moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
AI-MEL: Image Analysis and Machine Learning for Early Diagnosis and Risk Prediction in Children, Adolescents and Young Adults
Trial ID: NCT06621810
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-12-01
Completion date: 2026-11-30
Sponsor: German Cancer Research Center
Collaborators: Universität Tübingen; University of Florence; Fundacio Clinic Barcelona; Hospital Clinic of Barcelona
Conditions: Melanoma (Skin Cancer); Artificial Intelligence (AI); Pediatric Cancer
Locations: University of Tübingen; University of Florence; Hospital Clínic de Barcelona
Countries: Germany; Italy; Spain
Enrollment: 3000
Age range: None - None
Sex: ALL

Summary:
The goal of this study is to develop supportive diagnostic artificial intelligence algorithms to distinguish melanoma from nevi or other benign pigmented skin lesions, especially in younger patients (below the age of 30). The main goals it aims to achieve are:

* development of an algorithm based on dermatoscopic images, targeting skin cancer screening in vulnerable populations
* development of another algorithm based on histological images, intended to be used by pathologists on lesions that are still suspicious of melanoma after dermatologic assessment
* implementation of explainability methods to enable the user to better comprehend the systems' decisions, avoid biases and increase trust in these applications

There is no additional time commitment for the study participants for this study, as the data used in this project will be collected in routine clinical practice anyway.

Eligibility criteria:
Inclusion Criteria:

\-

Exclusion Criteria:

* Patients without a melanoma or nevus diagnosis
* images with insufficient image quality

Outcomes:
Area Under the Receiver Operator Curve (AUROC)
Evaluation of the Artificial Intelligence/Machine Learning-Based Diagnostic Software as a Medical Device Using Forced Cough Vocalization Signal Data Signatures in the Diagnosis of COVID-19 Illness
Trial ID: NCT05175690
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-01-10
Completion date: 2022-05-03
Sponsor: AudibleHealth AI, Inc.
Collaborators: University of South Florida; R. P. Chiacchierini Consulting, LLC; Analytic Solutions Group; Renaissance Worldwide Solutions, LLC; Medical & Regulatory Affairs Specialists, LLC
Conditions: COVID19
Interventions: Diagnostic Software as Medical Device
Locations: University of South Florida
Countries: United States
Enrollment: 1126
Age range: 18 Years - None
Sex: ALL

Summary:
The AudibleHealth Dx is a diagnostic software as a medical device (Dx SaMD) consisting of an ensemble of software subroutines that interacts with a proprietary database of Signal Data Signatures (SDS), using Artificial Intelligence/Machine Learning (AI/ML) to analyze forced cough vocalization signal data signatures (FCV-SDS) for diagnostic purposes.

This study will evaluate the performance of the AudibleHealth Dx in comparison to a standard of care Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) test for the diagnosis of COVID-19. Bidirectional Sanger sequencing will be used to reduce the rate of false negative and false positive results.

A secondary purpose of the study will be usability testing of the device for participants and providers.

Eligibility criteria:
Inclusion Criteria:

* Male or Female, 18 years of age or older
* Present for elective, outpatient COVID-19 RT-PCR testing
* Meet the FDA EUA approved indications for use for the RT-PCR nasal swab test for COVID-19
* Stated willingness to comply with all trial procedures and availability for the duration of the trial
* Informed consent must be obtained prior to testing

Exclusion Criteria:

* Less than 18 years of age
* Unable to cough voluntarily
* Present with acute traumatic injury to the head, neck, throat, chest, abdomen or trunk
* Patent tracheostomy stoma
* Recent chest/abdomen/trunk trauma or surgery, recent/persistent neurovascular injury or recent intracranial surgery
* Medical history of cribriform plate injury or cribriform plate surgery, diaphragmatic hernia, external beam neck/throat/maxillofacial radiation, phrenic nerve injury/palsy, radical neck/throat/maxillofacial surgery, vocal cord trauma or nodules
* Since persons with aphasia may have difficulty in producing an FCV-SDS in the time allotted by the app, this population also will be excluded from the current trial

Outcomes:
Non-inferiority of the positive percent agreement (PPA); Non-inferiority of the negative percent agreement (NPA)
Standardized Hypnotic Susceptibility Testing to Facilitate Development of a Machine Learning Tool to Characterize Physiological Biomarkers of Calm and Tranced States
Trial ID: NCT07102810
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-08-04
Completion date: 2025-09-09
Sponsor: Icahn School of Medicine at Mount Sinai
Collaborators: Entrancing Entertainment, Inc.
Conditions: Disorder; Trance; Anxiety
Interventions: Standardized Hypnotic Susceptibility Testing
Locations: Mount Sinai Hospital
Countries: United States
Enrollment: 50
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
This study seeks to contribute to the growing body of literature on hypnosis by providing robust, data-driven insights into the physiological mechanisms underlying trance states. The integration of electroencephalogram (EEG) and other wearable-derived physiological data will offer a comprehensive assessment of the changes that occur during a standardized hypnosis protocol: the Harvard Group Scale of Hypnotic Susceptibility (HGSHS:A). The results of this study are intended to facilitate derivation and validation of an Artificial Intelligence/Machine Learning (AI/ML)-based monitor that quantifies a patient's instantaneous emotional/arousal state along the spectrum that spans anxiety through states of calmness and trance. Future investigations will explore the ability of using such an interactive virtual system as a component of a closed-loop adaptive device to create optimal states of non-pharmacological sedation using personalized audiovisual content to allay anxiety and discomfort during medical procedures, such as percutaneous biopsies.

Eligibility criteria:
Inclusion Criteria:

* Written informed consent obtained from participant and ability and willingness for participant to comply with the requirements of the study.
* Adults of all genders, ages 18-65
* Healthy volunteers
* English-speaking

Exclusion Criteria:

* Participating currently in experimental drug trials.
* Recent (\<1 year) or current history of substance use disorder.
* Diabetes T1 or T2, major cardiovascular or respiratory diseases, major neurological diseases, or limited mobility
* Presence of a condition or abnormality that in the opinion of the investigator would compromise the safety of the patient or the quality of the data.
* Adults that cannot consent.
* Chronic use of psychoactive medications.
* Chronic use of antiepileptic medications.
* Active substance use disorder.
* Participants reporting significant phobias or anxiety disorders triggered by imagery or situations involving insects (specifically flies), enclosed spaces or elevators (claustrophobia), or heights (acrophobia).

Outcomes:
Harvard Group Scale of Hypnotic Susceptibility (HGSHS:A) Total Score
Accuracy of Guided Implant Placement Using Artificial Intelligence Segmentation Versus Conventional Technique: A Randomized Clinical Trial
Trial ID: NCT06967090
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-06-01
Completion date: 2026-07
Sponsor: Fayoum University
Conditions: Dental Implant
Interventions: Implant surgery using surgical guide
Enrollment: 20
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
To compare the accuracy of surgical guided implant using AI tooth segmentation of the CBCT alone without intraoral scan to fabricate the implant guide versus conventional technique by alignment the CBCT and intraoral scan to fabricate the implant guide in posterior implant.

Eligibility criteria:
Inclusion Criteria:

1. Patients with missing posterior teeth require implant surgery.
2. Patients have remaining teeth that can support the surgical guide.
3. The edentulous area should involve healed bone sites at least 3 months after extraction.
4. The edentulous area should have bone height of at least 10 mm from the alveolar crest to the nearest vital structure and bone width of at least 6 mm.
5. Age: patient above 18 years old.
6. All patients are in good health with no systemic, immunologic or debilitating diseases that could affect normal bone healing.
7. All selected patients are non-smokers and non-alcoholics. Patients are free from temporomandibular disorders and abnormal (TMD) oral habits such as bruxism.

Exclusion Criteria:

1. Systemic disease that may affect bone quality.
2. Patients with poor oral hygiene and active periodontal diseases.
3. Patient with limited mouth opening.

Outcomes:
The accuracy of the placed implants; Horizontal apex deviation, (Measuring unit in millimeter); Angular deviation, (Measuring unit in degree)
Artificial Intelligence-Based Evaluation of Chest X-Rays in the Diagnosis of Ventilator-Associated Pneumonia in the Intensive Care Unit: A Retrospective Comparative Study
Trial ID: NCT07509697
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2026-03-01
Completion date: 2026-03-25
Sponsor: Dr Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital
Conditions: Ventilator-Associated Pneumonia; Intensive Care Unit (ICU); Pneumonia, Bacterial; Artificial Intelligence (AI); Radiography, Thoracic
Locations: Dr. Abdurrahman Yurtaslan Ankara Oncology Hospital
Countries: Turkey (Türkiye)
Enrollment: 119
Age range: 18 Years - None
Sex: ALL

Summary:
Ventilator-associated pneumonia (VAP) is a common and serious infection in critically ill patients receiving mechanical ventilation in intensive care units (ICUs). One of the key diagnostic criteria for VAP is the presence of a new or progressive infiltrate on chest X-ray; however, interpretation of bedside chest radiographs is often challenging and subject to inter-observer variability.

This retrospective observational study aims to evaluate the role of artificial intelligence (AI) in the assessment of chest X-rays in patients with VAP. Chest radiographs obtained before and at the time of VAP diagnosis will be analyzed using a deep learning-based AI tool (Chester the AI Radiology Assistant), and changes in "infiltration" and "pneumonia" probability scores will be assessed.

AI-based findings will be compared with clinical decisions and independent radiologist evaluations regarding the presence of new infiltrates. The study aims to determine the level of agreement between these approaches and to explore whether AI-based analysis can support a more objective and standardized interpretation of chest radiographs in the diagnosis of VAP.

Eligibility criteria:
Inclusion Criteria:

* Adult patients (≥18 years)
* Admission to the anesthesia intensive care unit
* Requirement of invasive mechanical ventilation for at least 48 hours
* Clinical diagnosis of ventilator-associated pneumonia (VAP) based on institutional criteria
* Availability of at least one chest X-ray prior to VAP diagnosis and one chest X-ray at the time of diagnosis
* Availability of digital chest radiographs in the PACS system suitable for analysis

Exclusion Criteria:

* Age \<18 years
* Absence of accessible digital chest radiographs
* Chest radiographs with severe technical limitations preventing evaluation
* Chest radiographs that could not be processed by the AI system (no score generated)
* Presence of extensive pre-existing infiltrative lung disease preventing reliable assessment of new infiltrates
* Missing key clinical data (e.g., VAP diagnosis date, mechanical ventilation duration)

Outcomes:
Change in Chester AI-derived Infiltration and Pneumonia Probability Scores Between Pre-diagnosis and VAP Diagnosis Chest X-rays
Digital Support for Mental Health Intervention in Patients With Inflammatory Bowel Disease
Trial ID: NCT06575660
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-09-23
Completion date: 2025-10-09
Sponsor: University of Chicago
Collaborators: Cedars-Sinai Medical Center
Conditions: Inflammatory Bowel Diseases; Anxiety; Depression; Ulcerative Colitis; Crohn Disease
Interventions: Mental health support session
Locations: University of Chicago
Countries: United States
Enrollment: 22
Age range: 18 Years - None
Sex: ALL

Summary:
This is a small, exploratory study that will investigate using an artificial intelligence (AI) and virtual reality (VR), digital wellness application (app) to deliver a mental health support session in outpatient and hospitalized patients with inflammatory bowel disease (IBD) and co-existing symptoms of mild to moderate anxiety or depression.

The purpose of this study is to explore if a mental health support session using the app is feasible, safe, and acceptable to IBD patients and whether it could possibly help with physical and comorbid psychological symptoms of these patients.

Eligibility criteria:
Inclusion Criteria:

* 18+ years of age
* Patients with Inflammatory Bowel Disease (Crohn's Disease or Ulcerative Colitis)
* English-speaking
* Patients with score of 5-14 on General Anxiety Disorder-7 (GAD-7) questionnaire or 5-19 on Patient Health Questionnaire-9 (PHQ-9) within 3 months
* Able to provide written consent

Exclusion Criteria:

* Unwilling and/or unable to participate
* Non-English speakers
* Self-reported history of severe motion sickness
* The presence of a facial/head deformity that will prohibit comfortably wearing of a VR headset
* Legally blind or deaf
* Having had a seizure in the past year
* Unable to understand the instructions or to consent to participation in the study.
* Those who are pregnant

Outcomes:
Determine the feasibility of utilizing a GPT-4-powered AI-VR mental health support session in patients with IBD; Determine the safety of utilizing a GPT-4-powered AI-VR mental health support session in patients with IBD; Determine the patient acceptance of utilizing a GPT-4-powered AI-VR mental health support session in patients with IBD
Clinical Application of AI-assisted Ultrasound Technology in the Preoperative Evaluation of Thyroid Cancer
Trial ID: NCT06498674
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-07-01
Completion date: 2025-06-30
Sponsor: Fujian Medical University
Conditions: Thyroid Cancer
Interventions: Supplementary Pathological Examination; No Need for Supplementary Pathological Examination
Locations: Fujian Medical University Union Hospital
Countries: China
Enrollment: 300
Age range: None - None
Sex: ALL

Summary:
This study aims to explore the application of AI-assisted ultrasound technology in the preoperative assessment of thyroid cancer. Traditional ultrasound examination data from thyroid cancer patients will be collected, and AI systems will be utilized to detect and diagnose thyroid nodules and lymph nodes. In cases where there is disagreement between the two-dimensional ultrasound and AI system results, further confirmation will be sought through biopsy. Subsequently, pathological results will serve as the "gold standard" for comparison between the AI system and traditional ultrasound examination results, assessing their accuracy and reliability. Through this research endeavor, a more accurate and reliable method for preoperative assessment of thyroid cancer is aspired to be offered, thereby supporting clinical decision-making and paving the way for novel applications of AI in the field of medical imaging diagnosis.

Eligibility criteria:
Inclusion Criteria:

* Patients with preoperative pathological confirmation of thyroid malignant tumors undergoing surgical treatment.
* Patients with benign thyroid tumors, such as thyroid adenomas causing compressive symptoms, undergoing surgical treatment.
* Patients with complete and high-quality traditional two-dimensional color ultrasound images.
* Complete postoperative pathology reports.
* Willingness to participate in this clinical trial and signing of informed consent.

Exclusion Criteria:

* Patients with a history of neck surgery or radiotherapy.
* Patients with a history of malignant tumors in other parts of the body.
* Patients with thyroid dysfunction.
* Incomplete or poor-quality traditional two-dimensional color ultrasound images.
* Incomplete postoperative pathology reports.
* Refusal to participate in this clinical trial.

Outcomes:
Supplementary Pathological Examination; Change in Treatment Decision
Immunotolerance in Endometrial Cancer and at the Maternal-fetal Interface: Immunological
Trial ID: NCT06250010
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-12-13
Completion date: 2025-12-13
Sponsor: Regina Elena Cancer Institute
Collaborators: Federico II University
Conditions: Endometrial Cancer
Interventions: tissue samples (healthy and tumor taken from the same patient) and tissue samples (decidualized endometrium and trophoblast taken from it patient)
Locations: "Regina Elena" National Cancer Institute
Countries: Italy
Enrollment: 30
Age range: 18 Years - None
Sex: FEMALE

Summary:
This pilot/exploratory study will be configured as a non-retro-prospective study interventional on endometrial tissue samples taken from surgically treated patients at the Regina Elena National Cancer Institute, IRE - IFO and stored at the Biobank of same Institute (BBIRE) (cohort 1) and on samples of decidualized endometrium and trophoblast from patients with ongoing spontaneous abortion treated surgically at the UOC of Gynecology ed Obstetrics of the Federico II University Polyclinic of Naples (cohort 2)

Eligibility criteria:
Inclusion Criteria:

Cohort 1: Oncology patients

* Age \>18 years
* Histological diagnosis of endometrial hyperplasia with and without atypia, endometrial carcinoma endometrioid histotype at any stage of the disease (FIGO I-IV), patients subjected to hysterectomy for benign extra-endometrial pathology, patients with recurrence/metastasis from endometrioid endometrial carcinoma who undergo surgery;
* Adequate biological material to be able to carry out the planned analyses;
* Written informed consent (only for patients in the prospective part and/or in follow up/traceable);
* For the retrospective part: availability of samples adequately stored at the biobank of the Institute and availability of data relating to follow-up (at least 36 months).

Cohort 2: Patients with ongoing spontaneous abortion

* Age \>18 years;
* Diagnosis of ongoing spontaneous abortion which is subjected to an instrumental review procedure of the uterine cavity, under hysteroscopic guidance;
* Adequate biological material to be able to carry out the analyzes previously described;
* Written informed consent.

Exclusion Criteria:

* Comorbidities not controlled with adequate medical therapy;
* Infections of the endometrial cavity (pyometra);
* Synchronous tumors;
* Neoadjuvant treatments;
* Previous radiation treatments on the pelvic region.

Outcomes:
Description of genetic signatures shared between the maternal-fetal interface; Calculation of potential immuno-score; characterize the risk classes
Embedded Narrative in Interactive Game Design for Improving Medication Adherence of Schizophrenia：Exploratory Research
Trial ID: NCT06175559
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-09-01
Completion date: 2025-09-01
Sponsor: Zhu Dian
Conditions: Schizophrenia and Related Disorders; Medication Adherence; Mental Health Issue; Gamification
Interventions: Healing Town: Embedded Narrative in Interactive Game (Psycho-education Tool) Design
Locations: Shanghai Jiao Tong University
Countries: China
Enrollment: 20
Age range: 18 Years - 60 Years
Sex: ALL

Summary:
This study explores the potential of an interactive narrative game to enhance medication adherence among stable schizophrenia patients. Employing participatory design methods, game's development and testing process actively involved patients and individuals closely connected to their daily lives. Artificial Intelligence-Generated Content (AIGC) facilitated swift iterations in game's visual style and content. During game therapy, patients are tasked with making decisions based on presented situations, which influence the course of the narrative.

Eligibility criteria:
Inclusion Criteria:

* Stable Phase Schizophrenia
* on long-term medication
* medication adherence was problematic

Exclusion Criteria:

* Serious Schizophrenia
* Lack of basic cognitive abilities

Outcomes:
Medication Adherence; Medication Attitude
Feasibility Study of Deep Learning-based MDixon Quant for Quantitative Assessment of Chemotherapy-induced Fatty Liver
Trial ID: NCT06735118
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-12-25
Completion date: 2024-12-30
Sponsor: Yunnan Cancer Hospital
Conditions: Non-Alcoholic Fatty Liver Disease
Interventions: Neoadjuvant chemotherapy
Locations: Yunnan Cancer Hospital
Countries: China
Enrollment: 120
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
The purpose of this study is to quantitatively assess the changes in liver fat content in cancer patients before and after treatment.

The main questions it aims to answer are:How does the liver fat fraction change before and after chemotherapy? In this study, patients undergoing mDixon Quant scanning are subjected to fully automated segmentation and measurement of liver fat content using artificial intelligence.

Eligibility criteria:
Inclusion Criteria:

1. CT/B ultrasound showed no fatty liver
2. No MRI contraindications, including pacemaker, stent, metal implant, or claustrophobia
3. Received neoadjuvant/adjuvant chemotherapy

Exclusion Criteria:

1. Missing follow-up information
2. Liver lesions (metastases, hemangioma, etc.)
3. Poor image quality

Outcomes:
Extract the whole liver fat fraction
A Study on Artificial Intelligence-based Automated Biomedical Research
Trial ID: NCT06962423
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-05-15
Completion date: 2026-05-15
Sponsor: Tsinghua University
Conditions: Biomedical Research; Artificial Intelligence (AI)
Interventions: AI system
Locations: Tsinghua University
Countries: China
Enrollment: 40
Age range: 18 Years - None
Sex: ALL

Summary:
This study will evaluate the effects of the AI system developed by our team on biomedical research.

Eligibility criteria:
Inclusion Criteria:

* (1) Age ≥ 18 years old;
* (2) Agree to sign consent form and invest time in this study;
* (3) Able to understand and complete research tasks.

Exclusion Criteria:

* (1) Currently participating in other projects that may interfere with this research and unable to ensure participation time;
* (2) Have a history of major diseases or poor physical condition in the past three months that may affect research participation;
* (3) Unwilling or unable to understand and complete the research tasks.

Outcomes:
Research quality evaluation scale