomes:
Number of patient-triggered Apple Watch ECGs recorded; Frequency of medical providers accessing Apple Watch data; Number of Apple Watch ECGs of acceptable quality; Performance of Artificial Intelligence ECG algorithms for disease prediction with Apple Watch ECGs
Reproductive Outcomes in Children Born Through Assisted Reproduction (ART): Focus on Intracytoplasmic Sperm Injection (ICSI) With Surgically Retrieved Sperm.
Trial ID: NCT06951061
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2007-01-01
Completion date: 2027-05-30
Sponsor: Karolinska Institutet
Collaborators: Göteborg University; Uppsala University
Conditions: Reproductive Technology, Assisted; IVF Outcomes; ICSI
Interventions: IVF/ICSI
Locations: Department of Clinical Science, Intervention and Technology, Division of Obstetrics and Gynaecology, Karolinska Institute
Countries: Sweden
Enrollment: 200000
Age range: 18 Years - 45 Years
Sex: ALL

Summary:
This research project explores how the source of sperm affects outcomes in assisted reproductive technologies (ART), focusing on children conceived through intracytoplasmic sperm injection (ICSI) with surgically retrieved sperm (SRS). Outcomes will be compared to those from ICSI with ejaculated sperm and conventional IVF. Using national registry data from all IVF and ICSI treatments in Sweden between 2007 and 2023 (Q-IVF), the study applies artificial intelligence (AI) and machine learning (ML) to identify factors influencing success rates. The aim is to improve prediction models and support more personalized fertility treatments. Special emphasis is placed on understanding the potential risks and long-term health outcomes for children conceived using SRS, which may be associated with higher rates of genetic abnormalities. The results may help optimize care strategies for infertile couples.

Eligibility criteria:
Inclusion Criteria:

* All IVF treatments performed in Sweden during the study period.
* IVF treatments will be identified from the National register Q-IVF.

Exclusion Criteria:

* Children born from multiple pregnancies
* Children conceived through oocyte and sperm donation

Outcomes:
Biochemical pregnancy rate; Clinical pregnancy rate; Live birth rate
The Nori Health App: (Pilot) Randomized Controlled Trial
Trial ID: NCT05474742
Status: TERMINATED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-04-25
Completion date: 2022-09-01
Sponsor: UMC Utrecht
Collaborators: Elisabeth-TweeSteden Ziekenhuis; Radboud University Medical Center
Conditions: IBD; Crohn Disease; Ulcerative Colitis
Interventions: The Nori Health app
Locations: Radboudumc; Elisabeth-TweeSteden Ziekenhuis
Countries: Netherlands; Netherlands
Enrollment: 50
Age range: 18 Years - None
Sex: ALL

Summary:
A multicenter, randomized controlled trial with an embedded pilot and qualitative component to investigate the effects of the Nori Health app.

Eligibility criteria:
Patients with IBD are eligible for participation if they:

* Are aged \>18 years;
* Are able to read and write the Dutch language;
* Are in the possession of a tablet or smartphone with WiFi connection.

Excluded are those with cognitive impairments or who are already familiar with use of the Nori Health app.

Outcomes:
Patient Activation Measure (PAM)
Safety and Reliability of Artificial Intelligence Driven Symptom Assessment in Children and Adolescentes
Trial ID: NCT04661488
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-12-01
Completion date: 2021-12-01
Sponsor: Turku University Hospital
Conditions: Artificial Intelligence; Triage
Interventions: AI driven triage-system
Enrollment: 1000
Age range: 0 Years - 17 Years
Sex: ALL

Summary:
Digital health technologies (DHT) are increasingly developed to support healthcare systems around the world. However, they are frequently lacking evidence-based medicine and medical validation. There is considerable need in the western countries to allocate healthcare resources accurately and give the population detailed and reliable health information enabling to take greater responsibility for their health. Intelligent patient flow management system (IPFM, product name Klinik Frontline) is developed to meet these needs. In practice, IPFM is used for decision support in the triaging and diagnostic processes as well as automatizing the management of inflow of the patients. The core of the IPFM is a clinical artificial intelligence (AI), which utilizes a comprehensive medical database of clinical correlations generated by medical doctors.

The study population of this research consists of patients from the Paediatric Emergency Clinic of Turku University Hospital (TUH). Data will be gathered during 6 months of piloting, after which the results will be analysed. Anticipated number of patients to the study is minimum of 500 patients, with objective to be 1 000. When attending to the hospital, patients or their guardians will report their demographics, background information and symptoms using structured IPFM online form. Results obtained from IPFM are blinded from the healthcare professional and IPFM does not affect professional's clinical decision making. The data obtained from IPFM online form and clinical data from the emergency department and TUH will be analysed after the data collection. The main aim of the research is to validate the use of IPFM by evaluating the association of IPFM output with 1) urgency and severity of the conditions; and 2) actual diagnoses diagnosed by medical doctors. The main hypotheses of the research are that 1) IPFM is safe and sensitive in evaluating the urgency of the conditions of arriving patients at the emergency department and that 2) IPFM has sufficient correlation of differential diagnosis with actual diagnosis made by medical doctor.

Eligibility criteria:
Inclusion Criteria:

* all patients at the emergency department with acute symptoms

Exclusion Criteria:

* Emergency situation

Outcomes:
Emergency severity index (ESI)
Rehabilitation of People With Post-tuberculosis Lung Disease
Trial ID: NCT06127641
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-10-28
Completion date: 2027-12-31
Sponsor: Centro Universitário Augusto Motta
Collaborators: Rio de Janeiro State University
Conditions: Pulmonary Tuberculosis
Interventions: Standard care plus pulmonary rehabilitation; Standard Care
Locations: Agnaldo José Lopes; Centro Universitario Augusto Motta
Countries: Brazil; Brazil
Enrollment: 40
Age range: 18 Years - None
Sex: ALL

Summary:
Tuberculosis (TB) can leave numerous sequelae, where survivors experience a transition from an acute illness to living with a multifaceted chronic illness. Post-TB lung disease (PD-PTB) encompasses lung diseases and pathologies that occur after one or more episodes of TB, which can affect the patient's lung health and cause disabling symptoms that strongly affect their long-term health. In 2020, it was estimated that there were 155 million TB survivors still alive worldwide, with a large proportion of them carrying functional sequelae with profound socioeconomic repercussions. Thus, the aim of this study is to evaluate the effect of pulmonary rehabilitation (PR) on functionality and health-related quality of life (HRQoL) of people with PD-PTB and to build a PD-PTB severity scoring system based on the data. of pre-RP individuals using artificial intelligence technique.

Eligibility criteria:
Inclusion Criteria:

* Patients with post-tuberculosis lung disease, clinically stable and able to perform \>80% of training sessions.

Exclusion Criteria:

* Presence of comorbidities not related to PD-PTB.
* Patients with inability to perform TGlittre.
* Patients who have cognitive changes that impair their inclusion in the study.
* Treatment abandonment during the application of the PR protocol.
* Uncontrolled hypertension or use of psychotropic medications.
* Any significant limitations due to osteoarthropathy.
* Having had any orthopedic surgery in the previous year.

Outcomes:
Functional capacity using the ADL-Glittre test (TGlittre) incorporated into dynamic ventilation before and after the PR program
Analysis of Lumbar Spine Stenosis Specimens for Identification of Transthyretin Cardiac Amyloidosis
Trial ID: NCT06034405
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-09-19
Completion date: 2028-05
Sponsor: Columbia University
Collaborators: National Institute on Aging (NIA)
Conditions: Lumbar Spinal Stenosis; Cardiac Amyloidosis; ATTR Amyloidosis Wild Type; ATTR Gene Mutation; ATTRV122I Amyloidosis; Cardiomyopathy, Hypertrophic
Interventions: Tc99-PYP or Tc99-HDMP Scan
Locations: Cedars-Sinai Medical Center; Tufts Medical Center; Boston Medical Center; Mayo Clinic; Columbia University Irving Medical Center; Cleveland Clinic
Countries: United States; United States; United States; United States; United States; United States
Enrollment: 1663
Age range: 50 Years - None
Sex: ALL

Summary:
Primary objective:

To identify older adults with transthyretin cardiac amyloidosis (ATTR-CA) early in the course of the illness, at a time when disease modifying therapies are most effective.

The specific aims of this epidemiologic investigation include:

1. To identify subjects with previous lumbar spinal stenosis (LSS) Surgery who have evidence of transthyretin (TTR) amyloid deposits in spinal specimens and could be at risk for ATTR cardiac amyloidosis.
2. To evaluate for ATTR-CA among those with localized TTR in the spinal tissue.

The study will also explore the following:

1. The prevalence of amyloid in lumbar spinal stenosis specimens by Congo Red staining.
2. The prevalence of TTR deposits among subjects with amyloid as determined by mass spectrometry.
3. Evaluation of a novel artificial intelligence technique for that can identify amyloid histologically with standard H\&E staining.
4. Difference in ATTR-CA prevalence between subjects with TTR and indeterminate amyloid deposits in subject's spine by myocardial uptake of technetium pyrophosphate scan (Tc99-PYP).

Eligibility criteria:
Inclusion Criteria:

1. Clinically indicated spinal decompressive surgery within 20 years prior to enrollment.
2. Age ≥50 years at the time of the surgery.
3. Able to understand and sign the informed consent document after the nature of the study has been fully explained.

Exclusion Criteria:

The presence of any of the following excludes eligibility for enrollment in this study:

1. Confirmed primary amyloidosis (AL) or secondary amyloidosis (AA).
2. Known TTR amyloidosis.
3. Other reason that would make the subject inappropriate for entry into this study.

Outcomes:
Percent of subjects with previous LSS surgery who have evidence of TTR amyloid deposits in their spinal specimen and could be at risk for ATTR cardiac amyloidosis
Real-time Evaluation of Severity and Mucosal Healing in Patients With Ulcerative Colitis by a Deep Learning Algorithm: a Multi-center Prospective Study.
Trial ID: NCT03973437
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2019-06-01
Completion date: 2020-06-01
Sponsor: Shandong University
Conditions: Ulcerative Colitis
Interventions: Artificial inteligence associated ulcerative colitis severity scoring system; Conventional human scoring
Locations: Qilu hosipital
Countries: China
Enrollment: 200
Age range: 18 Years - 70 Years
Sex: ALL

Summary:
The purpose of this study is to develop an artificial intelligence(AI) assisted scoring system, which can evaluate the disease severity and mucosal healing stage in patients with ulcerative colitis. Then testify whether this new scoring system can help physicians to enhance the accuracy of disease severity assessments in a multi-center clinical practice.

Eligibility criteria:
Inclusion Criteria:

* Patients with ulcerative colitis undergoing colonoscopy

Exclusion Criteria:

* Known or suspected bowel obstruction, stricture or perforation
* Compromised swallowing reflex or mental status
* Severe congestive heart failure (New York Heart Association class III or IV)
* Uncontrolled hypertension (systolic blood pressure \> 170 mm Hg, diastolic blood pressure \> 100 mm Hg)
* Pregnancy or lactation
* Hemodynamically unstable
* Colonic surgery history
* Bad bowel preparation (segmental BBPS\<2)
* Unable to give informed consent

Outcomes:
The scoring accuracy of Mayo ES in AI-associated group and conventional group.; The scoring accuracy of UCEIS in AI-associated group and conventional group.
AI-Powered CURAᵀᴹ Application for Identifying At-Risk Pregnancies in Obstetric Management: A Randomized Controlled Trial (CURAte)
Trial ID: NCT06974188
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-08
Completion date: 2027-03
Sponsor: National University Hospital, Singapore
Collaborators: National University of Singapore; National University of Singapore, Saw Swee Hock School of Public Health; Agency for Science, Technology and Research (A*STAR)
Conditions: High-risk Pregnancy; Pregnancy; Antenatal Health
Interventions: AI-risk Stratification
Locations: National University Hospital
Countries: Singapore
Enrollment: 1700
Age range: 21 Years - 50 Years
Sex: FEMALE

Summary:
It is important to identify high pregnancies early through screening so that appropriate care and intervention may be instituted. An AI-assisted risk categorisation approach may be advantageous compared with traditional means of screening. The purpose of this study is to determine if the adoption of an AI-assisted approach in general pregnancy risk screening will improve the accuracy of antenatal risk categorization into high- and low- risk pregnancy groups, ultimately resulting in fewer poor maternal and fetal/neonatal outcomes.

Eligibility criteria:
Inclusion Criteria:

* Age: 21 years old to 50 years old
* Singleton Pregnancy
* No more than 13 weeks' and 6 days' gestation at recruitment
* Able to provide written, informed consent

Exclusion Criteria:

* Not proficient in the English language (AI intervention is only available in English at this stage)

Outcomes:
The number and proportion of cases displaying any one of the following outcomes listed below (composite):; In addition, these outcomes will be reported individually:
Development of an Artificial Intelligence- Informed Digital Tool to Help Clinicians Practice Cognitive Behavioral Therapy for Psychosis (CBTp)
Trial ID: NCT05127837
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-06-28
Completion date: 2024-12-09
Sponsor: University of Washington
Collaborators: National Institute of Mental Health (NIMH); Lyssn.io, Inc.
Conditions: Psychosis; Schizophrenia; Schizo Affective Disorder; Bipolar Disorder; Depressive Disorder
Interventions: CBTpro
Locations: South Central Health and Rehabilitation Programs; Psynergy; Telecare; Integrated Services of Kalamazoo; Community Mental Health Authority of Clinton, Eaton, and Ingham Counties (CEICMH); Coleman Health Services; PeaceHealth; Cascadia; Lifeworks NW; Harborview Medical Center; Sound Health; Frontier Behavioral Health; Comprehensive Healthcare
Countries: United States; United States; United States; United States; United States; United States; United States; United States; United States; United States; United States; United States; United States
Enrollment: 601
Age range: 18 Years - 100 Years
Sex: ALL

Summary:
The primary objective of this grant is to develop and evaluate an Artificial Intelligence-based clinical training tool--CBTpro--to support high-quality skills training in CBT for psychosis (CBTp). CBTpro will provide a rapid means of scaling and sustaining high-quality CBTp in routine care settings across the US.

Eligibility criteria:
Client Inclusion: Adults who speak English, meet with their provider who is participating in the study at least bi-weekly (2x a month), and do not plan to leave services in the next 6 months. Qualifying diagnoses for clients include: schizophrenia; schizoaffective disorder; schizophreniform disorder; delusional disorder; other specified schizophrenia spectrum and other psychotic disorder; unspecified schizophrenia spectrum or other psychotic disorder; bipolar disorder with psychotic features; major depressive disorder with psychotic features

Exclusion criteria: Dx of psychosis secondary to substance intoxication

Outcomes:
Revised Green Paranoid Thoughts Scale (R-GPTS) Mean Score
Ethical Assessment Tool of Artificial Intelligence Impementation in Scientific Research
Trial ID: NCT07340905
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-01
Completion date: 2027-01
Sponsor: Assiut University
Conditions: Artificial Intelligence (AI)
Enrollment: 200
Age range: 18 Years - None
Sex: ALL

Summary:
Artificial Intelligence (AI) is transforming scientific research by enabling computers to perform tasks that require human intelligence - such as data analysis, pattern recognition, prediction, and language processing.

AI is now integrated into every stage of the research process: from idea generation and literature review to data collection, analysis, and publication. Using tools like machine learning, deep learning, and natural language processing (NLP), researchers can analyze massive datasets, detect hidden trends, and accelerate discoveries across fields like medicine, biology, chemistry, and social sciences.

Eligibility criteria:
* Inclusion criteria
* Age ≥ 18
* Able to provide informed consent
* Has direct experience with research and/or AI tools used in research (often within the last 1-3 years)
* Speaks/reads the study language (e.g., English/Arabic)
* Available to complete the study activity (survey)
* Exclusion criteria
* No AI is involved.
* It's only theory/principles with no checklist or questions.
* It covers only one issue (privacy-only, bias-only) when you need full ethics.
* Not enough info to assess (no data source, no model description, no intended use).
* It's a small classroom/toy example with no real impact.

Outcomes:
Ethical Assessment Tool of Artificial Intelligence Impementation in Scientific Research