moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Blinded Randomized Control Trail of Artificial Intelligence-Assisted Ultrasound Screening for Neonatal Hip Dysplasia in a Clinical Cohort
Trial ID: NCT06765525
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-01-15
Completion date: 2025-04-30
Sponsor: RenJi Hospital
Conditions: Hip Dysplasia, Developmental
Interventions: Junior sonographer measurement of DDH; Senior sonographer measurement of DDH; Automated annotation of the DDH measurement through deep learning; AI-assisted junior sonographer meaturement of DDH
Locations: Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine
Countries: China
Enrollment: 1976
Age range: 28 Days - 6 Months
Sex: ALL

Summary:
To ascertain the efficacy of the DeepDDH system, a deep learning framework, in enhancing diagnostic accuracy and curtailing follow-up intervals for infants undergoing screening for developmental dysplasia of the hip (DDH), the researchers are executing a blinded, randomized controlled trial. This trial juxtaposes AI-only and AI-assisted assessments of DDH against sonographer interpretations across various proficiency levels in the preliminary analysis of ultrasound images.

Eligibility criteria:
Inclusion Criteria:

* Infants aged 28 days to 6 months who underwent DDH ultrasound examination in Renji Hospital, Shanghai Jiaotong University School of Medicine and the Sixth People's Hospital, Shanghai Jiaotong University School of Medicine between August 2014 and December 2021

Exclusion Criteria:

* Patients lacking or incomplete ultrasound images;
* Patients with poor image quality and unusable images after assessment, including non-compliance with Anatomical identification (Checklist I consists of seven anatomical structures: 1. Chondro-osseous border, 2. Femoral head, 3. Synovial fold, 4. Joint capsule and perichondrium, 5. Labrum, 6. Cartilagineous roof, 7. Bony roof) and Usability check (Checklist II includes three anatomical landmarks: 1. Lower limb of the os ilium, 2. Parallel middle plane, and 3. Labrum);
* Infants with hip dysplasia caused by other diseases such as cerebral palsy, joint contracture, suppurative coxitis, etc., or with other hip diseases and limb deformities.

Outcomes:
Average diagnostic accuracy
Feasibility and Acceptability of an LLM-based Chatbot for Family Caregivers: Evaluation Study
Trial ID: NCT07280650
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-08
Completion date: 2027-06-30
Sponsor: University of Michigan
Conditions: Caregivers
Interventions: LLM-based Chatbot
Locations: University of Michigan
Countries: United States
Enrollment: 60
Age range: 18 Years - None
Sex: ALL

Summary:
This study evaluates the feasibility and acceptability of using the artificial intelligence (AI) chatbot mobile application (an LLM-based Chatbot) to support the well-being of informal care partners of individuals with traumatic brain injury (TBI), dementia, or Huntington disease (HD). Over an 8-week intervention period, up to 60 care partners will engage with the application (app), spending four weeks focusing on physical health and four weeks focusing on mental health. Participants will complete surveys at the midpoint and end of the intervention period. Participants will also complete a semi-structured interview to discuss participants experience using an LLM-based Chatbot.

The study team hypothesizes that care partners will find the chatbot acceptable and feasible to use in daily life, and that there will be low attrition and high completion rates of the study.

Eligibility criteria:
Inclusion Criteria (care partner)

* Age 18 years or older
* Able to independently provide informed consent
* Able to read, speak, and understand English
* Have an Apple (iOS) smartphone, tablet, or laptop, have access to the internet and text messages, and be willing to use these resources for the study, including downloading and using the AI chatbot app (Earkick)
* Be caring for and adult with a medically documented diagnosis of TBI, dementia or Huntington disease (HD)
* Provide emotional, physical, and/or financial support/assistance to the individual with the TBI, dementia, or HD, indicating a response per protocol
* Indicate high levels of caregiver burden, confirmed using the 12-item Zarit Burden questionnaire (scores must be ≥20)
* Willing to complete all study assessments for the duration of their study participation

Inclusion Criteria (care recipient):

* Age 18 years or older
* Have a medically documented diagnosis of TBI, dementia or Huntington disease as noted per protocol

Exclusion Criteria (care partner)

* Anyone not meeting inclusion criteria above
* Professional, paid caregivers
* Anyone at the discretion of the Principal investigator (PI) that would be a preclusion of safe or meaningful participation

Exclusion Criteria (care recipient):

* Anyone not meeting inclusion criteria
* Lives in a care facility (assisted living, nursing home) or is bedbound

Outcomes:
Feasibility and Acceptability as measured by the Feasibility Questionnaire
ARTIFICIAL INTELLIGENCE APPLICATIONS IN REPRODUCTIVE MEDICINE
Trial ID: NCT05699850
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-11-01
Completion date: 2023-04-01
Sponsor: Al Baraka Fertility Hospital
Collaborators: Al-Azhar University
Conditions: ARTIFICIAL INTELLIGENCE (AI) APPLICATIONS IN REPRODUCTIVE MEDICINE
Interventions: ARTIFICIAL INTELLIGENCE (AI)
Locations: Al-Azhar university; Kamal Eldin Abdalla Rageh
Countries: Egypt; Egypt
Enrollment: 10
Age range: None - None
Sex: ALL

Summary:
Many studies have been published investigating the use of AI as an unbiased, automated approach to embryo assessment. This review will summarize the recent AI advancements in the IVF field. Hopefully, that incorporating AI technology into the IVF clinics may be the next frontier in the journey towards personalised reproductive medicine and improved fertility outcomes for patients.

Eligibility criteria:
Inclusion Criteria:

* fertility related

Exclusion Criteria:

* fertile people

Outcomes:
SUCCESS RATE
A New Strategy for the Visulation of Molecular Genotype in Glioma Evolution Based on the Radiomics and Microomics
Trial ID: NCT03750890
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2019-01-01
Completion date: 2022-12-31
Sponsor: Tongji Hospital
Conditions: Glioma of Brain
Locations: Zhenxiong Wang
Countries: China
Enrollment: 1000
Age range: None - None
Sex: ALL

Summary:
The key molecular changes in the progression of glioma are closely related to tumor heterogeneity, pathological grade, precision treatment and prognosis of glioma. At present, a visually quantitative assessment criteria about the key molecular typing of glioma is still absent. Based on the previous research, this project intends to establish a multi-dimensional database of glioma from clinical, radiomics and microomics levels. The investigators aim to filter out the specific molecular markers in the progression of glioma and explore the intrinsic connection of radiomics features and microomics molecular markers by using bioinformatics integration analysis and artificial intelligence multiple kernel learning. Thus, the investigators could determine the specific molecular mechanism in the progression of glioma, and establish a visually quantitative assessment system of pre-operative precisive grading, molecular typing discrimination and prognosis prediction. The completion of this project is of great significance for improving molecular diagnostic level of glioma, guiding individualized diagnosis and treatment decisions, and improving the survival rate of patients.

Eligibility criteria:
Inclusion Criteria:

1. postoperative pathological and genetic test confirmed brain glioma;
2. The preoperative Clear and complete multimodal imaging data were collected within 10 days.

Exclusion Criteria:

1. patients who underwent surgery more than 4 weeks after MRI;
2. patients with motion artifacts.

Outcomes:
radiomics features（the parameters of T1WI, T2WI, DWI, DKI, ASL, ESWAN, DCE, MRS and so on）; microomics molecular markers (IDH mutation, 1p/19q status, P53 mutation, TERT and ATRX mutation, EGFR amplification and mutation, MGMT methylation status, PTEN mutation, ZM fusion gene and other gene features)
Determining the Feasibility of Digital Interventions for Adults with Treatment-Resistant Depression
Trial ID: NCT06732089
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-12-16
Completion date: 2026-12
Sponsor: Unity Health Toronto
Collaborators: University of Toronto; Toronto Metropolitan University
Conditions: Major Depressive Disorder (MDD); Major Depressive Episode; Treatment-Resistant Major Depressive Disorder
Locations: St. Michael's Hospital, Unity Health Toronto
Countries: Canada
Enrollment: 200
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this observational study is to learn about remote mental health monitoring technology for adults with treatment-resistant depression. The main question it aims to answer is: are digital mental health monitoring tools (an electronic data capture platform and wearable device (e.g., smartwatch or smart-ring)) feasible to implement alongside clinical treatment for depression? The secondary aim of this study is to inform preliminary clinical parameters for larger, definitive studies.

Participants receiving neuropsychiatric treatment (repetitive transcranial magnetic stimulation, intravenous ketamine, or electroconvulsive therapy) as part of their regular medical care for treatment-resistant depression in the Interventional Psychiatry Program will have their clinical assessment data entered into a digital platform and will wear an accessory-based wearable device for the duration of treatment.

Eligibility criteria:
Inclusion Criteria:

* Adults (aged 18+) who are capable of giving informed consent
* Patients meeting diagnostic criteria for major depressive disorder (MDD) without psychotic symptoms according to the DSM-5
* Currently experiencing a major depressive episode (MDE)
* Montgomery-Asperg Depression Rating Scale (MADRS) score greater than 20 at screening
* Patients meeting criteria for treatment-resistant depression (TRD): failure of 2 or more adequate trials of antidepressant therapy of adequate dose and duration during the current episode
* Enrollment in one of the treatment modalities (rTMS, IVK, or ECT) at the Interventional Psychiatry Program at St. Michael's Hospital, Unity Health Toronto
* Ownership of a smartphone (for participants using wearable devices)

Exclusion Criteria:

* Individuals without Internet access (required to access study platforms)
* Medication changes, aside from the treatment received through the IPP, one month (28 days) before screening, or during the entire duration of treatment (variable based on treatment arm)
* Participants who do not speak English fluently enough to successfully communicate study information, answer questions accurately, and/or obtain informed consent

Outcomes:
Feasibility
Effectiveness of a Smartphone Wellness App to Improve Mental Well-being: A Pilot Study
Trial ID: NCT04640090
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-08-12
Completion date: 2021-09-14
Sponsor: Washington University School of Medicine
Conditions: Musculoskeletal Diseases; Chronic Pain; Anxiety Disorders; Depression
Interventions: Smartphone app (Wysa)
Locations: Washington University School of Medicine
Countries: United States
Enrollment: 60
Age range: 18 Years - None
Sex: ALL

Summary:
This pilot project will evaluate the potential of an affordable smartphone app to improve users' mental well-being.

Eligibility criteria:
Inclusion Criteria:

* Presentation to an orthopedic specialist at the Washington University Living Well Center for a musculoskeletal condition
* Score of 55 or higher on Patient-Reported Outcomes Measurement Information System (PROMIS) Depression and/or Anxiety measures
* Advised to consider behavioral health treatment as a component of comprehensive care for a musculoskeletal condition
* Cannot or does not wish to pursue in-person behavioral health treatment (e.g., with the center psychologist)

Exclusion Criteria:

* No access to an electronic device (such as a smartphone) to download and use the wellness app and to electronically complete follow-up self-reported health measures
* In active mental health crisis (e.g., suicidal/homicidal thoughts, psychosis) which would require an escalated level of care
* Initiating care with a behavioral health specialist

Outcomes:
Mean change in anxiety symptoms; Mean change in depression symptoms
A Web-based Tailored Intervention to Improve the Quality of Life of Long-term Breast Cancer Survivors. A Randomized Controlled Trial
Trial ID: NCT05322460
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-12-15
Completion date: 2025-11
Sponsor: Cristina García-Vivar
Collaborators: Instituto de Salud Carlos III
Conditions: Long-term Effects of Cancer Treatment; Long-term Effects Secondary to Cancer Therapy in Adults
Interventions: CUMACA-M Program
Locations: Public University of Navarre
Countries: Spain
Enrollment: 182
Age range: 18 Years - 99 Years
Sex: FEMALE

Summary:
The number of breast cancer survivors is growing. More cured women are becoming long-term survivors of breast cancer (LS-BC) at least 5 years after diagnosis and after treatment has been completed. Some of these LS-BC return to daily life without any problems; however, in many cases, they experience and have to deal with physical sequelae (chronic fatigue, pain, etc.) psychological sequelae (anxiety, depression, fear of recurrence, etc.) and social sequelae (altered interpersonal relationships, difficulty in returning to work, etc.). For many LS-BC, these health problems are not being met by health professionals. Thus there is a need to promote greater continuity and coordination between specialized oncology care and primary health care in order to enhance specific follow-up of these women in the community. However, the role of the primary health care team in the provision of care in the long-term cancer survival is not clearly defined and few actions have been aimed at improving care activities, standardizing procedures and protocols, developing documentation and registries and updating the training of health care professionals. Therefore, this study aim to design, implement, and evaluate a web based tailored intervention, using artificial intelligence, to improve the quality of life of long-term breast cancer survivors, and self-efficacy for the management of late sequelae from primary care.

Eligibility criteria:
Inclusion Criteria:

* having been diagnosed with breast cancer
* having completed active oncological treatments (chemotherapy and/or radiotherapy) in a period of time greater than five years,
* be free of disease at the time of data collection
* be a woman and over 18 years of age
* have knowledge of Internet use.

Exclusion Criteria:

* Women with a diagnosis of cancer other than breast cancer
* Recurrence of cancer or metastasis that causes a new treatment
* Being in active treatment for recurrence or new cancer
* Being a man

Outcomes:
Quality of life Cancer Survivor (QOL-CS); Self-Efficacy for Managing Chronic Disease; Sociodemographic data of LS-BC
A Multi-center, Randomized, Controlled Clinical Study on the Efficacy of Artificial Intelligence-Assisted Colonoscopy in Improving the Screening of Colorectal Cancer and Precancerous Lesions.
Trial ID: NCT07307547
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-04
Completion date: 2028-12
Sponsor: Zhejiang University
Conditions: Colorectal Neoplasms; Colonic Polyp; Adenoma; Colorectal Cancer
Interventions: AI-Assisted Colonoscopy
Locations: The Second Affiliated Hospital, Zhejiang University School of Medicine
Countries: China
Enrollment: 3342
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
This study is a multi-center, randomized controlled trial designed to evaluate whether an artificial intelligence (AI) system can assist endoscopists to improve the detection rate of colorectal adenomas and cancers during colonoscopy compared to standard colonoscopy. Early screening and diagnosis are key to reducing the burden of colorectal cancer, but current colonoscopy has limitations, including the risk of missed lesions. This trial aims to determine if AI can enhance screening quality and diagnostic accuracy.

Eligibility criteria:
Inclusion Criteria:

1. Age between 18 and 75 years, inclusive.
2. Scheduled for a screening, diagnostic, or surveillance colonoscopy.
3. Able to understand the study protocol and provide written informed consent.

Exclusion Criteria:

1. Known contraindications to colonoscopy or biopsy.
2. Personal history of colorectal cancer, inflammatory bowel disease (IBD), or previous colorectal surgery.
3. Known or suspected colorectal polyposis syndrome (e.g., Familial Adenomatous Polyposis - FAP).
4. Patients with active colorectal bleeding, bowel obstruction, or toxic megacolon.
5. Women who are pregnant, planning to become pregnant, or are breastfeeding.
6. Participation in another interventional clinical trial within the 30 days prior to enrollment.
7. Any other condition that, in the investigator's judgment, would make the participant unsuitable for the study.

Outcomes:
Adenoma Detection Rate (ADR)
Real-World Validation of an Artificial Intelligence Characterization Support (CADx) System for Prediction of Polyp Histology in Colonoscopy: A Prospective Multicentre Study
Trial ID: NCT05034185
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-03-03
Completion date: 2022-10-01
Sponsor: Changi General Hospital
Collaborators: National University Hospital, Singapore; Singapore General Hospital; Tan Tock Seng Hospital
Conditions: Colonic Polyp; Colonic Neoplasms; Colonic Dysplasia
Interventions: Computer-aided diagnosis (CADx) support tool
Locations: Changi General Hospital, National University Hospital, Singapore General Hospital and Tan Tock Seng Hospital
Countries: Singapore
Enrollment: 450
Age range: 40 Years - None
Sex: ALL

Summary:
Colorectal cancer (CRC) is a leading cause of cancer-related morbidity and mortality worldwide, with rates of CRC predicted to increase. Colonoscopy is currently the gold standard of screening for CRC. Artificial intelligence (AI) is seen as a solution to bridge this gap in adenoma detection, which is a quality indicator in colonoscopy. AI systems utilize deep neural networks to enable computer-aided detection (CADe) and computer-aided classification (CADx). CADe is concerned with the detection of polyps during colonoscopy, which in turn is postulated to help decrease the adenoma miss-rate.

In contrast, CADx deals with the interpretation of polyp appearance during colonoscopy to determine the predicted histology. Prediction of polyp histology is crucial in helping Clinicians decide on a "resect and discard" or "diagnose and leave strategy". It is also useful for the Clinician to be aware of the predicted histology of a colorectal polyp in determining the appropriate method of resection in terms of safety and efficacy. While CADe has been studied extensively in randomized controlled trials, there is a lack of prospective data validating the use of CADx in a clinical setting to predict polyp histology.

The investigators plan to conduct a prospective, multi-centre clinical trial to validate the accuracy of CADx support for prediction of polyp histology in real-time colonoscopy.

Eligibility criteria:
Inclusion Criteria:

1. Patients who have an indication for colonoscopy and who have at least one polyp detected during colonoscopy
2. 40 years of age and above
3. Consent obtained for the study

Exclusion Criteria:

1. Less than 39 years of age
2. Declined participation in study
3. Patients with no polyps detected during colonoscopy
4. Patients with inflammatory bowel disease
5. Patients with known unresected colorectal cancer

Outcomes:
To evaluate the diagnostic performance of the CADx support tool compared to optical prediction of polyp histology by the Clinician in real-time colonoscopy in a clinical setting
Pre-therapeutic 68Ga-PSMA PET AI Based Dose Prediction for 177Lu-PSMA Targeted Radionuclide Therapy
Trial ID: NCT06268418
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-11-30
Completion date: 2025-06-30
Sponsor: Central Hospital, Nancy, France
Conditions: Radionucleide Therapy
Interventions: Artificial intelligence
Locations: CHRU de NANCY; Nuclear medicine Department CHRU de NANCY
Countries: France; France
Enrollment: 46
Age range: 18 Years - 99 Years
Sex: MALE

Summary:
Targeted Radionuclide Therapy (TRT) is a contemporary approach to radiation oncology, aiming to deliver the maximal destructive radiation dose via cancer-targeting radiopharmaceutical. Radioactive ligands for the prostate-specific membrane antigen (PSMA) have emerged for the treatment of metastatic castration-resistant prostate cancer (mCRPC).Normal organ and tumor dose can be assessed by a series of cross-sectional whole-body SPECT scans, however, these require a large amount imaging time and are often not feasible in routine clinical practice.

An alternative is to generate a 3D time integrated activity (TIA) map per patient based on the PBPK and the pre-therapy imaging

Eligibility criteria:
Inclusion Criteria:

* Patients who received at least one dose of 177Lu-PSMA and for whom a 68Ga-PSMA PET/CT was performed as part of IVRT in the "pre-treatment" assessment

Exclusion Criteria:

* Patient opposition to the use of their data as part of this research.

Outcomes:
Evaluate the prediction of the absorbed dose by Deep Learning approaches for RLT with 177Lu-PSMA, from pre-treatment 68Ga-PSMA.PET/CT images
Prospective Validation of an Artificial Intelligent System for Eye Emergency Triage and Primary Diagnosis
Trial ID: NCT05680090
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-12-10
Completion date: 2023-01-20
Sponsor: Sun Yat-sen University
Conditions: Emergencies; Eye Diseases
Interventions: Artificial intelligent system for eye emergency triage and primary diagnosis
Locations: Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity
Countries: China
Enrollment: 100
Age range: None - None
Sex: ALL

Summary:
Ophthalmic emergencies are acute vision-threatening disorders, for which a delay in prompt emergency response could result in catastrophic vision loss. Triage is an effective process for ensuring that timely emergency care is provided despite limited resource by prioritizing patients to appropriate orders for visits. Historically, registered nurses classify emergency patients based on personal experiences with high variation. Additionally, primary healthcare providers have been conventionally at the forefront of providing first aid care. However, most of ocular emergencies are wrongly diagnosed or referred due to non-eye specialists' limited knowledge and training in the ophthalmology.

Here, the investigators established and validated an artificial intelligence system, EE-Explorer, to triage eye emergencies and assist in primary diagnosis using metadata and ocular images. This system has been integrated into a website to be prospectively validated in the real world.

Eligibility criteria:
Inclusion Criteria:

1. Suffering acute ophthalmic symptoms within one month
2. Visiting the ocular emergency department for the first time
3. Must be able to complete the triage form for ophthalmic emergency
4. Must be able to cooperate either by submitting smartphone photographs or receiving slit-lamp examination

Exclusion Criteria:

The image quality does not meet the clinical requirements.

Outcomes:
The accuracy of the triage model