moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Clinicians' Trust and Decision-Making Using AI-Based Fetal Growth Estimates With and Without Uncertainty: A Randomized Questionnaire Study
Trial ID: NCT07401368
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-06-01
Completion date: 2028-12-01
Sponsor: Rigshospitalet, Denmark
Collaborators: Slagelse Hospital; Copenhagen Academy for Medical Education and Simulation
Conditions: Fetal Growth; Obstetric Ultrasonography; Pregnancy; Clinical Decision-making
Interventions: Intervention - AI Performance Information
Locations: Department of Obstetrics and Gynecology, Slagelse Hospital
Countries: Denmark
Enrollment: 308
Age range: None - None
Sex: ALL

Summary:
This study examines how clinicians trust and use artificial intelligence (AI) when estimating fetal weight during pregnancy.

Accurate assessment of fetal growth is important for identifying growth problems that may affect pregnancy management. New AI-based tools can estimate fetal weight from ultrasound images, but little is known about how clinicians trust these estimates or how uncertainty information influences their decisions.

In this study, clinicians will review anonymized ultrasound cases and compare fetal weight estimates generated by an AI model with traditional estimates. Some clinicians will also be shown information about the AI model's performance and uncertainty, while others will not.

Participants will be asked to choose which estimate they find most reliable, indicate their level of confidence, and decide whether they would recommend follow-up scans. The study aims to better understand how AI and uncertainty information affect clinical decision-making and trust among clinicians with different levels of experience.

Eligibility criteria:
Inclusion Criteria:

* Clinicians working in obstetrics and gynecology departments.
* Regular use of obstetric ultrasound in clinical practice.
* Willingness to participate in a questionnaire-based study.

Exclusion Criteria:

* Clinicians who do not perform obstetric ultrasound examinations.
* Clinicians with a known conflict of interest related to the AI system being evaluated.

Outcomes:
Clinicians' choice of fetal weight estimation method
Effectiveness of an Image Analysing Algorithm (DERM) to Diagnose Non-melanoma Skin Cancer (NMSC) and Benign Skin Lesions Compared to Gold Standard Clinical and Histological Diagnosis
Trial ID: NCT04116983
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-06-26
Completion date: 2022-03-16
Sponsor: Skin Analytics Limited
Collaborators: Innovate UK
Conditions: Non-melanoma Skin Cancer
Interventions: Deep Ensemble for the Recognition of Malignancy (DERM)
Locations: Royal Free London NHS Foundation Trust; Royal Victoria Infirmary; Poole General Hospital
Countries: United Kingdom; United Kingdom; United Kingdom
Enrollment: 572
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to establish the effectiveness of an Artificial Intelligence (AI) algorithm (DERM) to determine the presence of Basal Cell Carcinoma (BCC) and Squamous Cell Carcinoma (SCC) and frequently observed benign conditions, when used to analyse images of skin lesions taken by commonly available smart phone cameras.

Eligibility criteria:
Inclusion Criteria:

* Participant is willing and able to give informed consent for participation in the study,
* Male or Female, aged 18 years or above,
* Have at least suspicious skin lesion which is suitable for photographing (\<15mm, not located on an anatomical site inappropriate to photograph (genitalia, hair-bearing areas, under nails), not previously biopsied, not located in an area of visible scarring or tattooing),
* In the Investigator's opinion, able and willing to comply with all study requirements.

Exclusion Criteria:

* Any other significant disease or disorder which, in the opinion of the Investigator, may either put the participant at risk because of participation in the study, or may influence the result of the study, or the participant's ability to participate in the study

Outcomes:
AUROC of DERM performance when analysing images of biopsied lesions
Use of Behavioral Artificial Intelligence to Predict and Increase Uptake of Peritoneal Dialysis [AI-Dialysis]
Trial ID: NCT06533254
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-09-01
Completion date: 2025-12-31
Sponsor: Scaled Insights
Collaborators: Dallas Renal Group; The Leeds Teaching Hospitals NHS Trust; Suffolk and North East Essex NHS Trust
Conditions: Chronic Kidney Disease
Interventions: Predicting choice of dialysis; Behavioral nudges
Locations: Dallas Renal Group
Countries: United States
Enrollment: 3000
Age range: 18 Years - None
Sex: ALL

Summary:
Scaled Insights Behavioral AI will be used to improve clinicians ability to detect and understand: The likelihood of patients with kidney failure opting for peritoneal dialysis and to select the right personalized nudge to get CKD patients to choose peritoneal dialysis

Scaled Insights' can utilize the data from this study to develop individualized nudges for healthcare professionals that can be broadly deployed within a digital platform so that clinics across the English speaking world can use it as a means of supporting patients with CKD in selecting peritoneal dialysis treatment.

Using Scaled Insights novel Behavioral Artificial Intelligence solution, the proposed project has two aims:

1. to identify personality attributes of patients with Chronic Kidney Disease and correlating their personality attributes with their subsequent choice of dialysis. Once correlated into personality based clusters, the AI Model will then be able will determine which personality cluster each net new patient will fall into and therefore predict their specific likelihood of opting for peritoneal dialysis or hemodialysis
2. once prediction is possible, customized nudges towards the selection of PD will be developed and tested to improve the likelihood that patients will opt for peritoneal dialysis

Hypotheses:

1. Scaled Insights Behavioral AI will identify the personality characteristics of patients with Chronic Kidney Disease who are likely to opt for both peritoneal dialysis and hemodialysis (Phase 1)
2. Scaled Insights Behavioral AI will identify the nudges that are most effective in encouraging or supporting patients with Chronic Kidney Disease in choosing peritoneal dialysis over hemodialysis (Phase 2)

Eligibility criteria:
Inclusion Criteria:

* Phase 1: US and UK adult patients aged 18+ years, with Chronic Kidney Disease stage 4-5.
* Phase 2: US and UK adult patients aged 18+ years, with Chronic Kidney Disease stage 4-5.

Exclusion Criteria:

* Phase 1: Young people aged \&lt;18 years. People who do not have Chronic Kidney Disease. People who reside outside of the USA and UK.
* Phases 2: Young people aged \&lt;18 years. People who do not have Chronic Kidney Disease stage 4-5. People who reside outside of the USA and UK.

Outcomes:
Choice of Dyalysis
Artificial Intelligence to Search for Abnormalities in Ambulatory Cancer Patients
Trial ID: NCT05412420
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-08-03
Completion date: 2023-10-13
Sponsor: Institut de Cancérologie de Lorraine
Conditions: Solid Tumor
Interventions: Patient Self-Reporting of Symptoms
Locations: Institut de Cancerologie de Lorraine
Countries: France
Enrollment: 500
Age range: 18 Years - None
Sex: ALL

Summary:
During treatment, cancer patients may experience side effects related to their disease but also to the different treatments they receive.

Currently, adverse effects and toxicities are well codified in the oncology community, notably via the NCI CTCAE criteria.

Unlike objective data such as a blood sample or a CTscan, a major bias in patient assessment is the subjective assessment of the physician or its team at a given time, which may not reflect the overall situation (for better or worse). Several studies had already highlighted the discrepancies between medical and patient data collection.

Self-assessment of symptoms is one way to overcome this bias. Moreover, there are now a large number of solutions that allow to perform these self-assessments at home.

Thanks to these tools, there are now two situations, the scheduled evaluation (before a chemotherapy treatment, or after a surgical procedure for instance) and the unscheduled situations, where it is the patient himself who can trigger an evaluation form.

These new evaluation methods also allow to take a quality of life approach. Patient-reported outcomes (PROs) is now a valid evidence-based assay to detect patient's symptoms and therefore provide helpful clinical information to healthcare providers.

The goal of this study is to go one step further than the previous PROs studies and evaluate the ability to train a machine learning algorithm to detect at-risk situations and lay the foundation for a viable solution for future prospective and randomized trials.

Eligibility criteria:
Inclusion Criteria:

* Follow-up for a solid tumor
* Chemotherapy treatment (oral and/or injectable) scheduled or in progress
* Life expectancy \> 3 months
* Performance Status (PS) \< 3
* Have an internet connection or assistance to answer questions throughout the study (nurse, family members, etc.)
* Patient having understood, signed and dated the consent form
* Patient affiliated to the social security system

Exclusion Criteria:

* Lack of means to answer the online questionnaires
* Patient in another therapeutic trial with an experimental molecule
* Patients and their families who cannot read or speak French
* Persons deprived of liberty or under guardianship (including curatorship)

Outcomes:
Number of unscheduled medical consultations or re-hospitalisations
The Development of Artificial Intelligence-assisted Glaucoma Screening Program
Trial ID: NCT03682783
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2018-10-01
Completion date: 2021-10-30
Sponsor: Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine
Conditions: Glaucoma Eye
Interventions: Fundus Image
Locations: Shanghai General Hospital
Countries: China
Enrollment: 8000
Age range: None - None
Sex: ALL

Summary:
This study will develop an artificial intelligence (AI) program for the screening of glaucoma.

Eligibility criteria:
Inclusion Criteria:

* able to complete fundus images

Exclusion Criteria:

* unable to complete fundus images

Outcomes:
The specificity of the algorithm for diagnosing glaucoma; The sensitivity of the algorithm for diagnosing glaucoma
AI-based Predictive and Interventional System for Early Detection of Non-compliance Risks With Oral Therapies in Lymphoma Patients, Integrating the Complete Care Pathway and an Interoperable Clinical Interface With Algorithms Paired With Explainability Tools.
Trial ID: NCT07546188
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-02-15
Completion date: 2029-02-15
Sponsor: Grand Hôpital de Charleroi
Conditions: Lymphoma, Non-Hodgkin; Care Coordination
Interventions: Retrospective Group; Prospective Group
Locations: Grand Hôpital de Charleroi
Countries: Belgium
Enrollment: 210
Age range: 18 Years - None
Sex: ALL

Summary:
This research forms part of a continuous quality improvement initiative. It aims to assess patient compliance of oral therapies by artificial intelligence. It could overcome the limitations of current practices and enhance the responsiveness and accuracy of clinical interventions.

Eligibility criteria:
Inclusion Criteria:

* All patients aged 18 and over who are treated in the Haematology Department at the Grand Hôpital de Charleroi from November 2025 onwards
* Treated for a lymphoma, Non Hodgkin
* Capable of giving informed consent

Exclusion Criteria:

* All other patients who did not meet the eligibility criteria

Outcomes:
ROC-AUC
Results Comparison of Cervical Cancer Early Detection Using Cerviray With VIA Test
Trial ID: NCT06518070
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-03-01
Completion date: 2023-10-31
Sponsor: Universitas Padjadjaran
Conditions: Cervical Cancer
Interventions: Cerviray AI
Locations: Padjajaran University
Countries: Indonesia
Enrollment: 44
Age range: None - None
Sex: FEMALE

Summary:
Cervical cancer, primarily caused by Human Papilloma Virus (HPV), is a significant global health concern, ranking as the fourth most common cancer in women worldwide. In Indonesia, it stands as the second most frequent cancer among women. This study investigates the performance of Cerviray® AI and Cerviray® AI Evaluation, an artificial intelligence (AI) technology for diagnosing cervical cancer, aiming to compare its sensitivity, specificity, positive predictive value (PPV), and area under the receiver operating characteristic curve (AUC ROC) with the Visual Inspection with Acetic Acid (VIA) test.

Eligibility criteria:
Inclusion Criteria:

* high-risk women of childbearing age who would be screened for cervical cancer (by Cerviray AI examination, Cerviray AI evaluation, and VIA test) at 1 clinic and other 4 health centers

Exclusion Criteria:

* incomplete data
* patient refusing to be examined
* patient refusing to participate in the study

Outcomes:
Cerviray AI
Advanced Machine Learning Analysis of Handwriting in Patients With Movement Disorders
Trial ID: NCT05355480
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-12-01
Completion date: 2023-12-31
Sponsor: Neuromed IRCCS
Conditions: Movement Disorders; Parkinson Disease; Writing Disorder
Locations: IRCCS Neuromed
Countries: Italy
Enrollment: 40
Age range: 18 Years - 90 Years
Sex: ALL

Summary:
Handwriting is a complex cognitive prowess that deteriorates in patients affected by neurodegenerative diseases, including movement disorders. More in detail, patients with Parkinson's disease (PD) may manifest prominent handwriting abnormalities which have been collectively identified as parkinsonian micrographia. MIcrographia may manifest at the onset of the disease and then worsens progressively with time. Previous techniques released to investigate micrographia in PD relied on perceptual analysis of simple tasks or were based on expensive technological tools, including tablets. However, handwriting can be promptly collected in an ecological scenario, through safe, cheap, and largely available tools. Also, the objective handwriting analysis through artificial intelligence would represent an innovative strategy even superior to previous techniques, since it allows for the analysis of large amounts of data. In this experimental project, the investigators apply a specific machine learning algorithm to analyze handwriting samples recorded in healthy controls and PD patients. The study aims to verify whether the technique proposed by the investigators would be able to detect parkinsonian micrographia objectively, monitor the evolution of handwriting abnormalities and assess the symptomatic improvement of handwriting following L-Dopa administration in PD patients.

Eligibility criteria:
Inclusion Criteria:

* Healthy conditions
* clinical diagnosis of Parkinson's disease

Exclusion Criteria:

* cognitive decline

Outcomes:
Stroke size of handwriting characters
Comparison of Aurora Fundus Camera With Traditional Fundus Camera in Diabetic Retinopathy With Phoebus Visual Artificial Intelligence
Trial ID: NCT03903042
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2018-11-01
Completion date: 2019-05
Sponsor: Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine
Conditions: Diabetic Retinopathy
Locations: Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine
Countries: China
Enrollment: 300
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to compare the effect of Aurora handheld fundus camera with traditional desktop fundus camera in the fundus photography screening of diabetic patients, and to evaluate the effect of artificial intelligence algorithm in the diagnosis of diabetic retinopathy.

Eligibility criteria:
Inclusion Criteria:

1. Participants are more than 18 years of age, male or female Chinese patients;
2. Diagnosed with diabetes;
3. Prior written informed consent should be obtained

Exclusion Criteria:

1. Patients with invisible fundus caused by any cause;
2. Patients or his/her licensor unwill to sign an informed consent or follow this protocol;
3. Pregnant women

Outcomes:
Image Quality of Aurora camera
Implantation of an Endovascular Prosthesis for the Treatment of Abdominal Aortic Aneurysms: Use of a Machine Learning Model From CT Images to Predict Complications
Trial ID: NCT06884397
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-04-14
Completion date: 2026-06-30
Sponsor: Groupe Hospitalier de la Rochelle Ré Aunis
Collaborators: La Rochelle University
Conditions: Abdominal Aortic Aneurysms (AAA)
Interventions: Abdominal aortic endoprosthesis
Locations: Groupe Hospitalier de la Rochelle Ré Aunis
Countries: France
Enrollment: 300
Age range: 18 Years - None
Sex: ALL

Summary:
Abdominal aortic aneurysm, a condition characterised by an increase in the diameter of the aorta, can be treated either surgically or endovascularly. In the latter, an endoprosthesis consisting of a metal spring covered with an impermeable fabric is inserted through an artery and deployed inside the aorta. This new method appears to be less invasive than surgery, but its long-term results are not yet fully understood. As a result, patients who have undergone this treatment are monitored by their surgeon to ensure that there is no endoleak. Several research teams have proposed analysing medical images to predict this risk of endoleak. Doctors are now trying to use artificial intelligence to automate the analysis of these images.

Eligibility criteria:
Inclusion Criteria:

* Treated for an abdominal aortic aneurysm through endovascular prosthesis implantation

Exclusion Criteria:

* Underwent intraoperative embolization of the inferior mesenteric artery or aneurysmal sac
* Refuse the use of their data

Outcomes:
Number of endoleaks following endograft implantation
Pragmatic Analysis of the Impact and Utilization of Workflow-Integrated Artificial Intelligence for RPM Enrollment
Trial ID: NCT05744180
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-06-19
Completion date: 2024-01-16
Sponsor: Mayo Clinic
Conditions: Nursing
Interventions: Interventional
Locations: Mayo Clinic Minnesota
Countries: United States
Enrollment: 10
Age range: 18 Years - None
Sex: ALL

Summary:
The objective of this study is to evaluate effectiveness, usability and clinical utility of the remote patient monitoring (RPM) "fit" score when choosing patients to enter the RPM Program.

Eligibility criteria:
Inclusion Criteria:

* The study participants will be nurses who are part of the RPM care team that cares for adult patients ≥18 years.
* A patient's data will be included in the analysis if the patient is ≥18 years old and receives care from a participating nurse.
* Patient data will only be collected if permitted (based on the use of the Minnesota Research Authorization Retrieval Tool).
* Patients who will be considered for this study will be assessed based on standard RPM program inclusion and exclusion criteria for the any of the chronic disease RPM programs (congestive heart failure, coronary artery disease, hypertension, type 2 diabetes, COPD, and general complex care).

Exclusion Criteria:

\- \< 18 years old.

Outcomes:
Evaluation of the effectiveness, usability, and clinical utility of the RPM "fit" score as displayed in the Acute Multipatient Viewer (AMP) and underlying AI models in the real-world setting
Diagnosis of Heart Failure in the Post-COVID-19 Clinic, Primary Care and Hospital Setting Using a Digital Stethoscope With Artificial Intelligence (AI) Electrocardiogram (ECG)
Trial ID: NCT04601415
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-02-06
Completion date: 2021-05-27
Sponsor: Imperial College London
Conditions: Heart Failure
Interventions: ECG from handheld device
Locations: Patrik Bachtiger
Countries: United Kingdom
Enrollment: 1050
Age range: 18 Years - None
Sex: ALL

Summary:
Abbreviations/acronyms:

DUO-EF = prediction of ejection fraction (EF) using the Eko-DUO digital stethoscope algorithm HF = heart failure HFrEF = heart failure with reduced ejection fraction COVID-19 = coronavirus disease 2019 Eko DUO = digital stethoscope device cMRI = cardiac magnetic resonance imaging ECG = electrocardiogram

Prospective observational study of left ventricular ejection fraction predicted by application of artificial intelligence to single-lead ECG acquired by a digital stethoscope; in the post-covid-19 follow up clinic, in patients presenting with heart failure symptoms in primary care, and in patients attending for echocardiography and cardiac MRI.

Eligibility criteria:
Inclusion Criteria:

* Attendance at GP with ?HF symptoms
* Referral from GP or elsewhere for echocardiogram in hospital
* Age \>18

Exclusion Criteria:

* Unable to give informed consent

Outcomes:
Area under receiver operating curve
eHealth for Screening Health Risks in Home-Based Older Adults: A Prospective Observational Study
Trial ID: NCT06019390
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-10-01
Completion date: 2024-07-01
Sponsor: Presage
Collaborators: Assistance Publique - Hôpitaux de Paris
Conditions: Frail Elderly Syndrome; Older
Interventions: Presage care
Enrollment: 200
Age range: 65 Years - None
Sex: ALL

Summary:
Introduction: Due to the worldwide ageing population and the increasing prevalence of chronic illnesses and cognitive deficits, appropriate instruments for frailty early screening and assessment of geriatric syndromes are required. Digital tools based on artificial intelligence can help improve the evaluation and screening of health risks in the older adults.

Objectives: This study aims to evaluate the effectiveness of the PRESAGE CARE questionnaire in screening geriatric syndromes among older adults and to assess its correlation with standardized test scores of the comprehensive geriatric assessment (CGA).

Methods: A monocentric prospective observational study will be conducted between October 2023 and July 2024, on seniors who are in the day hospitalization service of Charles-Foix hospital in France. During the consultation with older adults, the investigators will ask the patient's home caregiver to use the PRESAGE CARE application to fill in the PRESAGE CARE questionnaire. Once the questionnaire will be completed on the application, the PRESAGE platform will automatically recorde specific risks for each senior. Subsequently, the investigators will retrieve the data from standardized tests conduct on the same day and enter the results for each patient into the platform.

The primary outcome will be the correlation between standardized test scores and the risks computed by the Presage Care system for the risk of depression, the risk of undernutrition, the risk of cognitive impairment and the risk of loss of autonomy in activities of daily living.

Secondary outcome will be the comparison of the Area Under the Median Curve (AUC), and the sensitivity, specificity and positive predictive values of artificial intelligence (AI)-based models on the Présage Care questionnaire.

Eligibility criteria:
* Inclusion criteria :

  1. family members of individuals aged 65 and above,
  2. who are present within the day hospitalization service of Charles-Foix Hospital
  3. and able to provide informed consent.
* Non inclusion criteria

  1. refuse to give their consent to participate in the study,
  2. or have cognitive impairments
  3. or are occasional caregivers.

Outcomes:
Correlationbetween standardized test scores and the corresponding risks identified by PRESAGE questionnaire.