moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
REGISTRY on the Implementation of Artificial Intelligence in the Automatic Analysis of Vascular Network Segmentation
Trial ID: NCT06451315
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-01-30
Completion date: 2024-07-15
Sponsor: University Hospital, Bordeaux
Collaborators: Nurea
Conditions: Abdominal Aortic Aneurysm
Interventions: Analyze CTscans pre-operatively by Nurea System; Analyze CTscans pre-operatively by hospital practitioner
Locations: Service de Chirurgie Vasculaire, Hôpital Pellegrin-tripode, CHU de Bordeaux
Countries: France
Enrollment: 250
Age range: 18 Years - None
Sex: ALL

Summary:
The team hypothesizes that fully automatic analysis of AAA could provide increased performance (decreased duration of segmentation with increased reproducibility and decreased inter and intraobserver variability) to detect aortic aneurysmal sac enlargement (volumes and diameters) and predict the risk of complications during the procedure and during follow-up (MAE, MACE, MALE, Stroke) compared to standard methods of measurement relying on approximate maximum sac diameter.

Eligibility criteria:
Inclusion Criteria:

* Male or female ≥18 years of age
* AAA treated with EVAR between

  * ≥50mm
  * or ≥5mm increase in 6 months
* The subject is legally competent, has been informed of the nature, the scope, and the relevance of the study and has not manifested an opposition to participate.
* Available for set-up and proper implementation of follow-up visits (standard of care) throughout the duration of the registry

Exclusion Criteria:

* Advancing Alzheimer's disease or socially dependent patient
* Opposition to the use of their data for this research
* Life expectancy supposed to be inferior to 2 years
* Pregnant woman

Outcomes:
global volume measure of the aneurysm; global volume measure of the aneurysm; global volume measure of the aneurysm; global volume measure of the aneurysm; global volume measure of the aneurysm
Voice-Based AI to Scale Evaluation of Crisis Counseling in 988 Rollout
Trial ID: NCT06299384
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-04-02
Completion date: 2025-10-31
Sponsor: Lyssn.io, Inc.
Collaborators: ProtoCall Services, Inc.
Conditions: Suicide; Suicide and Self-harm
Interventions: LyssnCrisis
Locations: ProtoCall Services, Inc.
Countries: United States
Enrollment: 81
Age range: 18 Years - None
Sex: ALL

Summary:
Effective training requires repeated opportunities for skills practice with performance-based feedback, which is challenging to provide at scale. This research study focuses on developing an AI-based, coding and feedback tool ("LyssnCrisis") for implementation in a nationally utilized crisis call center, training counselors (call-takers) in suicide risk assessment skills, and evaluating LyssnCrisis to improve services and client outcomes. This research study's goal is to maximize the human capacity of call-takers to help assess their callers for risk of suicidality, and thus, a core aspect of the current research is developing a novel training process that supports human call-taker capacities.

Eligibility criteria:
Inclusion Criteria:

Must be employed at Protocall Services, Inc.

Exclusion Criteria:

Call-takers who participated in Stage 2 research will not be able to participate in Stage 3 research.

Outcomes:
Post-Call Survey; Call-taker crisis counseling fidelity; System Usability Scale (SUS); Acceptability of Intervention Measure; AIM; Intervention Appropriateness Measure; IAM; Feasibility of Intervention Measure; FIM
Clinical Trial Evaluating Outcomes of Self-Fit and Professionally Fit Hearing Aids
Trial ID: NCT05527730
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-07-07
Completion date: 2022-12-20
Sponsor: Whisper.ai
Collaborators: San Jose State University
Conditions: Hearing Loss
Interventions: Comparison of PRO-FIT or SELF-FIT fitting
Locations: Whisper.ai
Countries: United States
Enrollment: 80
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
Approximately 48 million people in the United States have hearing loss or hearing difficulties in noisy environments. Whisper.ai Inc has previously brought to market a commercial hearing aid system that reduces background noise and amplifies voices and sounds of interest using its proprietary platform based on machine learning and artificial intelligence technologies.

Whisper.ai Inc now seeks to bring to market a new hearing system that will incorporate a "self-fitting" capability. Self-fitting hearing aids have emerged in recent years as a potentially viable option to calibrate hearing aids to the needs of individual users without clinician intervention. The purpose of this project is to evaluate the efficiency and reliability of the fitting procedure and algorithms developed by Whisper.ai Inc. The study will be carried out using a randomized crossover design in two phases: in phase 1, subjects will be tested and a hearing aid will be fitted using conventional audiological standard procedures , and in phase 2, the subject will be tested and a hearing aid will be fitted using the results of the self-fitting algorithm.

Objective, subjective, and behavioral responses will be gathered from a variety of hearing-related surveys and tests, and will be analyzed quantitatively to evaluate the efficiency and reliability of the self-fitting algorithm. The investigators expect the fitting results of the Whisper.ai self-fitting algorithm to be similar to those of standardized fitting procedures conducted by hearing professionals.

Eligibility criteria:
Inclusion Criteria:

* Adults age 18-80 years old
* Mild-to-moderate hearing loss in both ears from 500 to 4000 Hz
* Reported one year of previous hearing aid experience in at least one ear
* Ability to use a laptop/desktop computer to browse the internet
* Ability to complete online surveys in a web browser
* Communicates verbally and in writing in English

Exclusion Criteria:

* History of neurological deficits (e.g. stroke, Alzheimer's disease, etc.)
* Inability to remain alert throughout initial testing appointment

Outcomes:
Hearing aid gain comparisons using Real Ear Measures Data Analysis Plan
Using CNN Image Recognition to Predict Rectal Cancer Outcomes
Trial ID: NCT05723965
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2010-10-01
Completion date: 2022-12-31
Sponsor: Taichung Veterans General Hospital
Conditions: Rectal Cancer Stage III
Interventions: As training material for deep learning model.; As materials for external validation for the buildup model.
Locations: Taichung Verterans General Hospital
Countries: Taiwan
Enrollment: 720
Age range: 20 Years - 100 Years
Sex: ALL

Summary:
Investigator retrospective collect cases during 2010-2021 diagnosed as rectal adenocarcinoma with high quality CT images. Local advanced rectal cancer cases were labeled as "disease". Nor were defined " normal".

Using artificial intelligence CNN on jupyter notebook with open phyton code to train and develop models capable to recognizing local advanced rectal cancer. Modify the phyton code for better predict rate and help physician to quickly evaluate disease severity for fresh rectal cancer cases.

Eligibility criteria:
Inclusion Criteria:

* clinical staging T3-4 with high quality CT images.

Exclusion Criteria:

* 1\. not primary malignancy lesion
* 2\. not localizing rectum
* 3\. T1-2 lesion
* 4\. non contrast or poor quality images

Outcomes:
accuracy of artificial intelligence with experienced physician
Peer Comparison-based Risk Communication (PRICOM): A Study Protocol to Evaluate the Feasibility of Delivering a Risk Communication Intervention in the Primary Care Setting for Adults With Type 2 Diabetes Mellitus
Trial ID: NCT06837428
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-08-21
Completion date: 2024-08-29
Sponsor: SingHealth Polyclinics
Conditions: Diabetes Type 2
Interventions: Peer comparison-based Risk Communication (PRICOM)
Locations: SingHealth Polyclinics - Bedok Polyclinic
Countries: Singapore
Enrollment: 40
Age range: 40 Years - 79 Years
Sex: ALL

Summary:
The study objective is to evaluate the feasibility of delivering a risk communication intervention, Peer comparison-based Risk Communication (PRICOM), in the primary care setting for adults with poor T2DM control. Findings from the study will inform further revisions to PRICOM and provide an estimated effect size to estimate the required sample size for a multi-site trial.

Eligibility criteria:
Eligibility criteria for patient participation:

* T2DM on follow-up at the respective study site for at least 12 months
* Age 40 to 79 years
* At least one HbA1c reading ≥ 8.0% within the last 6 months
* Able to read and speak English

Exclusion criteria for patient participation:

* Not a Singapore citizen or permanent resident
* Pregnant
* End-stage kidney disease or on renal replacement therapy
* Known terminal illness
* Visual and/or hearing impairment
* Cognitive impairment or mental illness
* Unable to provide informed consent

Outcomes:
Risk perception score change; Recruitment rate; Retention rate
Artificial Intelligence-Based Disease Management in the Vulnerable Period of Heart Failure, An Observational Study to Evaluate Patient Speech and Voice in the Vulnerable Phase of Heart Failure
Trial ID: NCT05544006
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-10-26
Completion date: 2023-04-02
Sponsor: Albert Saglik Hizmetleri ve Ticaret A.S.
Collaborators: Dokuz Eylul University
Conditions: Heart Failure
Locations: Dokuz Eylul University Research and Application Hospital
Countries: Turkey (Türkiye)
Enrollment: 76
Age range: 18 Years - 85 Years
Sex: ALL

Summary:
The study aims to evaluate the effect of AI-based discharge training after acute decompensation of heart failure on the patient's quality of life and to examine the relationship between changes in voice and speech characteristics of patients and changes in hospitalization, discharge, and early post-discharge clinical status.

Eligibility criteria:
Inclusion Criteria:

* Hospitalisation due to heart failure decompensation
* Having a smartphone with internet access
* Willingness to participate in the study
* NYHA 2-4 symptoms

Exclusion Criteria:

* Diagnosis of active malignancy
* Pregnancy
* Vision and hearing problems
* Moderate to severe cognitive impairment
* Dementia

Outcomes:
Heart failure-related hospitalizations; Change from baseline in health-related quality of life
Genetics, Imaging and Artificial Intelligence for Precision Care in Cardiomyopathy
Trial ID: NCT05750147
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-03-01
Completion date: 2027-08-01
Sponsor: Imperial College London
Conditions: Cardiomyopathies; Hypertrophic Cardiomyopathy; Dilated Cardiomyopathy
Interventions: Blood Sample Collection
Locations: Guys & St Thomas' NHS Foundation Trust; Kings College Hospital
Countries: United Kingdom; United Kingdom
Enrollment: 1000
Age range: None - None
Sex: ALL

Summary:
Cardiomyopathies are diseases of the heart muscle. Known genetic factors may account for some cardiomyopathy cases but there is still much to understand about the genetic and environmental causes and how the disease progresses.

Finding new ways to diagnose and treat cardiomyopathies could improve the health and well-being of patients with these conditions.

This study will collect data from individuals with cardiomyopathy or related heart muscle disease, or with a possible genetic predisposition to cardiomyopathy, and follow them over time to observe the progress of their heart and health. This study will collect DNA, blood samples, and detailed clinical \& lifestyle information at the start of the study, and data collected during routine healthcare visits over time.

* learn what causes cardiomyopathy, and therefore how to treat it
* understand why cardiomyopathy progresses differently in different people, to improve the ability to recognise who will benefit from different treatments at different times

The investigators will collaborate with other centres internationally to collect a large of group of participants with similar cardiomyopathies, providing power to identify new pathways that cause disease and ways of predicting which participants are at risk of having more severe disease.

Eligibility criteria:
Inclusion Criteria:

Adults with the capacity to consent Children with parental/guardian consent Male and Female

Meeting the following criteria:

1. Patients with a confirmed diagnosis of cardiomyopathy or related condition
2. Patients with a family member with cardiomyopathy, or a related condition
3. Patients with a genetic variant that may predispose to cardiomyopathy, or a related condition

Exclusion Criteria:

Patients without the capacity to provide informed consent

Outcomes:
Incidence of genetic variants; The incidence of major adverse cardiovascular events over 5 years
Effect of Automated AI Designed Presurgical Plate Therapy on Cleft Size Reduction in Newborns With Cleft Lip and Palate : A Comparative Study
Trial ID: NCT06970158
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-08-01
Completion date: 2025-12-31
Sponsor: University Hospital, Basel, Switzerland
Collaborators: University of Basel
Conditions: Congenital Disorder; Cleft Lip Palate
Interventions: AI-Designed Passive Plate Therapy
Locations: Saveetha Medical College; GSR Hospital Hyderabad
Countries: India; India
Enrollment: 70
Age range: 1 Day - 6 Months
Sex: ALL

Summary:
The goal of this observational study is to evaluate whether passive plate therapy using automated AI-designed devices can reduce cleft size in newborns with cleft lip and palate. The main questions it aims to answer are:

Does AI-designed passive plate therapy reduce the cleft size between birth and primary surgery? (Primary aim)

How does cleft size at the time of surgery compare between infants who received passive plate therapy and those who did not? (Secondary aim)

Researchers will compare infants who received AI-designed passive plate therapy with those who received no presurgical therapy to determine whether the intervention leads to a greater reduction in cleft width.

Participants will: Undergo intraoral scans at birth and again at the time of primary surgery, around 4 months of age.

Receive either no presurgical intervention or be treated with AI-designed passive plates, depending on site-specific clinical practices

Eligibility criteria:
Inclusion criteria:

Infants diagnosed with unilateral cleft lip and palate Age at enrollment: within the first 14 days of life (for treatment group) Age at cleft surgery: approximately 4 months Medically stable and fit to undergo intraoral scanning and cleft surgery Parent or legal guardian has provided written informed consent

Exclusion Criteria:

Syndromic cleft lip and palate or other craniofacial syndromes Bilateral cleft lip and palate Significant comorbidities affecting feeding, growth, or surgery (e.g., cardiac anomalies) Premature infants (\<37 weeks gestational age at birth) Infants who have already undergone any presurgical orthopedic intervention elsewhere Guardians/ Parents unwilling or unable to comply with follow-up or study procedures

Outcomes:
Percent Reduction in Cleft Width from Birth to Primary Surgery
Characterization of the Functional Connectome in Prader-Willi Syndrome: Integrating Neuroimaging and Artificial Intelligence to Assess the Impact of Physiological and Therapeutic Interventions
Trial ID: NCT06900335
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2025-03-01
Completion date: 2025-03-15
Sponsor: Corporacion Parc Tauli
Conditions: Prader Willi Syndrome; Hyperphagia; Behavior Disorders
Interventions: GH treatment (Somatropin); Eating
Locations: Consorci Corporació Sanitària Parc Taulí
Countries: Spain
Enrollment: 101
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this observational study is to explore brain network changes and identify patterns related to hyperphagia, hormonal treatment effects, and cognitive deficits in adults with Prader-Willi Syndrome (PWS). The main questions it aims to answer are:

* How are brain connectivity patterns altered in PWS patients compared to healthy and obese controls?
* How do brain network changes relate to hyperphagia and the response to growth hormone therapy?

Researchers will compare PWS patients to healthy and obese controls to see if there are significant differences in brain network connectivity before and after meals and growth hormone therapy. Ultimately, researchers will try to develop predictive models of treatment outcomes using AI and machine learning.

Eligibility criteria:
Inclusion Criteria:

* Genetically confirmed diagnosis of Prader-Willi Syndrome.
* Age older than 18 years old.

Exclusion Criteria:

* Age younger than 18 years old.
* Contraindications for fMRI.
* Visual defects.

Outcomes:
Functional connectivity patterns
Multi-center and Prospective Cohort Study of Artificial Intelligence Model for Gadolinium-based Contrast Agent Reduction in Brain MRI (MAGNET)
Trial ID: NCT05754476
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2019-03-29
Completion date: 2024-12-31
Sponsor: Beijing Tiantan Hospital
Conditions: Brain Diseases
Interventions: Low-dose GBCA levels
Locations: Beijing Tiantan Hospital
Countries: China
Enrollment: 3000
Age range: None - None
Sex: ALL

Summary:
MAGNET is a multi-center and prospective study to minimize Gadolinium-based Contrast Agent (GBCA) combining novel artificial intelligence (AI) methods with pre-contrast images and/or low-dose images to synthesize virtual contrast-enhanced T1 (vir-T1c) images, based on a large clinical and MRI database and subsequently validated for its clinical value. MRI examinations for patients included T1-weighted images (T1WI) before and after contrast agent administration and at two dose levels: low-dose (10% or 25%) and full-dose (100%), T2-weighted images (T2WI), fluid-attenuated inversion recovery (FLAIR), and diffusion-weighted imaging sequences (DWI) and the computed apparent diffusion coefficient (ADC), all either acquired three dimensional \[3D\] or two dimensional \[2D\]). The standard dose of intravenous gadolinium contrast agent was 0.1mmol/kg(body weight) by manual injection or automatic injection with a high-pressure syringe at a flow rate of 4mL/s.The sequence parameters used for the 3DT1WI scans must be consistent, and the standard for intravenous injection of gadolinium contrast agent is 0.1mmol/kg (body weight), administered either manually or automatically with a high-pressure syringe at a rate of 4mL/s.

Additionally, arterial spin labeling (ASL), amide-proton transfer chemical exchange saturation transfer (APT-CEST), susceptibility-weighted imaging (SWI), or quantitative susceptibility mapping (QSM) can be acquired at the same time if the conditions permit.

Eligibility criteria:
Inclusion Criteria:

1. Patients with suspected or known brain diseases including tumors, vascular disorders, inflammatory diseases, neurodegenerative diseases and trauma, follow-up, routine brain, and others requiring MRI exams with GBCAs.
2. Informed written consent obtained from the patient, and/or patient's parent(s), and/or legal representative.

Exclusion Criteria:

1. Patients with contraindications to MR examination.
2. Patients with incomplete MRI data and obvious image artifacts.

Outcomes:
quantitative metrics; qualitative assessments
Heart Failure in Norway: Combining Artificial Intelligence and a Database of 15000 Patients to Understand Clinical Characteristics, Mortality and Health Care Resource Use
Trial ID: NCT04398563
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-01-20
Completion date: 2023-12
Sponsor: University Hospital, Akershus
Collaborators: Novartis
Conditions: Heart Failure; Heart Failure With Preserved Ejection Fraction; Heart Failure With Reduced Ejection Fraction
Locations: Akershus University Hospital
Countries: Norway
Enrollment: 14998
Age range: 18 Years - None
Sex: ALL

Summary:
The prevalence of Heart failure above 70 years of age is 10% and 5 year mortality rate above 60%, higher than for cancer. The readmission rate first after hospitalisation is 44% despite the availability of life prolonging and life quality enhancing treatment. There is a lack of resources for adequate diagnostic workup necessary for implementing evidence-based treatment. This projects aims at assessing the impact of guidelines based diagnostic workup and guidelines based treatment of heart failure on mortality and readmission rates. As the symptoms defining the degree of heart failure and the discharge medication only is available in the electronic patient files, artificial intelligence is used to retrieve this information to assess if treatment is according to guidelines.

The project is using first a rule based text processing approach using IBM Watson, then advancing to a machine learning approach using readmission and mortality as endpoints.

The project has access to digitally stored echocardiographic measurements as well as digital ECG's and lab data on 15 000 patients admitted with a diagnosis of Heart failure. If the retrieval of symptoms and function by artificial intelligence is successful, the next step is to assess if those benefitting the most from echocardiography can be identified using information from the ECG's, lab data or symptoms and functional capacity as described in the Electronic Health Records.

Eligibility criteria:
Inclusion Criteria: Diagnosis with ICD10 codes as follows; I50.\*, I42.3, I11.\* and COPD J44.\* from 2007 through 2019.

In addition all subjects with NT-proBNP above age specific normal range in same period.

Exclusion Criteria:

none

Outcomes:
Mortality; Readmission
SAGE-NIV: Surveillance and Artificial Intelligence Guidance for Exacerbations in COPD Patients With Home Non-Invasive Ventilation
Trial ID: NCT07267104
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-03-25
Completion date: 2026-12
Sponsor: Corporacion Parc Tauli
Conditions: COPD (Chronic Obstructive Pulmonary Disease)
Interventions: The intervention involves download data of ventilator with clinical dates of the patient and model ventilator and parameters in acute exacebartion fo COPD
Locations: Corporation Parc Tauli de Sabadell
Countries: Spain
Enrollment: 75
Age range: 40 Years - 80 Years
Sex: ALL

Summary:
This study will look at people with COPD who use a home breathing machine called non-invasive ventilation (NIV). NIV machines collect information about your breathing, such as air flow, pressure, and mask leaks.

Researchers want to use a computer program, called artificial intelligence (AI), to study this information. The goal is to find early signs that your breathing may be getting worse.

People with COPD who already use NIV at home may join this study. The study does not change your treatment. It only uses the breathing data already recorded by your NIV machine.

The computer program will look for patterns in the data. These patterns may help doctors:

Notice early warning signs of a COPD flare-up Find problems with how you and the machine work together Improve the way NIV is monitored at home The main goal is to create a tool that helps patients and doctors manage home NIV more easily and more safely.

Eligibility criteria:
Inclusion Criteria:

* Age between 40 and 80 years.
* COPD diagnosed by pulmonary function tests.
* Home NIV therapy with good adherence (minimum daily compliance \> 5 hours) for at least 6 months.
* Users of the ResMed LUMIS 150 ventilator. This is due to the presence of the decoding tool and a larger storage capacity (more than 100 nights) in the removable device of the ventilator.
* Acute exacerbation requiring hospital admission or home care.

Exclusion Criteria:

* Lack of informed consent.
* Previous clinical instability defined by the need for antibiotics and/or systemic corticosteroids in the two months prior to the inclusion exacerbation, excluding the 48 hours prior to admission, as this was considered part of the inclusion clinical picture.

Outcomes:
Mean expiratory constant time (seconds)