moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Tailored Supramarginal Resection in Glioblastoma Guided by Artificial Intelligence-based Recurrence Probability Maps. A Non-randomized Pilot Study
Trial ID: NCT05735171
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-11-01
Completion date: 2025-12-01
Sponsor: Hospital del Rio Hortega
Collaborators: UiT The Arctic University of Norway; University Hospital of North Norway; University of Valladolid
Conditions: Glioblastoma
Interventions: AI-guided surgery
Locations: University Hospital Rio Hortega
Countries: Spain
Enrollment: 20
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
Glioblastomas are the most common and poorly prognostic primary brain neoplasms. Despite advances in surgical techniques and chemotherapy, the median survival time for these patients remains less than 15 months. This highlights the need for more effective treatments and improved prognostic tools. The globally accepted surgical strategy currently consists of achieving the maximum safe resection of the enhancing tumor volume. However, the non-enhancing peritumoral region contains viable cells that cause the inevitable recurrence that these patients face. Clinicians currently lack an imaging tool or modality to differentiate neoplastic infiltration in the peritumoral region from vasogenic edema. In addition, it is not always feasible to include all the T2-FLAIR signal alterations surrounding the enhancing tumor in the surgical planning due to the proximity of eloquent areas and the higher risk of postoperative deficits.

However, the investigators have developed a model to predict regions of recurrence based on machine learning and MRI radiomic features that have been trained and evaluated in a multi-institutional cohort.

The investigators aim to analyze whether an adjusted supramarginal resection guided by these new recurrence probability maps improves survival in selected patients with glioblastoma.

Eligibility criteria:
Inclusion Criteria:

* A suspected diagnosis of supratentorial glioblastoma by MRI.
* Tumor in non eloquent brain region according to the UCSF (University of California, San Francisco) classification, including the sensor motor areas (precentral and postcentral gyri), perisylvian language areas in the dominant hemisphere (superior temporal, inferior frontal, and inferior parietal gyri), basal ganglia, internal capsule, thalamus, and visual cortex around the calcarine sulcus
* Indication for surgical treatment and where supramarginal resection is considered possible according to the preoperative imaging. This consideration needs to be verified by two specialists in neurosurgery. This criterion needs to be verified by two senior neurosurgeons.
* Karnofsky Performance Score ≥ 70;
* Written informed consent

Exclusion Criteria:

* Tumors in eloquent areas.
* Recurrent gliomas (except biopsy)
* MR image data not usable due to artifacts during acquisition. Inability to give written informed consent
* KPS \< 70
* Severe comorbidity.

Outcomes:
Feasibility using eligibility; Feasibility using the proportion of consent
The Value of Artificial Intelligence-based 18F-FDG PET/CT in Diferential Diagnosis, Efficacy Prediction and Prognosis Prediction of T-NK Cell Lymphoma: a Clinical Study
Trial ID: NCT06747299
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-01-01
Completion date: 2027-12-31
Sponsor: Ruijin Hospital
Collaborators: West China Hospital
Conditions: T-NK Cell Lymphoma
Locations: Ruijin Hospital affiliated to Shanghai Jiao Tong University of Medicine
Countries: China
Enrollment: 200
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
Based on the PET/CT imaging data of patients with T-NK cell lymphoma, machine learning and deep learning methods are used to extract imaging features, establish a T-NK cell lymphoma prediction model, and provide more scientific and accurate prognosis prediction for the clinic.

Eligibility criteria:
Inclusion Criteria:

1\. Pathological histology confirmed as T-NK Cell Lymphoma; 2.18F-FDG PET/CT examination before treatment; 3. Using modern best practice treatment options; 4. Complete clinicopathological and follow-up data were obtained.

Exclusion Criteria:

1. The patient had previously received antitumor therapy;
2. The patient had a history of other tumors;
3. Incomplete clinical information or imaging data;
4. Concomitant other malignant tumors.

Outcomes:
Evaluation the value of Artificial Intelligence-based 18F-FDG PET/CT of T-NK Cell Lymphoma
An Artificial Intelligence - Assisted Model for Predicting Optimal Timing of Surgery in Advanced Ovarian Cancer: the Combined Study of the SUNNY Trial and the Real World Data
Trial ID: NCT06839157
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2016-08-22
Completion date: 2027-06
Sponsor: Shanghai Gynecologic Oncology Group
Collaborators: Shanghai Zhongshan Hospital; Sun Yat-Sen University Cancer Center; Fudan University; Zhejiang Cancer Hospital
Conditions: Ovarian, Fallopian, and Primary Peritoneal Cancer; Artificial Intelligence (AI)
Enrollment: 1025
Age range: 18 Years - None
Sex: FEMALE

Summary:
This study integrates data from the randomized controlled SUNNY trial (RCT) and real-world (RWD) data, and employs multimodal data fitting to construct a medical artificial intelligence model to identify the clinical characteristics of patient subgroups suitable for primary debulking surgery (PDS) or interval debulking surgery (IDS), and the cutoff values for selecting different timings of surgery for advanced ovarian cancer.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18 years
* Patients who were included in the SUNNY study or who were newly diagnosed with stage IIIC or IV primary epithelial ovarian cancer, fallopian tube cancer, or primary peritoneal cancer during the SUNNY study period (2015-2023)
* Underwent primary debulking surgery or interval debulking surgery
* Data avaliable on first-line treatment and follow-up

Exclusion Criteria:

* Non-epithelial ovarian cancer or borderline tumors.
* Low-grade tumors.
* Mucinous ovarian cancer.
* Missing data on first-line treatment and follow-up

Outcomes:
The accuracy of predicting the 3-year overall survival (OS) difference between primary debulking surgery (PDS) and interval debulking surgery (IDS).
Efficacy of the Artificial Intelligence HeartBot II in Increasing Awareness and Knowledge of Heart Attack in Women: Study Protocol for a Randomized Controlled Trial With a Waitlist Control
Trial ID: NCT07416734
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-03-01
Completion date: 2027-06-30
Sponsor: University of California, San Francisco
Collaborators: University of California, Davis
Conditions: Participants Must be Women Aged 25 Years or Older; Participants Should Have no Self-reported History of Heart Disease or Stroke; Participants Should Have no Terminal Illness or Diagnosed Cognitive Impairment, Including Alzheimer's Disease; Participants Should Not be a Healthcare Professionals or Healthcare Trainees; Participants Should Not be Employed in the Healthcare Field; Participants Should Reside in the United States and be a University of California, San Francisco Health Patient; Participants Should Possess a Smartphone With an Active Data Plan or Access to Wi-Fi
Interventions: HeartBot II Program; Waitlist Control
Locations: University of California, San Francisco
Countries: United States
Enrollment: 200
Age range: 25 Years - None
Sex: FEMALE

Summary:
The goal of this clinical trial is to find out whether an app-based program called HeartBot II, which uses an artificial intelligence (AI) chatbot, can help improve women's awareness and knowledge of heart attacks in women.

This is an online study with about 200 women taking part. Participants will be randomly assigned (by chance) to one of two groups: an intervention group or a wait-list control group.

Participants in the intervention group will begin using the HeartBot II program right away. Participants in the wait-list control group will wait 12 weeks before starting the HeartBot II program. The HeartBot II program includes four short modules. In each module, participants will interact with a chatbot and spend about 10 to 15 minutes completing the content.

Participants will be asked to complete an online screening and baseline survey at the start of the study, as well as two follow-up surveys at 12 weeks and 24 weeks.

Eligibility criteria:
Inclusion Criteria:

* Women
* 25 years or older
* Living in the US
* University of California, San Francisco Health patient
* Possessing a smartphone with a data plan or Wi-Fi access

Exclusion Criteria:

* Individuals who identify as male
* Having self-reported cognitive impairment
* Having history of heart disease or stroke
* Healthcare professional/trainee
* Working in the healthcare field

Outcomes:
Awareness and knowledge of symptoms and response to a heart attack
The Relationship of Artificial Intelligence Literacy With Academic Achievement, Reading Habits, Smartphone Addiction, and Internet Addiction Among University Students
Trial ID: NCT06689319
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-11-15
Completion date: 2025-04-15
Sponsor: Nagihan Acet
Conditions: Technology Literacy; Reading Habits; Smartphone Addiction; Internet Addiction; Academic Acheivement
Interventions: Assessment of Artificial Intelligence Literacy
Locations: Atılım University
Countries: Turkey (Türkiye)
Enrollment: 184
Age range: 18 Years - 35 Years
Sex: ALL

Summary:
This study investigates the relationships between artificial intelligence (AI) literacy and factors such as academic achievement, reading habits, smartphone addiction, and internet addiction among university students. As AI technologies become increasingly integrated into daily life, AI literacy-necessary for understanding and evaluating AI-is emerging as a critical skill. While factors like academic success and regular reading habits may enhance AI literacy, behaviors like smartphone and internet addiction may have an adverse effect by promoting superficial information access over deeper critical engagement. This prospective, observational, and cross-sectional study will assess AI literacy using the Artificial Intelligence Literacy Scale and analyze its association with academic and behavioral factors. The study will be conducted among participants aged 18-35 in the Physiotherapy and Rehabilitation Department Laboratory at Atılım University. Data will be evaluated using descriptive statistics, correlation analyses (Pearson or Spearman, depending on distribution), and significance testing. The results may highlight the impact of academic and behavioral factors on AI literacy, offering insights for educational strategies aimed at fostering critical AI competencies.

Eligibility criteria:
Inclusion Criteria:

* Being between 18-35 years of age.
* Willingness to participate after receiving detailed information about the study's purpose and methodology.

Exclusion Criteria:

* Missing responses in questionnaires.
* Illiteracy.
* Inability to cooperate.

Outcomes:
Assessment of reading habits; Assessment of smartphone addiction; Assessment of internet addiction; Assessment of academic achievement
Postpartum Education Via Artificial Intelligence for Recovery and Loneliness (PEARL): A Randomized Controlled Trial
Trial ID: NCT07223736
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-11-10
Completion date: 2026-11-01
Sponsor: University of California, San Diego
Conditions: Pelvic Floor Disorder; Loneliness; Postpartum; Mental Health; Depression; Peritraumatic Distress; Women; Artificial Intelligence (AI); Chatbot
Interventions: Generative artificial intelligence (genAI) postpartum chatbot; Standard of Care (SOC)
Locations: University of California San Diego
Countries: United States
Enrollment: 130
Age range: 18 Years - None
Sex: FEMALE

Summary:
The goal of this clinical trial is to learn whether a postpartum chatbot powered by generative artificial intelligence (genAI) can help new mothers get better pelvic floor health information and feel less lonely after childbirth.

The main questions this study aims to answer are:

* Does using the chatbot improve postpartum pelvic floor health knowledge?
* Does using the chatbot help reduce feelings of loneliness during the postpartum period?
* Does using the chatbot impact pelvic floor symptoms?

Researchers will compare standard postpartum care to standard care plus the chatbot.

Participants will:

Be assigned by chance (like flipping a coin) to standard postpartum care with or without access to the chatbot.

If in the chatbot group, participants will receive education and support via the chatbot over a 4-week period.

Both groups will complete questionnaires to measure their pelvic floor knowledge, pelvic floor symptoms, feelings of loneliness, depression, infant bonding, perceived social support, adverse childhood experiences, and peri-traumatic distress.

The chatbot was created by urogynecology experts in collaboration with UC San Diego computer science and biomedical informatics researchers. The chatbot is designed to give new mothers personalized, evidence-based information and support in real time.

Eligibility criteria:
Inclusion Criteria:

1. Has the capacity to provide informed consent
2. Stated willingness to comply with all study procedures and availability for the duration of the study
3. Postpartum persons, aged \>18 years old
4. Primiparous
5. Vaginal or cesarean delivery
6. English Speaking
7. Internet access and proficiency of internet access
8. Access to a smartphone
9. Postpartum 2-6 weeks

Exclusion Criteria:

1. Multiparous
2. Major neonatal anomaly
3. Delivery \< 34 weeks gestational age
4. Intrauterine fetal demise (IUFD)
5. Enrollment in any interfering studies
6. Unanticipated NICU admission
7. Discharge home without live baby
8. Surrogates/gestational carrier
9. Birthing individuals with baby placed for adoption
10. Currently pregnant
11. Psychiatric history requiring psychiatric hospitalization prior to delivery
12. Other psychiatric conditions needing immediate attention and intervention as determined by study team and/or treatment team

Outcomes:
Prolapse and incontinence knowledge
AID-OMIE - Artificial Intelligence in Detection of Occlusive Myocardial Infarction in Emergency Medicine
Trial ID: NCT06767709
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-09-01
Completion date: 2026-05-31
Sponsor: Institute of Mountain Emergency Medicine
Conditions: Cardiac Arrest, Out-Of-Hospital; Cardiac Arrest Due to Underlying Cardiac Condition
Enrollment: 200
Age range: 18 Years - None
Sex: ALL

Summary:
Study Objective and Hypothesis The study hypothesizes that artificial intelligence (AI)-assisted interpretation of the 12-lead electrocardiogram (ECG) can improve the care of patients resuscitated after out-of-hospital cardiac arrest (OHCA) by enabling faster and more accurate detection of occlusion myocardial infarction (OMI). This enhanced diagnostic approach could reduce the time required for revascularization, improve patient outcomes, and decrease unnecessary activations of cardiac catheterization laboratories. The primary objective of the study is to assess the effectiveness of an AI-powered ECG model in identifying acute OMI in OHCA patients whose post-return of spontaneous circulation (ROSC) ECG does not show ST-elevation.

Methods

This is a retrospective observational study involving OHCA patients in Bolzano, Italy, who meet the following inclusion criteria:

Aged 18 years or older. Achieved ROSC after cardiac arrest. Underwent coronary angiography (CAG) within seven days post-OHCA. Prehospital post-ROSC ECG and CAG reports available.

Exclusion criteria include in-hospital cardiac arrest (IHCA), traumatic cardiac arrest, cardiac arrest from a non-cardiac cause, and poor-quality or corrupted ECG images. Post-ROSC ECGs will be analyzed using the PMcardio App, an AI tool for ECG interpretation. The data will be fully anonymized before storage. Coronary angiography charts will be reviewed for the presence of atherosclerotic lesions, the degree of arterial narrowing, and Thrombolysis in Myocardial Infarction (TIMI) flow, which assesses blood flow in coronary arteries.

Study Outcomes The primary outcome is the sensitivity and specificity of the AI-assisted ECG in detecting OMI in patients whose post-ROSC ECG does not show ST-elevation. Secondary outcomes include the frequency of OMI in OHCA patients without ST-elevation and the ability of the AI model to rule out OMI accurately in these cases.

Eligibility criteria:
Inclusion Criteria:

* OHCA from with ROSC in the Province of Bolzano, Italy
* Coronary angiography (CAG) within 7 days post-OHCA
* Age \> 18 years
* Available prehospital post-ROSC ECG
* Available CAG report

Exclusion Criteria:

* In-Hospital Cardiac Arrest (IHCA)
* Age \< 18 years
* Traumatic cardiac arrest
* Cardiac arrest from a clear non-cardiac cause
* Corrupted ECG images
* Poor ECG digitalization quality

Outcomes:
Sensitivity and specificity of detecting OMI from the post-ROSC ECG with AI-assisted ECG interpretation in patients following OHCA with ROSC, where the post-ROSC ECG does not show ST-elevation.
Comparison of Delivery Format of Cognitive Behavioral Therapy Materials on Engagement and Symptom Reduction: AI-Enabled App vs PDF Workbook
Trial ID: NCT06459128
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-06-05
Completion date: 2024-07-17
Sponsor: Limbic Limited
Conditions: Mental Health Issue
Interventions: Limbic Care; Digital Workbook
Locations: Limbic Limited
Countries: United Kingdom
Enrollment: 540
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this clinical trial is to compare two different ways of delivering cognitive behavioral therapy (CBT) materials to people with symptoms of anxiety and depression. The main questions it aims to answer are:

1. Do people using the app experience greater reductions in anxiety and depression symptoms compared to those using the workbook?
2. Does the personalization offered by the app increase engagement with CBT materials compared to the workbook?

Participants will be given access to either the Limbic Care app or an equivalent PDF workbook and will be asked to engage with the materials for several days each week over a period of 6 weeks. Each week, participants will complete a survey about their anxiety and depression symptoms and report any new health issues and their experiences with the materials.

Eligibility criteria:
Inclusion Criteria:

* Has a personal smartphone with Internet access with an accepted operating system: iOS (any) or Android (10 or later)
* Has a GAD-7 score \>= 8 OR a PHQ-9 score \>= 10 (indicating anxiety and/or depression symptoms above a clinical threshold, according to NHS Talking Therapies guidelines)
* US resident
* Fluent in English

Exclusion Criteria:

* Currently undergoing mental health therapy
* Changed psychotropic medication in the past 8 weeks (this includes starting/stopping medication or increasing/decreasing dosage)
* Self-identify as being at risk of harming themselves or others
* Have used the Limbic app before
* Use recreational drugs more frequently than once per week
* Consume \>= 10 alcohol units per week

Outcomes:
Change in GAD-7 (Anxiety); Change in PHQ-9 (Depression); Therapy Completion; Engagement Duration
Data Collection for the Validation of an AI Software to Support MSK Ultrasound Examination
Trial ID: NCT07336407
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-09-01
Completion date: 2025-11-30
Sponsor: Smart Alfa Teknoloji San. ve Tic. A.S.
Conditions: Ultrasound Imaging of Anatomical Structures
Interventions: Ultrasound Scans
Locations: Scripps Clinic Torrey Pines; AABP Integrative Pain Care and Wellness Clinic; Memorial Hermann Houston Health & Sports Medicine
Countries: United States; United States; United States
Enrollment: 79
Age range: 18 Years - None
Sex: ALL

Summary:
This study conducted at three locations, aiming to collect ultrasound images from volunteers and assess the performance of an AI software, MSK Go, using these images.

The software to be assessed is designed to assist physicians and healthcare professionals in performing ultrasound exams by classifying scan views and identifying key anatomical structures during a musculoskeletal ultrasound examination.

The main question the study aims to answer is whether the AI software performs safely and effectively for future clinical use.

Eligibility criteria:
Inclusion Criteria:

* Provision of a signed and dated informed consent form
* Stated willingness to comply with all study procedures and availability for the duration of the study

Exclusion Criteria:

* Pregnancy
* Inability to lie flat
* Open skin lesions or active infection at the scan site
* Anatomical deformity in the joints to be scanned

Outcomes:
Rate of spatial (area) overlap of anatomical structure areas on the ultrasound image; Rae of correctly classifying the views of the joint
AI-based Prediction of Cardiac Function Using Echocardiography and Body Composition Data (ECHO-FIT Study)
Trial ID: NCT06811519
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-02-24
Completion date: 2028-12-31
Sponsor: Yonsei University
Conditions: Heart Failure; Left Ventricular (LV) Systolic Dysfunction; Body Composition Measurement; Artificial Intelligence (AI)
Interventions: Body Composition Analyzer (ACCUNIQ BC720)
Locations: Yongin Severance Hospital
Countries: South Korea
Enrollment: 2000
Age range: 20 Years - None
Sex: ALL

Summary:
This prospective observational study (ECHO-FIT Study) aims to develop and validate a predictive model for cardiac function, particularly left ventricular ejection fraction (LVEF), by integrating echocardiographic measurements with body composition data obtained from the QCCUNIQ BC 720 device.

The study plans to enroll 2,000 adult participants, comprising 1,000 individuals with normal LVEF (≥50%) and 1,000 with heart failure (LVEF \<50%), all of whom will undergo standard-of-care echocardiography and body composition analysis.

By analyzing the relationships between key echocardiographic parameters (such as LVEF and diastolic function) and body composition measures (including fat mass, skeletal muscle mass, and total body water), we will develop a non-invasive prediction model capable of identifying individuals at higher risk of cardiac dysfunction.

This innovative approach has the potential to enhance early detection and personalized management of heart failure, reduce dependence on resource-intensive diagnostic procedures, and ultimately improve patient outcomes.

Eligibility criteria:
Inclusion Criteria:

* Aged 20 years or older.
* Undergoing a standard echocardiographic examination.
* Providing consent to undergo body composition analysis.
* Signing the informed consent form to voluntarily participate in the study.

Exclusion Criteria:

* Having a physical or mental condition that makes it impossible to conduct an echocardiogram or perform body composition analysis.
* Deemed inappropriate for study participation by the researcher (e.g., unable to cooperate).

Outcomes:
Left Ventricular Ejection Fraction < 50%