moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Comparing the Number of False Activations Between Two Artificial Intelligence CADe Systems: the NOISE Study
Trial ID: NCT04399590
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-09-01
Completion date: 2021-03-31
Sponsor: Istituto Clinico Humanitas
Conditions: Artificial Intelligence
Interventions: Interficial Intelligence
Locations: Endoscopy Unit, Humanitas Research Hospital
Countries: Italy
Enrollment: 40
Age range: 18 Years - None
Sex: ALL

Summary:
One fourth of colorectal neoplasias are missed during screening colonoscopies-these can develop into colorectal cancer (CRC). In the last couple of years, Artificial Intelligence Deep learning systems were introduced in the endoscopic setting to allow for real-time computer-aided detection/characterization (CAD) of polyps with high- accuracy. Few CADe (detection) and CADx (diagnosis, characterization) have been therefore proposed with this purpose. Because CAD systems are based on deep learning where the computer directly learns polyp recognition from supervised data without any human-control on the final algorithm, their outcome incorporates some unpredictability in the clinical setting that must be cautiously interpreted after its application. This means that the endoscopist may be presented with FP images that he would have never been selected in the first place as suspicion areas. These FPs may hamper the efficiency of CADe-colonoscopy. Additional time may be required to discriminate between an actual FP and a possible false negative result. An excess of FPs may reduce the motivation of the endoscopist for CADe, leading to its underuse in clinical practice. Although the indications of a CADe must always be interpreted by physician, FP may result in unnecessary polypectomy with related adverse events when used without appropriate training. Yet, there is a lack of information among quantity and quality of False Positive signals provided by the systems. From a post-hoc analysis of a Randomized Clinical Trial, in which we extracted and analysed a video library of CADe-colonoscopy (GI Genius) performed in our institution Humanitas Clinical and Research Hospital IRCCS we aimed that False positives by CADe are primarily due to artefacts from the bowel wall. Despite a high frequency, FPs from this CADe system resulted in a negligible 1% increase of the total withdrawal time as most of them were immediately discarded by the endoscopists.

Eligibility criteria:
Inclusion Criteria:

1. Age over 18 years
2. Ability to provide and to give informed consent
3. Boston Bowel Preparation Score \> 6 (\>2 each segment)

Exclusion Criteria:

1. Boston Bowel Preparation Score \< 6 (\<2 each segment)
2. Patients who had chronic inflammatory bowel diseases (such as Chron or Ulcerative Colitis)
3. Inability to obtain written informed consent
4. Patient unwilling to participate to the study

Outcomes:
To evaluate the cause of False Positives (FPs) signals, their frequenTocy and time rate, on two different CAD systems: CADe (GI Genius, Medtronic) and CADe/CADx (CAD EYE, Fujifilm) and report a comparison among the two
A Rapid Diagnostic of Risk in Hospitalized Patients With COVID-19, Sepsis, and Other High-Risk Conditions to Improve Outcomes and Critical Resource Allocation Using Machine Learning
Trial ID: NCT05893420
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-12-31
Completion date: 2026-12-31
Sponsor: AgileMD, Inc.
Collaborators: Biomedical Advanced Research and Development Authority; University of Chicago; BayCare Health System; University of Wisconsin, Madison; Yale University
Conditions: Sepsis; Septicemia; Respiratory Failure; Hemodynamic Instability; COVID-19; Cardiac Arrest; Clinical Deterioration
Interventions: eCARTv5 clinical deterioration monitoring; Standard of care control
Locations: Yale New Haven Health System; BayCare Health System; University of Wisconsin Health
Countries: United States; United States; United States
Enrollment: 30000
Age range: 18 Years - None
Sex: ALL

Summary:
In this study, the investigators will deploy a software-based clinical decision support tool (eCARTv5) into the electronic health record (EHR) workflow of multiple hospital wards. eCART's algorithm is designed to analyze real-time EHR data, such as vitals and laboratory results, to identify which patients are at increased risk for clinical deterioration. The algorithm specifically predicts imminent death or the need for intensive care unit (ICU) transfer. Within the eCART interface, clinical teams are then directed toward standardized guidance to determine next steps in care for elevated-risk patients.

The investigators hypothesize that implementing such a tool will be associated with a decrease in ventilator utilization, length of stay, and mortality for high-risk hospitalized adults.

Eligibility criteria:
Inclusion Criteria:

* 18 years old
* Admitted to an eCART-monitored medical-surgical unit (scoring location)

Exclusion Criteria:

* Younger than 18 years old
* Not admitted to an eCART-monitored medical surgical unit (scoring location)

Outcomes:
Hospital mortality for elevated risk patients
POCUS Artificial Intelligence With Portable and Ultraportable Ultrasound Devices in Critically Ill Patients
Trial ID: NCT05336448
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-07-27
Completion date: 2022-06-11
Sponsor: Hospital Garcia de Orta
Conditions: Critical Illness
Locations: Hospital Garcia de Orta
Countries: Portugal
Enrollment: 99
Age range: 18 Years - None
Sex: ALL

Summary:
This is a prospective study that aims to assess the differences in point-of-care ultrasound assessment (POCUS) with portable and ultra-portable devices, using conventional vs artificial intelligence (AI) methodologies, performed by experienced vs inexperienced physicians, in critically ill patients.

Eligibility criteria:
Inclusion Criteria:

* Patients aged 18 years or over, admitted to the intensive care department which, according to the clinical assessment of the medical team, would benefit from an ultrasound assessment.

Exclusion Criteria:

* Patients with atrial fibrillation or other dysrhythmias;
* Inappropriate acoustic window for ultrasound assessment.

Outcomes:
Assess artificial intelligence ultrasound software to assess various echocardiographic variables useful for the management of critically ill patients
Enhancing Pediatric Nursing Students' Communication Skills and Grit Through an AI-Supported Empathy-Map Scaffolding Model: A Practice-Based Study
Trial ID: NCT07370285
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-04-20
Completion date: 2027-08-31
Sponsor: Mackay Memorial Hospital
Conditions: Artificial Intelligence (AI); Empathy; Scaffolding; Pediatric Nursing; Communication Skills; Grit
Interventions: AI-Supported Empathy-Map Scaffolding Model
Enrollment: 66
Age range: 18 Years - 25 Years
Sex: ALL

Summary:
The nurse-patient communication environment in pediatric care is characterized by high uncertainty and complexity. Due to children's limited language development and emotional regulation abilities, coupled with parents' high level of involvement, nursing students often experience anxiety, lack of confidence, and avoidance behaviors, which negatively affect their clinical learning outcomes and the establishment of therapeutic relationships. Therefore, providing effective communication support strategies is essential in pediatric nursing education. This study aims to implement an instructional scaffolding model using artificial intelligence (AI)-generated empathy maps to enhance the communication skills, empathy performance, and grit of nursing students during pediatric clinical practicums when encountering communication challenges.

A mixed-methods research design was adopted, and the participants were third-year nursing students enrolled in a pediatric nursing practicum course. The teaching intervention included AI-assisted generation of age-appropriate communication strategies, the construction of a grit-oriented empathy map, small group scenario-based exercises, and the application of learned strategies in clinical settings. Quantitative data were collected using pre- and post-intervention assessments, including an empathy scale, a communication skills scale, and a grit scale, to evaluate changes in learning outcomes. Qualitative data, including reflective journals, clinical observations, and focus group interviews, were analyzed to explore students' learning processes and strategy adaptations. Triangulation was applied to strengthen the validity of the findings.

It is anticipated that this teaching model will enhance students' understanding of pediatric patients' emotional needs, strengthen their communication strategy application and clinical interaction quality, and promote persistence and adaptability in challenging situations. Through evidence-based teaching practice, this study is expected to provide a feasible and scalable innovative instructional model that supports the effective integration of AI into clinical nursing education, thereby improving pediatric nursing competence and the quality of care for children.

Eligibility criteria:
Inclusion Criteria:

1. Undergraduate nursing students aged 18 years or older
2. Students enrolled in the Pediatric Nursing Practicum course

Exclusion Criteria:

1\. Students who have not passed the Pediatric Nursing course

Outcomes:
Communication Skills
Effect of a ChatGPT-Based Digital Counseling Intervention on Anxiety and Depression in Patients With Cancer: A Prospective, Randomized Trial
Trial ID: NCT06854315
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-07-02
Completion date: 2024-12-31
Sponsor: Gazi University
Conditions: Cancer; Anxiety; Depression; Artifical Intelligence
Interventions: ChatGPT-Based Digital Counseling; Standard Clinician-Led Education
Locations: Etlik City Hospital; Gazi University Faculty of Medicine, Department of Medical Oncology
Countries: Turkey (Türkiye); Turkey (Türkiye)
Enrollment: 150
Age range: 18 Years - 85 Years
Sex: ALL

Summary:
This study aims to evaluate the impact of a ChatGPT-based digital counseling intervention on anxiety and depression in newly diagnosed cancer patients undergoing chemotherapy. Patients are randomly assigned to one of two groups:

ChatGPT Group: Patients receive standard chemotherapy education plus access to ChatGPT, where they can ask up to 10 personalized questions about their treatment.

Control Group: Patients receive only standard chemotherapy education provided by oncologists and nurses.

The primary outcome is the change in anxiety and depression levels, measured using the Hospital Anxiety and Depression Scale (HADS), recorded at baseline and before the second chemotherapy cycle.

Secondary outcomes include patient engagement patterns, the types of questions asked, and an assessment of ChatGPT's responses by oncologists.

This study seeks to explore whether AI-based digital counseling can serve as a complementary support tool for newly diagnosed cancer patients.

Eligibility criteria:
Inclusion Criteria:

Age ≥18 years Newly diagnosed solid tumor (treatment-naïve) Scheduled to begin first-line chemotherapy ECOG performance status ≤2 Sufficient cognitive function to understand and complete the Hospital Anxiety and Depression Scale (HADS) questionnaire Ability to provide written informed consent

Exclusion Criteria:

Recurrent or metastatic cancer requiring urgent treatment Cognitive impairment that affects the ability to understand or complete the Hospital Anxiety and Depression Scale (HADS) questionnaire Severe psychiatric disorders (e.g., schizophrenia, bipolar disorder, severe major depressive disorder) that could interfere with study participation Concurrent participation in another interventional study related to psychological support or digital counseling

Outcomes:
Change in Anxiety and Depression Scores (HADS-A and HADS-D)
A Prospective Cohort Study for Machine Learning-Based Prediction of Anal Fistula Formation After Perianal Abscess Drainage Based on Drainage Setting, Provider Experience, and MRI Interpretation (PRISM)
Trial ID: NCT07019532
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-07-01
Completion date: 2026-06-01
Sponsor: Gumushane State Hospital
Conditions: Perianal Abscess; Anal Fistula
Enrollment: 450
Age range: 18 Years - None
Sex: ALL

Summary:
This prospective cohort study investigates the influence of provider experience and drainage location on fistula formation within 6 months following perianal abscess drainage. Additionally, the study explores the role of artificial intelligence (AI)-based interpretation of magnetic resonance (MR) images in early identification of fistula development.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18
* First-time perianal abscess
* Surgical drainage performed

Exclusion Criteria:

* Existing anal fistula history
* Crohn's disease
* Immunosuppressive treatment
* Incomplete 6-month follow-up

Outcomes:
Fistula formation within 6 months
Using Machine Learning to Detect and Predict Loosening NexGen Total Knee Replacement
Trial ID: NCT06724094
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-08
Completion date: 2025-12
Sponsor: University Hospital Southampton NHS Foundation Trust
Conditions: Aseptic Loosening of Prosthetic Joint
Enrollment: 2105
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this trial is to investigate whether Machine Learning (ML) can be used to detect small degrees of loosening, lucent zones, or any other changes on radiographs that might predict early failure following NexGen total knee replacement.

Researchers will identify plain AP and lateral plain film radiographs from two groups of patients. Those who has NexGen total knee replacements (TKRs) that went on to failure, and those who has well performing TKRs. Radiographs from these two groups will be labelled as 'failure' and 'well performing' and will be processed through a machine learning algorithm.

The algorithm will be successful if it is able to detect a NexGen TKR that went on to failure or went on to perform well. This will be determined by using a test set.

The population will be adults who had the recalled a NexGen Total Knee Replacement with a standard tibial tray. It will include adults only, who has the TKR at University Hospitals Southampton between 2003 and 2022.

Failure will be defined as revision of tibial or femoral components which is likely due to aspectic loosening. It will exclude washouts, exchange of poly, peri-prosthetic fractures, microbiologically confirmed infection.

Well performing TKRs will be defined as patients who have had their TKR in situ for 10 years and have reported no significant symptoms.

Eligibility criteria:
Inclusion Criteria:

* Had a NexGen TKR between 2003 and 2022.

Exclusion Criteria:

* Below 18 yrs old.
* Revision surgery for any reason other than aseptic loosening
* patients who have not had a revision but who do not have a well functioning TKR.

Outcomes:
Predictive accuracy of machine learning model
A Retrospective Analysis of Magnetic Resonance Imaging Data for Breast Cancer Screening in the Open Consortium for Decentralized Medical Artificial Intelligence
Trial ID: NCT05698056
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-01-01
Completion date: 2027-12-31
Sponsor: Technische Universität Dresden
Collaborators: European Institute for Biomedical Imaging Research (EIBIR), Austria; University Hospital, Aachen; Vall d'Hebron Institute of Oncology; Mitera Hospital; Radboud University Medical Center; UMC Utrecht; Ribera Salud Hospitals, Spain; Fraunhofer Institute for Digital Medicine (MEVIS), Germany; University Hospital, Zürich; Cambridge University Hospitals NHS Foundation Trust
Conditions: Breast Cancer
Interventions: No intervention.
Locations: Daniel Truhn; Jakob Nikolas Kather
Countries: Germany; Germany
Enrollment: 25000
Age range: 18 Years - 90 Years
Sex: FEMALE

Summary:
ODELIA is a project that aims to improve breast cancer detection in magnetic resonance imaging by utilizing artificial intelligence and swarm learning (MRI). The project will create an open-source swarm learning software framework that will be used to train AI models for breast cancer detection. These models' performance will be compared to that of conventional AI models, and the results will be used to assess the effectiveness of swarm learning in improving the accuracy and robustness of AI models. The project will use retrospective, anonymized breast MRI datasets with manual ground truth labels for cancer presence. The study is not associated with any patient treatment or intervention. The project's goal is to provide evidence of the clinical benefits of swarm learning in the context of breast cancer screening, such as accelerated development, improved performance, and robust generalizability.

Eligibility criteria:
Inclusion Criteria:

* Female
* age at the MRI examination from 18-90 years

Exclusion Criteria:

* insufficient image quality as judged by a blinded radiologist before start of the analysis
* non-identifiably ground truth (i.e., diagnosis has not yet been established)

Outcomes:
Diagnostic performance for breast cancer detection (Sensitivity and specificity)
Investigation of Respiratory System, Musculoskeletal System, Physical Activity Level and Quality of Life in Different Types of Office Workers.
Trial ID: NCT06216431
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-02-28
Completion date: 2026-12-15
Sponsor: Izmir Democracy University
Conditions: Work-related Illness
Interventions: Physical Evaluations of individuals
Locations: Izmir Democracy University
Countries: Turkey (Türkiye)
Enrollment: 52
Age range: 18 Years - None
Sex: ALL

Summary:
There are no studies in the literature reporting the posture, chest mobility, dyspnea, flexibility, quality of life and physical activity levels of emergency call center employees. The aims of this observational study are; 1) to evaluate posture, chest mobility, strength, dyspnea, flexibility, musculoskeletal disorders, physical activity and quality of life in emergency call center workers and office workers, 2) to reveal the relationships between these parameters of both research groups and 3) The aim is to compare these measurement parameters in emergency call center workers and office workers.

Eligibility criteria:
Inclusion Criteria for Emergency Call Center Workers

* Being an operator at Izmir 112 Emergency Call Center
* Being 18 years or older
* Volunteering to participate in the study
* Being able to understand and answer surveys

Inclusion Criteria for Office Workers

* Being actively working at a desk
* Being 18 years or older
* Volunteering to participate in the study
* Being able to understand and answer surveys

Exclusion Criteria:

* Have any history of acute/chronic infection, physical or mental disability/disease and/or cognitive impairment that would prevent measurement.
* Being pregnant
* Being able to exercise regularly

Outcomes:
Handgrip strength
Establishment of an Artificial Intelligence Recognition System for Wrist and Hand Joint Ultrasound Images/Videos and Its Assistance to Untrained Sonographers
Trial ID: NCT06883669
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2021-01-01
Completion date: 2025-06-01
Sponsor: West China Hospital
Conditions: Education; Joint Diseases
Locations: Xinyi Tang
Countries: China
Enrollment: 500
Age range: 18 Years - 100 Years
Sex: ALL

Summary:
1. To develop an AI system that can automatically identify standard sections and save images during wrist and hand joint ultrasound scans, while labeling key anatomical structures.
2. To recruit sonographers untrained in musculoskeletal ultrasound, train them in wrist and hand joint scans, and compare their scanning speed and image quality when using and not using the AI system.

Eligibility criteria:
(1) Research Subjects-for AI system establishment

Inclusion Criteria:

1. Healthy volunteers with good compliance
2. No history of disease on peripheral nerve, muscle, or tendons;
3. No early - stage RA or history of RA.

Exclusion Criteria:

1\. Amputees or those with limb disabilities.

Individuals with poor compliance.

(2) Research Subjects-for AI system validation

Inclusion Criteria:

1\. Sonographers with at least 2 years of ultrasound scanning experience

Exclusion Criteria:

1\. Those who have performed musculoskeletal ultrasound scanning or received related training

Outcomes:
Image acquisition is complete
Clinical Performance of the New Artificial-intelligence Powered 3D Total Body Photography System VECTRA® in Early Melanoma Detection and Its Impact on Patients' Burden of Disease: A Prospective Cohort Study in a Real-world Setting
Trial ID: NCT04605822
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-01-25
Completion date: 2024-01-31
Sponsor: University Hospital, Basel, Switzerland
Conditions: Melanoma (Skin)
Interventions: 3D imaging Total Body Photography Vectra® WB360; 2D imaging FotoFinder ATBM® Master imaging system; Smartphone application (SkinVision®); Standard-of-care clinical assessment of the skin
Locations: Department of Dermatology, University Hospital Basel
Countries: Switzerland
Enrollment: 455
Age range: 18 Years - None
Sex: ALL

Summary:
This study is to compare 2D- and 3D-imaging and routine clinical care in early melanoma detection in a prospective large-scale real-world data set.

Eligibility criteria:
Inclusion Criteria:

* Written informed consent of the patient
* Sufficient fluency in German language skills to complete all questionnaires of the study without external assistance
* High-risk criteria for melanoma. For "high risk" one of the following criteria needs to be fulfilled:

  * At least one previous melanoma (including melanoma in situ)
  * A diagnosis of ≥ 100 nevi
  * A diagnosis of ≥ 5 atypical nevi
  * A diagnosis of dysplastic nevus syndrome or known CDKN2A mutation
  * A strong family history (≥ 1 first- and/or second-degree relatives)

Exclusion Criteria:

* Lack of informed consent for study participation.
* Fitzpatrick skin type V-VI.
* Acute psychiatric illness or acute crisis

Outcomes:
Analyses of histopathology reports of all excised suspectable lesions; Analyses of dermatologists' assessment of each pigmented skin lesion as benign (melanocytic nevi / dysplastic nevi) or malignant (melanoma) before and after (without and with knowledge of) computer-guided risk assessment scores; Analyses of 2D FotoFinder® Mole Analyzer scoring of pigmented skin lesions (0.0 - 1.0); Analyses of 3D Vectra® WB360 imaging scoring of pigmented skin lesions (0- 10); Analyses of Smartphone app Skin Vision® scoring of pigmented skin lesions (low, medium or high risk)