moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
The Use of Artificial Intelligence (AI) to Safely Reduce the Workload in Breast Cancer Screening With Mammography in Region Östergötland
Trial ID: NCT06187350
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-08-01
Completion date: 2025-03-20
Sponsor: Ostergotland County Council, Sweden
Conditions: Artificial Intelligence; Breast Diseases; Neoplasms; Breast Neoplasms
Interventions: AI cancer detection system
Locations: Region Östergötland
Countries: Sweden
Enrollment: 60000
Age range: 40 Years - 74 Years
Sex: FEMALE

Summary:
The overall aim of the project is to investigate how artificial intelligence (AI) can be used to streamline and at the same time increase diagnostic safety in breast cancer screening with mammography. AI has been shown in a number of studies to have great potential for both increasing diagnostic certainty (e.g. reduced occurrence of interval cancers) and at the same time reducing the workload for doctors. However, much research remains to clinically validate these new tools and to increase the understanding of how they affect the work of doctors. The specific goal of the project is to investigate whether the implementation of AI in breast cancer screening in Östergötland, Sweden, can increase the sensitivity (the mammography examination's ability to find breast cancer) and the specificity (that is, the right case is selected for further investigation: a minimum of healthy women are recalled but so many breast cancer cases that are possible are selected for further investigation) and at the same time make screening more efficient through reduced workload. AI will be implemented in the clinical routine and performance metrics such as cancer detection rate etc will be closely monitored. The study do not assign specific interventions to the study participants.

Eligibility criteria:
Inclusion Criteria:

Women participating in the regular Breast Cancer Screening Program in Region Östergötland

Exclusion Criteria:

Women with breast implants or other foreign implants in the mammogram Women with symptoms or signs of suspected breast cancer

Outcomes:
Cancer Detection rate; Positive predictive value of Transpara® scores
Nurses' Perspectives on the Implementation of a Nursing Workload Dashboard in the Intensive Care Unit
Trial ID: NCT07173218
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-10-01
Completion date: 2026-02-01
Sponsor: Willemijn Berkhout
Conditions: Nursing Workload
Enrollment: 60
Age range: None - None
Sex: ALL

Summary:
Increasing patient complexity, staffing shortages, and administrative burdens have intensified nurses' workloads, contributing to burnout and reduced job satisfaction. These challenges were particularly evident in the intensive care units (ICU) during the COVID-19 outbreak. With the growing healthcare demand in front of us, it is essential to understand and manage perceived workload effectively to maintain high-quality care and promote staff well-being. A real-time overview of nursing workload may facilitate the identification of patients requiring additional support and enable more effective distribution of workload among nurses during shifts. A dashboard has therefore been developed for use in the ICU to provide an overview of the patients at the unit and their corresponding nursing workload. To assess whether this dashboard is fit for purpose, this study aims to evaluate nurses' perspectives on its implementation, focusing on acceptance, adoptability, appropriateness, and fidelity. Additionally, the model for the calculation of the nursing workload will be assessed in terms of its alignment with ICU nurses' perceived workload and its potential for automation with artificial intelligence (AI).

Eligibility criteria:
Inclusion Criteria:

* An ICU nurse within the Erasmus Medical Center
* Informed-consent is signed by the ICU nurse before the interview and survey

Outcomes:
Implementation outcomes acceptance, adoptability, appropriateness and fidelity
A Pilot Randomized Controlled Trial of Behavioral Parent Training (BPT) With or Without AI Support in Children With Disruptive Behaviors
Trial ID: NCT07469215
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-04
Completion date: 2027-03
Sponsor: Fundació Sant Joan de Déu
Collaborators: Parente AI
Conditions: Behavior Problem of Childhood and Adolescence; Parent Management Training; Artificial Intelligence (AI); Disruptive Behaviours; Irritability; Attention Deficit Disorder With Hyperactivity (ADHD)
Interventions: Behavioral Parent Training (BPT); Behavioral Parent Training (BPT) + Support of AI app
Locations: Hospital Sant Joan de Déu
Countries: Spain
Enrollment: 40
Age range: 5 Years - 12 Years
Sex: ALL

Summary:
The goal of this clinical trial is to learn if adding an artificial intelligence (AI) application called to standard Behavioral Parent Training (BPT) helps families with children who have disruptive behavior problems. It will also help researchers understand if the app is easy to use and helpful for parents.

The main question it aims to answer is:

\- Is it feasible and acceptable for parents to use the AI app alongside their therapy sessions?

The secondary questions it aims to answer are:

* Does the app help reduce children's disruptive behaviors and irritability more than therapy alone?
* Does using the app help lower stress, anxiety, and depression levels for the parents?

Researchers will compare:

1. Standard BPT: Parents receive 8 weekly group training sessions (online).
2. BPT plus ParenteAI: Parents receive the same 8 weekly sessions plus 24/7 access to an AI virtual assistant for personalized support.

Participants will:

* Attend 8 weekly group training sessions.
* Complete surveys about their child's behavior and their own well-being at baseline, after group training sessions 4 and 8, and 3 and 6 months after finalizing the group training.
* If in the experimental group, use the ParenteAI app to get real-time coaching and support for managing their child's behavior at home.
* Provide feedback on their experience and satisfaction with the program.

Eligibility criteria:
Inclusion Criteria:

* Being a primary caregiver with a child aged 5 to 12 years exhibiting disruptive behaviors (e.g., symptoms of ODD, CD, IED, ADHD, and/or unspecified behavioral problems).
* Having access to an electronic device and regular internet access.
* Stable concomitant intervention including medications throughout the study.
* Speak Spanish or Catalan language.
* Signed informed consent by parents or legal guardians of the child.

Exclusion Criteria:

* Having ASD as primary diagnosis.
* Psychosis, self-harming behaviors, severe mood disorder.
* Known Intelligent quotient \< 70
* Caregivers and/or children receiving any concurrent psychological treatment.
* No signing the informant consent.

Outcomes:
Acceptability of AI support; Usefulness of BPT with or without AI support; Satisfaction with AI application support; Therapeutic alliance with AI-assistant; Treatment Attendance Rate; Study Dropout Rate
Cardiovascular Risk and Effects of Cardiovascular Drug Therapy During nCoV-19 Infection
Trial ID: NCT04371289
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-04-08
Completion date: 2021-09-10
Sponsor: Istituto Auxologico Italiano
Conditions: COVID-19; Cardiovascular Risk Factor
Locations: Istituto Auxologico Italiano
Countries: Italy
Enrollment: 4356
Age range: 18 Years - None
Sex: ALL

Summary:
Northern Italy, and particularly Lombardy, is one of the regions of the world mostly affected by COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. To investigate the still largely unknown pathophysiology of this disease, we have built a consortium of Italian Hospitals to include a large cohort of COVID-19 patients from mild out-patients managed by GPs to inpatients developing mild, moderate or severe disease assessed both in hospital and at a 3-6 month follow-up visit). Consortium partners have a wide expertise to allow for 1) comprehensive assessment of risk factors for severe COVID-19 syndrome; 2) study the pathophysiology of its cardio-respiratory manifestations; 3) estimate risk scores also with artificial intelligence and 4) assess its clinical immunoinflammatory and cardiorespiratory sequelae in discharged patients at short term follow-up. To this aim, we will

1. Enroll around 5500 COVID-19 patients (1000 outpatients and 4500 in-patients), which will allow to:

   1.1 Phenotype patients with COVID-19 of variable severity 1.2 Assess the prevalence of COVID-19 among GPs in relation with their use of PPE 1.3 Evaluate the impact of patients' demographic and clinical characteristics COVID-19 severity
2. Use an electronic CRF (on RedCap) to record clinical, biohumoral and imaging data of inpatients with COVID-19 of various severity to explore the prognostic and pathophysiological role of immunologic factors, activation of blood coagulation, endothelial dysfunction, inflammatory response, genetic (ni particular X-linked), hormonal and metabolic factors, comorbidities and acute cardiac damage. Blood samples will be collected. We will also use machine learning techniques to develop multivariable models for patients' risk stratification
3. A follow-up visit at 3-6 months after discharge will be performed to identify residual clinical consequences that might affect long-term prognosis.

Eligibility criteria:
Inclusion Criteria:

* Positivity to the test for COVID-19 and / or chest Xray or CT positive for interstitial pneumonia compatible with infection with this virus, regardless of the severity of the infection and the need or not for hospitalization
* Informed consent freely granted also verbally

Exclusion Criteria:

* Failure to satisfy the inclusion criteria

Outcomes:
Predictive modeling of in-hospital outcome; Clinical, pathophysiological and molecular mechanisms; Short -Term Sequelae
Development of an Algorithm to Differentiate Heart Murmurs by Timing, Disease Type, and Severity in Adult Patients Using Electronic Stethoscopes
Trial ID: NCT04400513
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-06-25
Completion date: 2021-12-31
Sponsor: Eko Devices, Inc.
Conditions: Murmur, Heart; Aortic Valve Stenosis; Tricuspid Regurgitation; Mitral Regurgitation; Innocent Murmurs; Heart Murmurs
Interventions: Use of Eko CORE and Eko DUO electronic stethoscopes
Locations: CoxHealth
Countries: United States
Enrollment: 1145
Age range: 18 Years - None
Sex: ALL

Summary:
The Eko CORE and DUO stethoscopes are FDA-approved electronic devices that have the capacity to record heart sounds. The study seeks to expand murmur detection to include VHD classification through the development of novel ML algorithms that are able to distinguish between systolic vs. diastolic vs. continuous murmurs, as well as classify VHD type and severity, using 4-point auscultation with Eko CORE and DUO electronic stethoscopes to record heart sounds.

Eligibility criteria:
Inclusion Criteria:

1. Adults aged 18 years and older
2. Patient or patient's legal healthcare proxy consents to participation
3. Have an audible heart murmur in at least one auscultation position, as heard by a clinician
4. Undergoing or have undergone a complete (i.e., not limited) echocardiogram
5. Willing to have heart recordings done with two different electronic stethoscopes

Exclusion Criteria:

1. Patient or proxy is unwilling or unable to give written informed consent
2. Unable to complete a full clinical echocardiogram
3. No audible heart murmur in at least one auscultation position, as heard by a clinician
4. Experiencing a known or suspected acute cardiac event
5. Mechanical ventricular support (such as ECMO, LVAD, RVAD, BiVAD, Impella, intra-aortic balloon pumps, TAH, VentrAssist, DuraHeart, HVAD, EVAHEART LVAS, HeartMate, Jarvik 2000)
6. Unwilling or unable to follow or complete study procedures

Outcomes:
Murmur Timing
Barcelona Esquerra Glaucoma Artificial Intelligence-based Screening Program (BEGAS): Artificial Intelligence Applied to Optic Nerve Retinographies for a Glaucoma Screening Program in a Primary-care Setting
Trial ID: NCT06353542
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-05-02
Completion date: 2026-09
Sponsor: Hospital Clinic of Barcelona
Conditions: Glaucoma
Interventions: Software analysis; Ophthalmologist examination
Locations: Hospital Clínic - ICOF
Countries: Spain
Enrollment: 500
Age range: 40 Years - 80 Years
Sex: ALL

Summary:
Two primary care-based screening systems will be tested to identify subjects with referrable glaucoma to hospital care.

Subjects between 45 to 64 years old living in the metropolitan area of Barcelona will be invited to participate in a one-time visit, with an optic disc examination and intraocular pressure (IOP).

The criteria for referring a patient will be the detection of glaucoma but with two different approaches depending on which Integrated Practice Unit (IPU) the patients will be allocated to: one arm using an Artificial Intelligence (AI) reading software of the optic disc picture; and the other one will base their referral after an ophthalmic examination performed by an ophthalmologist.

In both circuits, an optic nerve head photography will be obtained, and a masked reading center will be established to determine the ground truth for diagnosis.

This screening trial will explore the level of agreement between both systems and the cost-effectiveness of each of them.

Secondary analyses will include potential diagnostic composite scores (including other ancillary tests, such as optical coherence tomography images, that could maximize the screening process); the identification of population and disease characteristics (type of glaucoma, intraocular pressure) that could increase the effectivity and adherence to the screening process.

Eligibility criteria:
Inclusion Criteria:

* Patients aged 40 to 80 years old from our reference population
* Family history of glaucoma
* Willingness to participate
* Signed written informed consent

Exclusion Criteria:

* Not signing the informed consent
* Patients that had a previous diagnosis of glaucoma or any ophthalmic disease that required a regular ophthalmic examination and/or treatment
* Congenital or childhood glaucoma
* History of strabismus or amblyopia
* Known ophthalmic diseases which imply media opacity (cataract, cornea opacities) that might preclude from taking fundus retinographies

Outcomes:
Diagnostic agreement between the AI software and the ophthalmic examination; Health-Related Quality of Life (HRQoL); Demographics
PISTACHIO (Preemption Of Disruptive Behavior In Children) Real-Time Monitoring Of Sleep And Behavior Of Children 3-7-Year-Old Receiving Parent Child Interaction Therapy Augmented With Artificial Intelligence Randomized Controlled Trial
Trial ID: NCT05077722
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-03-24
Completion date: 2023-12-01
Sponsor: Mayo Clinic
Conditions: Disruptive Behavior; Attention-deficit Hyperactivity; Oppositional Defiant Disorder
Interventions: Garmin
Locations: Mayo Clinic in Rochester
Countries: United States
Enrollment: 100
Age range: 3 Years - 99 Years
Sex: ALL

Summary:
The purpose of this study is to develop an innovative wearable tracking protocol that will use Artificial Intelligence (AI) technology to monitor sleep and behavior of 3-7 year old children.

Eligibility criteria:
Inclusion Criteria - Children:

* Ages 3-7.
* Outpatients or Inpatients.
* Any gender, race or ethnicity.
* Able to provide developmentally appropriate informed assent, and legal guardians able to provide informed consent .
* EBP Severity rated above the clinically significant range (≥120; T-score ≥ 60) (Eyberg Child Behavior Inventory- ECBI; Eyberg \& Pincus, 1999).
* Need for more intensive behavioral treatments such as ER visit for behavioral dyscontrol or hospitalization will not be exclusionary or exit criteria.
* Families approached for participation will be asked to commit to complete the treatment.
* At least one primary caregiver and the identified child will have to be able to speak and understand English.

Exclusion Criteria - Children:

* Formal diagnosis of Severe Intellectual disability, Autistic Spectrum Disorder Level 3, or a psychotic disorder for the child.
* Parents not consenting to the study.
* Parents or child is not able to adhere to the study protocol.
* A Child who is reasonable expected to be unable to tolerate wearing the Garmin device for at least 70% of the time during the day and night 70% of the days during the treatment (12 weeks). This is based on the principal investigator's discretion.
* Unable to speak and understand English.
* Refusal or withdrawal of consent, inability, or unwillingness to adhere to study procedures.
* Children in foster care.

Inclusion Criteria - Adults:

* Agree to wear Garmin watch.
* Ages 18-99.
* Any gender, race, ethnicity.
* Able to provide informed consent.

Exclusion Criteria - Adults:

* Unable to speak and understand English.
* Refusal or withdrawal of consent, inability, or unwillingness to adhere to study procedures.

Outcomes:
Frequency of Garmin use
Dilated Cardiomyopathy Detection Using Artificial Intelligence and Screening With Mobile Technology
Trial ID: NCT06688396
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-12-04
Completion date: 2026-12
Sponsor: Lancaster General Hospital
Collaborators: Mayo Clinic
Conditions: Dilated Cardiomyopathy; Heart Failure; Heart Disease
Interventions: Mobile 6L AI-EKG Screening
Locations: Lancaster General Hospital
Countries: United States
Enrollment: 150
Age range: 18 Years - None
Sex: ALL

Summary:
This is a prospective, single-arm clinical trial in which participants with dilated cardiomyopathy will invite their first degree relatives to undergo mobile cardiac, electrocardiogram screening.

Eligibility criteria:
Inclusion Criteria (Proband)

* Male or female age ≥ 18 years
* Confirmed diagnosis of non-ischemic DCM determined at the discretion of the treating physician and defined as LVEF ≤ 45% at any time during the clinical course of the patient
* Must have at least one living adult FDR
* Able to provide informed consent

Inclusion Criteria (FDR)

* Male or female age ≥ 18 years who are first-degree relatives of patients with DCM
* Proband has provided informed consent
* FDR able to provide informed verbal consent
* Access to a smartphone or digital tablet with cellular data or Wi-Fi access

Exclusion Criteria (Proband)

* DCM attributed to any other secondary form of cardiomyopathy per the investigator's determination
* Proband has previously informed FDR to undergo cardiac screening
* Ischemic cause of reduced LVEF

  * evidence of myocardial infarction, inducible ischemia or chest pain on stress testing in absence of coronary angiogram to rule out ischemic disease
  * history of acute coronary syndromes (STEMI (ST-Elevation Myocardial Infarction), NSTEMI (Non-ST-Elevation Myocardial Infarction) or unstable angina) revascularization or ≥75% stenosis of either left main or Left Anterior Descending (LAD) or ≥75% stenosis of 2 major epicardial vessels on angiogram
* Moderate or greater primary valvular abnormality not due to underlying cardiomyopathy
* Congenital structural heart disease
* Severe and untreated or untreatable hypertension
* Cardiomyopathy due to acute or reversible conditions; examples include tachyarrhythmias, thyroid disorders, iron overload
* Any secondary cause of reduced LVEF such as pregnancy, myocarditis, amyloidosis, sarcoidosis, exposure to toxins including alcohol, substance abuse or chemotherapeutic drug
* (CPC Participants only) Home address outside of traveling range

Exclusion Criteria (FDR)

* Previously informed about cardiac screening or has completed cardiac screening by transthoracic echocardiogram (TTE)
* Previously diagnosed with reduced LVEF
* (CPC Participants only) Home address outside of traveling range

Outcomes:
This study's primary endpoint is to assess the feasibility of performing AI-EKG test and the subsequent uptake of cardiac screening in FDR of DCM patients.
RIDERS: Artificial Intelligence 3D Augmented Reality Robot-Assisted-Radical- Prostatectomy v.s. no3D Intervention: a Prospective Multicenter Randomized Controlled Trial
Trial ID: NCT06318559
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-06-29
Completion date: 2027-12-31
Sponsor: Fondazione del Piemonte per l'Oncologia
Collaborators: San Luigi Gonzaga Hospital
Conditions: Cancer of Prostate
Interventions: 3D Robot-assisted laparoscopic radical prostatectomy and pelvic lymphadenectomy
Locations: Fondazione del Piemonte per l'Oncologia-IRCCS Candiolo; AOU san Luigi Gonzaga
Countries: Italy; Italy
Enrollment: 120
Age range: 40 Years - 99 Years
Sex: MALE

Summary:
This is a prospective randomized multicenter study aimed at comparing 3D AI-AR-RARP vs. no 3D, verifying the impact of this new technology on oncological and functional outcomes after the procedure.

Eligibility criteria:
Inclusion Criteria:

1. Signature of the written informed consent and consent to the use of personal data
2. Age \> 40 years and male sex
3. Pre-operative MRI performed according to ESUR recommendations and reporting in accordance with PiRads V.2
4. Disease with evidence of bulging or radiological T3 on pre-operative MRI
5. Histological diagnosis of acinar type prostate cancer in the area highlighted on MRI
6. Absence of bulky (\>3 cm), bony or visceral retroperitoneal or pelvic lymph node metastatic lesions
7. Patients eligible for radical prostatectomy + pelvic lymphadenectomy
8. ECOG PS 0-1
9. Life expectancy ≥ 5 years
10. Patients motivated to preserve erection and with pre-operative sexual activity with IIEF \>17
11. Availability of the patient's pre-operative clinical data
12. Patients must be available to carry out the visits foreseen in the follow-up of the protocol and consent to data collection

Exclusion Criteria:

1. Special histotypes of prostate cancer
2. Patients with PSA \> 100 ng/ml at diagnosis
3. Inability to perform MRI (pacemaker wearers, claustrophobia...) or MRI of inadequate quality to obtain the HA3DTM 3D reconstruction
4. Concomitant treatment with other antineoplastic drugs including investigational endocrine therapies
5. Serious uncontrolled concomitant medical condition or disease including active, uncontrolled infections
6. Patients with dementia or psychiatric illness that limit compliance with study requirements or that may prevent understanding and/or signing informed consent.

Outcomes:
Perioperative surgical oncological outcomes