moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Daily-Adaptive Stereotactic Body Radiation Therapy for Biochemically Recurrent, Radiologic Apparent Prostate Cancer After Radical Prostatectomy
Trial ID: NCT05946824
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: PHASE2
Start date: 2023-12-14
Completion date: 2028-11-24
Sponsor: University of Rochester
Collaborators: Varian Medical Systems
Conditions: Recurrent Prostate Cancer After Surgery
Interventions: Daily-adaptive Stereotactic Body Radiation Therapy; Daily-adaptive Stereotactic Body Radiation Therapy
Locations: Wilmot Cancer Institute - Dept of Radiation Oncology
Countries: United States
Enrollment: 80
Age range: 18 Years - None
Sex: MALE

Summary:
There is significant, proven use of radiation for recurrent prostate cancer after surgical resection. This treatment typically is delivered over seven and a half weeks of daily treatments, presenting a burden to patients and the health care system. Stereotactic body radiation (SBRT) is a radiation technique in which large doses are delivered over a short period of time. To date there is extremely limited evidence in SBRT for recurrent prostate cancer after surgery, with a significantly growing body of evidence for primary SBRT treatment of prostate cancer in men who opt for non-surgical upfront treatment. Additionally, advances in imaging have allowed better detection of the site of recurrence, and novel artificial intelligence aided daily-adaptive radiation therapy have allowed more precise delivery of radiation doses. This study seeks to evaluate the role of Daily-Adaptive with AI-assisted SBRT in the post operative setting utilizing Ethos Plan Adaptive technology in attempt to maintain control and minimize side effects.

Eligibility criteria:
Inclusion Criteria:

* Adenocarcinoma of the prostate with previous surgical resection
* Radiologically detected prostate bed OR regional nodal recurrence defined as iliac, obturator, perirectal or pre-sacral node generally encompassing below the aortic bifurcation
* Prostate bed recurrence as occurring within the region of the prostate or RTOG consensus definition of the surgical field
* At least two serum detectable PSA levels defined as \>0.02 ng/dl at least 30 days apart.

Exclusion Criteria:

* Metastatic disease
* Prior radiation therapy to the pelvis region
* Inflammatory bowel disease
* Hospitalization for a gastrointestinal diagnosis in the preceeding 3 months
* Hospitalization for a urinary tract issue / diagnosis in the preceeding 3 months
* PSA \>10 ng/dl at study entry,

Outcomes:
Incidence of Treatment-Emergent Adverse Events [Safety and Tolerability]); The primary objective of the full enrollment Phase II component is to report biochemical recurrence free survival (bPFS).
A Feasibility Study of a Digital Mental Health Intervention (Wysa in Hindi) in Improving Mental Health Distress in Adolescents and Young Adults with Type 1 Diabetes in Low Resource Settings
Trial ID: NCT06320756
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-02-23
Completion date: 2024-05-28
Sponsor: Wysa
Collaborators: Udaan
Conditions: Mental Health Issue; Diabetes Distress
Interventions: Wysa in Hindi; Individual and group sessions on topics like motivation, coping, wellbeing
Locations: Udaan
Countries: India
Enrollment: 79
Age range: 13 Years - 25 Years
Sex: ALL

Summary:
The principal objective of this study is to investigate the feasibility of including a digital mental health intervention (Wysa in Hindi) within pre-existing usual care to support adolescents and young adults with Type 1 Diabetes (T1D) who have mild and above mental health distress (operationalized as Patient Health Questionnaire-9: 5-14 scores; or Diabetes Distress Scale-17: \>2.0 mean score) with their mental health distress. This will be conducted through an exploratory randomized control study comparing Wysa in Hindi plus usual care with a control arm that just has usual care. The study further explores the effectiveness of the digital mental health intervention using the Patient Health Questionnaire (PHQ-9) and Diabetes Distress Scale (DDS-17).

Eligibility criteria:
Inclusion Criteria:

* Age between 13-25 years
* Diagnosis of Type1 diabetes
* Access to a smartphone with internet connection
* Can read and understand Hindi
* Mental health distress as demonstrated by elevated scores on any of the scales: Mild -moderate symptoms (between 5-14 score) on Patient Health Questionnaire (PHQ-9), moderate and above diabetes distress (mean score 2.0 and higher) on the Diabetes Distress Scale (DDS-17)

Exclusion Criteria:

* Do not own or have access to a mobile device
* Are identified to have risk of harm/ suicidality (as identified on PHQ-9)
* Are currently undergoing or have received psychiatric support
* Experience any mental or physical impairments that create accessibility barriers to the use of a smartphone or access to a digital mental health intervention

Outcomes:
Feasibility - Recruitment rate (Proportion of participants who completed consent procedures); Feasibility - Retention rate (Proportion of participants who remained enrolled within the study for the entire study duration); Feasibility - Wysa Engagement (Proportion of recruited participants who engaged in at least 1 interaction with the Wysa app); Feasibility - App therapist engagement rate (Proportion of recruited participants who completed at least one therapist session within the Wysa app); Satisfaction with the app which is collected through 3 quantitative questions and 1 free text qualitative question on the app; Therapist Satisfaction feedback collected through 3 quantitative questions and 1 free text qualitative question on the app
Implementation of an Integrated System of Artificial Intelligence and Referral Tracking for Real-time Diabetic Retinopathy Screening
Trial ID: NCT05166122
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-01-01
Completion date: 2022-09-30
Sponsor: Department of Medical Services Ministry of Public Health of Thailand
Collaborators: Health Systems Research Institute,Thailand; Google LLC.
Conditions: Diabetic Retinopathy; Artificial Intelligence; Screening
Interventions: Artificial Intelligence
Locations: Rajavithi hospital
Countries: Thailand
Enrollment: 1600
Age range: 18 Years - None
Sex: ALL

Summary:
This research study aims to bring an artificial intelligence system to screen for diabetic retinopathy (DR) along with referral tracking systems to the screening unit in Uthai Hospital in Phra Nakhon Sri Ayutthaya to assess the effectiveness of screening and follow-up of patients referred to Phra Nakhon Sri Ayutthaya Hospital. It will be compared with the existing screening system and follow up with regular referral by personnel

Eligibility criteria:
Inclusion Criteria:

* Patients aged 18 years and over.
* Patients who have been screened for diabetic retinopathy at Uthai Hospital Phra Nakhon Sri Ayutthaya Province that can refer patients to Phra Nakhon Sri Ayutthaya Hospital to see an ophthalmologist
* People with diabetes who are listed on the civil registry
* Able to take pictures of the retina at least 1 eye.

Exclusion Criteria:

* Being a patient in a community hospital with an in-house ophthalmologist
* Patients who were previously diagnosed for the following conditions / diseases: retinal edema, diabetic retinopathy (NPDR, PDR). The retina is affected by radiation (Radiation retinopathy) or retinal vein blockage (RVO).
* Past history of laser retinal treatment or retinal surgery
* Having other eye diseases (non-diabetic retinopathy) that requires referral to an ophthalmologist.
* Inability to take pictures of the retina (for any reason)

Outcomes:
Referral adherence
Analysis of Circulating Tumor Markers in Blood
Trial ID: NCT04025541
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2018-05-29
Completion date: 2029-05-29
Sponsor: Institut du Cancer de Montpellier - Val d'Aurelle
Conditions: Cancer; Breast Cancer; Sarcoma; Lung Cancers; Glioma; Colon Cancer
Interventions: Blood sampling; Blood sampling C3; Blood sampling C4/7/10/13; Blood sampling C5; Blood sampling C6; Blood sampling C8; Blood sampling C9; Blood sampling C11 + FFPE; Blood sampling C12
Locations: Centre Regional de Lutte Contre le Cancer - Centre Val d'Aurelle; ICM
Countries: France; France
Enrollment: 992
Age range: 18 Years - None
Sex: ALL

Summary:
The circulating tumoral biomarkers in the blood are the object of numerous researches for several decades. The potential clinical interests of these circulating biomarkers are diagnostic, prognostic, predictive of the efficiency of targeted therapies (according to the mutational profile of the cancer), and could allow the study of the mechanisms of resistance under process. In the multiplicity of these blood potential biomarkers joins a permanent evolution of the technological means used to detect them/to quantify, as well as to estimate their clinical utility.

Eligibility criteria:
Inclusion Criteria:

1. Patient presenting an invasive tumoral pathology (proved or suspected), whatever is the location or the stage,
2. Man or woman ≥ 18 years,
3. Obtaining of the informed consent signed before any procedure of specific preselection on approval.

Exclusion Criteria:

1. Private persons of freedom or under guardianship,
2. Patient whose regular follow-up is impossible for psychological, family, social or geographical reasons,
3. Pregnant woman and/or breast-feeding,
4. Unaffiliated patient to Social Protection System,

Outcomes:
Estimation of the feasibility of the various blood tumoral biomarkers analysis
Blinded Retrospective Study to Clinically Validate the Accuracy of the Lung Cancer Detection System (LCDS) AI-based Decision Support Tool for Lung Cancer Low-Dose CT
Trial ID: NCT07052773
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-10-26
Completion date: 2024-05-05
Sponsor: Genesis Medical AI
Conditions: Lung Cancer Screening
Interventions: Lung Cancer Detection System (LCDS)
Locations: Assuta Medical Center
Countries: Israel
Enrollment: 100
Age range: 50 Years - 79 Years
Sex: ALL

Summary:
The goal of this observational study is to clinically validate the accuracy of an AI-based decision support tool-the Lung Cancer Detection System (LCDS)-for detecting lung nodules in asymptomatic adults aged 50-79 with a history of heavy smoking who underwent low-dose chest CT (LDCT) scans.

The main questions it aims to answer are:

* Can the LCDS accurately detect the presence of solid pulmonary nodules on LDCT scans, as measured by sensitivity and specificity?
* How does the LCDS's performance compare to existing AI systems using the Area Under the Curve-Receiver Operating Characteristic (AUC/ROC) Curve?

Researchers will compare the AI-based interpretations to a ground truth established by consensus among radiologists' double-readings to see if the LCDS can accurately classify cases as 'lung nodule presence' or 'lung nodule absence'.

Participants will:

* Have their de-identified LDCT scans (collected between 2018 and 2023) reviewed retrospectively.
* Be evaluated through the LCDS tool, which will classify cases based on lung nodule presence.

Contribute to performance evaluation using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and ROC analysis.

Eligibility criteria:
Inclusion Criteria:

* Undergone an LDCT scan between 2018 and 2023, while a diagnosis record exists.
* Age is between 50-79 years old.
* History of smoking at least a 20 pack-year smoking history and currently smoke or have quit within the past 15 years.

Exclusion Criteria:

* History of lung cancer: Subjects with a previous diagnosis of lung cancer may be excluded to ensure that the study focuses on detecting new cases or evaluating the progression of the disease.
* Prior lung nodule detection: Individuals who have previously undergone LDCT scans with documented lung nodules that required medical intervention may be excluded to avoid potential confounding factors in the analysis.

Outcomes:
Sensitivity of LCDS for Detection of Solid Pulmonary Nodules; Specificity of LCDS for Detection of Solid Pulmonary Nodules
Nudging Flu Vaccination in Patients at Moderately High Risk for Flu and Flu-related Complications
Trial ID: NCT05509283
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-09-13
Completion date: 2022-10-26
Sponsor: Geisinger Clinic
Collaborators: National Bureau of Economic Research, Inc.; Massachusetts Institute of Technology
Conditions: Influenza; Vaccination; Health Promotion; Health Behavior; Risk Reduction
Interventions: Risk Reduction
Locations: Geisinger
Countries: United States
Enrollment: 40671
Age range: 18 Years - None
Sex: ALL

Summary:
This study will test the relative efficacy of high-risk messages in increasing flu shot rates in patients at moderately high risk for flu and complications (those in the top 11-20% of risk). It will also examine whether informing patients that their high-risk status was determined by analyzing their medical records or by an artificial intelligence (AI) / machine-learning (ML) algorithm analyzing their medical records will affect the likelihood of receiving a flu vaccine.

Eligibility criteria:
Inclusion Criteria:

* Included on a list of active Geisinger patients (all patients on this list attended at least one primary care appointment at Geisinger between 10/1/2008 and 4/13/2022, and either had a Geisinger primary care provider assigned as of April 2022, or were in the Electronic Health Record \[EHR\] since at least September 2021 and had at least one encounter in 2020-2022)
* Aged 18 or older
* In the top 11-20% of risk for flu and flu complications, according to Medial's flu complications machine learning algorithm (which operates on coded EHR data)
* Has a Geisinger PCP assigned as of August 2022
* Has had an encounter in the last 2 years as of August 2022

Exclusion criteria:

\- Cannot be contacted via any of the communication modalities (e.g., letter, patient portal, SMS) being used in the study, either due to insufficient/missing contact information in the EHR or because they opted out of all modalities

Outcomes:
Flu vaccination
Evaluation of the Concordance Between the Software Application and Standard Ultrasound Assessment in Classifying Hip Types in Infants Screened for Developmental Dysplasia of the Hip (DDH)
Trial ID: NCT07488260
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-07
Completion date: 2027-01
Sponsor: Pentacomp Systemy Informatyczne S.A
Conditions: Developmental Dysplasia of the Hip (DDH)
Interventions: AI-supported hip ultrasound analysis
Enrollment: 150
Age range: 4 Weeks - 6 Months
Sex: ALL

Summary:
The goal of this prospective, multicentre clinical investigation is to evaluate whether an artificial intelligence (AI)-supported software application can assist clinicians in the diagnostic assessment of developmental dysplasia of the hip (DDH) during routine ultrasound screening in infants.

The main questions it aims to answer are: Does the diagnostic classification generated by the application agree with the classification obtained by physicians using standard ultrasound examination according to the Graf method? Can the application reliably identify key anatomical landmarks and angles required for DDH assessment?

Researchers will compare the AI-generated diagnostic results with those obtained from standard ultrasound examinations performed by physicians to evaluate the level of agreement between the two approaches.

Participants will:

undergo a routine hip ultrasound examination performed by a physician according to the Graf method,

have ultrasound images analyzed by the application,

have the AI-generated results compared with the physician's assessment.

Eligibility criteria:
Inclusion Criteria:

1. Participants whose Parent(s) or Legally Authorized Representative(s) have provided written informed consent;
2. Participants who need to undergo routine DDH screening and receive an ultrasound as part of the clinical investigation;
3. Participants of either sex aged between 4 weeks and 6 months weeks.

Exclusion Criteria:

1. Participants with open wounds, ulcers, burns, viral or bacterial infections at the site intended for examination;
2. Participants with significant anatomical abnormalities (e.g. cerebral palsy, joint contracture, suppurative coxitis, other hip or limb deformities, or teratological hip dysplasia) that may affect the feasibility or accuracy of the examination;
3. Participants with history of trauma, surgical intervention, or post-traumatic/post-surgical condition in the area to be examined.

Outcomes:
Diagnostic agreement rate between the software application and the standard ultrasound assessment conducted by physicians
Impact Evaluation of Use of MATCH AI Predictive Modelling for Identification of Hotspots for TB Active Case Finding in Pakistan: a Pragmatic Stepped Wedge Cluster Randomized Trial
Trial ID: NCT06017843
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-09-01
Completion date: 2025-06-30
Sponsor: Centre for Global Public Health Pakistan
Collaborators: Mercy Corps Pakistan
Conditions: Tuberculosis
Interventions: Camps site selection for active case finding for TB using MATCH-AI
Locations: Mercy Corps Pakistan
Countries: Pakistan
Enrollment: 180000
Age range: 15 Years - None
Sex: ALL

Summary:
The aim of this pragmatic, stepped wedge cluster-randomized trial is to measure the comparative yield (number of incident TB cases diagnosed during active case-finding camps) using a site selection approach based on predictions generated via an artificial intelligence software called MATCH-AI (intervention group) versus the conventional approach of camp site selection using field-staff knowledge and experience (control group). The trial will help inform whether a targeted approach towards screening for TB using artificial-intelligence can improve yields of TB cases detected through community-based active case-finding.

Eligibility criteria:
Inclusion Criteria:

* All individuals \>15 years of age presenting to camp sites
* Individuals with previous history of TB disease

Exclusion Criteria:

* Children and adolescents \<15 years of age
* Pregnant women

Outcomes:
Camp positivity yield
Evaluation of a Mobile AI-powered Decision Support System for Insulin Dosing and Glucose Prediction in Type 1 Diabetes: The glUCModel Clinical Trial Protocol
Trial ID: NCT07304778
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-07-11
Completion date: 2026-06-01
Sponsor: Universidad Complutense de Madrid
Collaborators: Bioinspired Intelligence SL; Hospital de Toledo
Conditions: Diabetes (Insulin-requiring, Type 1 or Type 2)
Interventions: glUCModel app
Locations: Universidad Complutense de Madrid; Hospital Universitario de Toledo
Countries: Spain; Spain
Enrollment: 34
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
The goal of this clinical trial is to evaluate the safety and efficacy of integrating predictive models into insulin therapy management via the user-centered glUCModel mobile app in People with Type 1 Diabetes Mellitus following Multiple Insulin Dosing therapy. Participants will be aged 18 to 65 years. The main questions it aims to answer are:

Does using the app improve glycaemic control, as measured by time in range? Does using the app reduce the number of episodes of hyperglycaemia and hypoglycaemia? Are the app's design and functionality adequate?

The study will comprise four phases:ses}):

* Screening phase: Informed consent, collection of sociodemographic and clinical data, and baseline Pittsburg, IFIS, and DTSQ questionnaires.
* Run-in phase: 2 weeks of standard care with CGM. Data will be used to generate personalized predictive models in the intervention group.
* Active treatment phase: Participants continue MDI therapy. The intervention group will additionally use the glUCModel mobile app. CGM data from the final 2 weeks will be analyzed.
* Evaluation and analysis phase: Participants will complete the uMARS, Pittsburgh, and DTSQ questionnaires. Statistical analysis and correlations among outcomes will be processed.

Eligibility criteria:
Inclusion Criteria:

* HbA1c \< 9%
* Currently following an MDI Bolus-Basal therapy.

  * Wearing CGMs connected to a mobile phone.
  * Spanish language proficiency.
  * Willingness to participate in the trial.
  * At least one year since the time of diabetes diagnosis.
  * Ability to use a mobile application like glUCModel.
  * Own a mobile phone running Android or iOS operating system.
  * Ability to follow a Portion-controlled diet for diabetes.
  * Educated to do an active management of insulin dosing

Exclusion Criteria:

* HbA1c \< 9%.
* Not wearing CGMs.
* Non-Spanish language proficiency.
* Less than one year since the time of diabetes diagnosis
* Unable to use a mobile application like glUCModel
* Unable to follow a Portion-controlled diet for diabetes
* Unable to do an active management of insulin dosing.
* Diagnosed with a significant psychiatric disorder.
* Subjects in treatment with corticoids
* Patients who have required hospitalization or surgery in the last six months.
* Pregnancy or planning a pregnancy

Outcomes:
Time in Range (TIR); Usability and adherence
Technological Innovation in the Virtual Assistance of Patients With Uncontrolled Arterial Hypertension - Hyper 2
Trial ID: NCT05843682
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-10-07
Completion date: 2024-02-29
Sponsor: University of Sao Paulo General Hospital
Collaborators: InCor Heart Institute
Conditions: Essential Hypertension; Metabolic Cardiovascular Syndrome; Heart Disease Risk Factors
Interventions: Using the Avatr App
Locations: Heart Institute (InCor), Hospital das Clinicas do HCFMUSP
Countries: Brazil
Enrollment: 100
Age range: 20 Years - 65 Years
Sex: ALL

Summary:
The purpose of this study is to evaluate the effectiveness of the use of artificial intelligence in home monitoring in patients with uncontrolled arterial hypertension.

Eligibility criteria:
Inclusion criteria:

1. Primary arterial hypertension in drug treatment with at least 3 classes of antihypertensive drugs in optimized dose
2. Aged between 20 and 65 years
3. Blood pressure of the office ≥ 140 and/or 90 mmHg
4. Facilities to use mobile and apps

Exclusion criteria:

1. Stroke sequelae
2. Cognitive dysfunction

Outcomes:
Blood pressure control; Glycemic control; Change of out-of-hospital visits
Efficacy of a Phone Application on Treatment Adherence in Asthmatic Patients in Upper Egypt
Trial ID: NCT06233123
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-02-01
Completion date: 2025-03-01
Sponsor: Assiut University
Conditions: Bronchial Asthma; Adherence, Medication
Interventions: the Medisafe android application (https://www.medisafe.com).
Enrollment: 180
Age range: 18 Years - 40 Years
Sex: ALL

Summary:
asthma adherence is mandatory for disease control

Eligibility criteria:
Inclusion Criteria:

Doctor-diagnosed asthma. On at least Global Initiative for Asthma (GINA) step 3 which means they will be on regular inhaled steroids.

No change in regular asthma treatment in the last 1 month Sufficient English-language ability to understand the text in mobile phone-delivered interventions The education level required will be at least a high school diploma.

Exclusion Criteria:

GINA step 5 asthma (complex issues, too unstable). Recent changes to asthma treatment within the last month Another chronic health condition (eg diabetes, congenital heart disease, cystic fibrosis) Patients without mobile device skills

Outcomes:
Asthma control after 3 months