moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Validation Study of an Artificial Algorithm for Glaucoma Detection in an African Population
Trial ID: NCT06552247
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-08-14
Completion date: 2024-08-31
Sponsor: Centro Hospitalar Universitário Lisboa Norte
Collaborators: Dr. Agarwal's Eye Hospital
Conditions: Glaucoma; Diabetic Retinopathy
Interventions: Fundus Picture AI testing
Enrollment: 100
Age range: 18 Years - None
Sex: ALL

Summary:
Artificial Intelligence (AI) algorithms require validation in a variety of populations to ensure widespread clinical applicability. In Ophthalmology, AI algorithms are reaching maturity in diagnosis such as diabetic retinopathy and glaucoma. Higher-at-risk subjects of African descent are nevertheless usually under-represented in training datasets and therefore unclear about representativity.

A small scale validation study in consecutive patients in a large Eyesore unit in Mozambique will be performed to determine the diagnostic ability of these AI softwares in this population

Eligibility criteria:
Inclusion Criteria:

* subjects age above 18 years old presenting at the Eye Unit
* willingness to sign an informed consent for the screening process

Exclusion Criteria:

* none
* Poor quality in screening image will be included in the intention to treat analysis, but excluded from the diagnostic comparator outcome.
* Patients with a known glaucoma diagnosis will not be excluded from the screening

Outcomes:
Diagnostic agreement between referring decision and reading center decision
Pain Relief Through Artificial Intelligence (AI) -Guided Dynamic Neuromodulation
Trial ID: NCT06276881
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-06-03
Completion date: 2023-05-01
Sponsor: Dandelion Science
Collaborators: National Institutes of Health (NIH); National Institute on Drug Abuse (NIDA)
Conditions: Pain
Interventions: Visual Stimulation
Locations: Dandelion Science
Countries: United States
Enrollment: 75
Age range: 18 Years - 50 Years
Sex: ALL

Summary:
The goal of this study is to investigate a novel neuromodulatory approach utilizing visual stimulation to impact pain perception in healthy participants.

Eligibility criteria:
Inclusion Criteria:

* Participants of both sexes
* Participants of diverse ethnic and racial backgrounds
* Participants who speak, read, and understand English and are willing and able to provide written informed
* 18 to 50 years old

Exclusion Criteria:

* Participants with neurologic, endocrine, renal, gastrointestinal, and chronic pain disease
* Participants who use unprescribed drugs or medications
* Chronically medicated participants
* Participants with photosensitivity and epileptic seizures
* Participants who have undergone major surgery within two months of the experiment date
* Participants with cardiac disorders
* Participants who are pregnant
* Prisoners
* Participants who are unable to provide consent
* Participants diagnosed with a psychiatric disorder that is likely to interfere with the conduct of the study
* Participants with implanted electronic devices of any kind, including pace-makers, electronic infusion pumps, stimulators, defibrillators

Outcomes:
Visual Analog Scale (VAS) Score; Event-related Potential (ERP)
Digital Art and Artificial Intelligence : Encouraging the Discovery of the Senses Via the Impressionist Painters for the Well-being of Adults - Qualitative Study
Trial ID: NCT06609642
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-01-22
Completion date: 2026-02
Sponsor: Olivier Beauchet
Collaborators: Centre integre universitaire de sante et de services sociaux du Centre-Sud-de-l'Île-de-Montréal
Conditions: Adults
Interventions: Art application
Locations: CRIUGM
Countries: Canada
Enrollment: 30
Age range: 18 Years - None
Sex: ALL

Summary:
The study aims at examining the impact of an immersive and interactive digital application (App) that uses impressionist artworks to promote the mental well-being of both young and older adults. The goal is to study the usability and accessibility of this App, particularly for older adults, who may have sensory impairments and lower digital literacy compared to younger adults.

The study is based on the hypothesis that the App, which allows interaction with paintings by Claude Monet and Helen McNicoll, can trigger positive emotions, thereby improving users' well-being. The App uses artificial intelligence to create extensions of the paintings and music based on the sonification of electrophysiological signals captured from plants.

The study seeks to answer four research questions:

1. The usage behaviors of young and older adults with the App.
2. The impressions and feedback from users after testing the App.
3. The obstacles and enablers to the App's use.
4. The expectations and needs of users for better use of the App.

The study's objectives include analyzing the usage behaviors, impressions, barriers, and needs of both young and older adults. It is a qualitative study based on non-participatory observations and semi-structured interviews with young and older participants who have used the App.

The App is based on impressionist artworks, a style known for evoking positive emotions, and aims to promote mental health by harnessing the emotional power of digital art and artificial intelligence.

Eligibility criteria:
1. For adults of any age:

   1. Inclusion criteria:

      * Concordance of sex and gender.
      * No psychiatric or cognitive disorders.
      * Ability to understand spoken and written French.
      * Provide written consent to participate in the study.
   2. Exclusion criteria:

      * Non-binary.
      * Inability to provide informed consent to participate in the study.
      * Participation in a concurrent experimental clinical study to avoid interference with our study.
      * Inability to understand spoken and written French.
      * Having moderate to severe visual or auditory impairment.
2. Young adults:

   The inclusion criterion is being aged between 18 and 35 years.
3. Older adults:

The inclusion criterion is being aged 60 years or older.

Outcomes:
Observation grid for behaviors and difficulties encountered; Impressions and feedback; Enablers and barriers; Expectations and needs
Enhancing Interdisciplinary Understanding of Ophthalmology Notes Through Local Large Language Model
Trial ID: NCT06624605
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-02-01
Completion date: 2024-05-31
Sponsor: John J Chen
Conditions: Communication; Artificial Intelligence (AI); Artificial Intelligence Technology; Interdisciplinary Communication
Interventions: Large Language Model-generated Plain Language Summary of Ophthalmology notes
Locations: Mayo Clinic
Countries: United States
Enrollment: 851
Age range: 18 Years - None
Sex: ALL

Summary:
This prospective, randomized controlled trial evaluated the efficacy of adding Large Language model (LLM)-generated Plain Language Summaries (PLSs) to Standard Ophthalmology Notes (SONs) in enhancing comprehension among non-ophthalmology providers. The study utilized surveys to assess non-ophthalmology providers\&#39; comprehension and satisfaction with the notes and ophthalmologists\&#39; evaluation of PLS accuracy, safety, and time burden. An objective semantic and linguistic analysis of the PLSs was also conducted.

Eligibility criteria:
Inclusion Criteria:

* Evaluation by an Ophthalmologist in the Mayo Clinic Department of Ophthalmology in Rochester, MN in either the inpatient or outpatient setting between Feb 1, 2024 - May 31, 2024.

Exclusion Criteria:

* No in-network non-Ophthalmologist care providers in the medical record
* Procedure-only visits containing only a procedure note

Outcomes:
Non-Ophthalmologist Note Comprehension and Satisfaction
Prediction Model of Hip Fragility Fracture Using Explainable Artificial Intelligence and Realistic Data From a Traumatology Department and Orthogeriatric Ward
Trial ID: NCT06545487
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-03-05
Completion date: 2024-04-09
Sponsor: Universidad de Valparaiso
Collaborators: HOSPITAL DOCTOR GUSTAVO FRICKE; Centro Interdisciplinario para el Desarrollo del Adulto Mayor Gerópolis
Conditions: Osteoporosis Risk; Fracture of Femur; Fragility
Interventions: BLOOD SAMPLE
Locations: Universidad de Valparaíso
Countries: Chile
Enrollment: 50
Age range: 60 Years - None
Sex: ALL

Summary:
The aim of the project is to build a prediction model of hip fragility fracture using hospital data routinely collected in the traumatology department from the last 12 years and up to date Explainable Artificial Intelligence (XAI) tools. This model should be adapted to the "real world" conditions of the region and predict clinical data such as risk of fracture and refracture, mortality risk, fracture type classification and the generation of a specific comorbidity index.

Eligibility criteria:
Inclusion Criteria:

* minimum 60 years

Exclusion Criteria:

* Hip fractures
* unable to walk from point of examination

Outcomes:
Comparison between parameters intra-groups; Comparison between parameters inter-groups
Use of Artificial Intelligence-Guided Echocardiography to assIst cardiovascuLar Patient managEment
Trial ID: NCT05558605
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-02-24
Completion date: 2026-06-30
Sponsor: Baker Heart and Diabetes Institute
Collaborators: Alice Springs Hospital; Princess Alexandra Hospital, Brisbane, Australia; Royal Perth Hospital; Ochre Health; Walgett Aboriginal Medical Services; Merriden Health Service; Royal Hobart Hospital; Quairading Medical Practice
Conditions: Heart Failure; Valve Heart Disease
Interventions: AI-guided echo; Standard echo
Locations: Alice Springs Hospital
Countries: Australia
Enrollment: 612
Age range: 45 Years - None
Sex: ALL

Summary:
Heart Failure and valvular heart disease are disproportionate problems in rural and remote Australia (RRA). Echocardiography is the best imaging investigation, and essential for management, but access to this essential test shows huge geographic variations, primarily because of dependence on expert acquisition. This trial seeks to demonstrate the effectiveness of artificial intelligence-based echocardiography for triage and management of patients with known or suspected heart disease in RRA.

Eligibility criteria:
Inclusion Criteria:

* Age \>45 years
* eligible for Medicare
* exercise intolerance or cardiovascular (CV) risk factors

Exclusion Criteria:

* Known HF or HVD
* situations where cardio-protection is already indicated (eg. known CAD)
* comorbid conditions with life expectancy \<2 years
* inability to provide written informed consent

Outcomes:
Diagnosis of cardiac dysfunction or heart valve disease
Use of Artificial Intelligence and Machine Learning in Cardiovascular Risk Prediction in Urban Asian Indians
Trial ID: NCT05939869
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-07-01
Completion date: 2024-08-30
Sponsor: Diabetes Foundation, India
Collaborators: National Diabetes Obesity and Cholesterol Foundation; Johns Hopkins University
Conditions: Type2diabetes
Interventions: Medical and Clinical History
Locations: Fortis Cdoc Hospital
Countries: India
Enrollment: 500
Age range: 25 Years - 80 Years
Sex: ALL

Summary:
The Investigators are recruiting T2DM patients (n, 500) from Fortis-CDOC Hospital.

Patients' weight, BMI, lipid profile, liver and kidney function tests, EGG, glycemic parameters, blood pressure, etc. will be entered in MS Excel sheets and appropriate data coding will be performed. Additional information on sleep hygiene, self-perceived stress, environmental pollution, and socio-economic status (education, occupation, and family annual income) will be collected by phone interviews. The entered data will be filtered for outliers and missing data will be excluded from the final data sheet.

Johns Hopkins Team will perform the following:

1. Mediation and moderation analysis,
2. Machine Learning methods
3. Deep Learning and Neural Networks to devise prediction models for different metrics, including diabetes, blood pressure, and lipid control.
4. Traditional statistics like Propensity Score Matching and Multivariate Linear Regression

Data pre-processing The data pre-processing will be performed to standardize the variables and minimize the impact of non-normality. During this step, the raw data would be converted into appropriate transformations. Python and R programming will be used for AI and machine learning methods.

Data analysis Our research collaborators are well versed in techniques like multi-fold cross-validation, Synthetic Minority Oversampling Technique for Nominal and Continuous (SMOTE-NC), a widely used technique for balancing the observations only in the training dataset and not in the testing dataset, and hyper tuning of parameters. For our research, we would require a graphic processing unit (GPU) to perform high-quality and fast computing (especially important when analyzing large data sets through neural networks and machine learning). We have an understanding with ORACLE (a large software giant), for providing GPUs at no cost on a lease basis on the submission of a feasible proposal.

Key Milestones Expected

* During the initial three months of the study, the plan is to obtain all requisite permissions for data gathering from the Institutional Ethics Review Committees of the respective institutions. The research assistant would be recruited from FORTIS-CDOC Hospital.
* Over the next 12 months, there will be data tabulation and gathering
* The last 3-4 months will be allocated to data analysis, application of AI algorithms (using training and testing datasets), and reporting of the data (meetings and manuscripts)

Eligibility criteria:
Inclusion Criteria:

\- T2DM

Exclusion Criteria:

* T1DM
* Genetic Diabetes
* Gestational Diabetes
* Terminal Illness (Cancer)

Outcomes:
Risk of CVD Event
Efficacy and Cost-effectiveness of an Artificial Intelligence System (GI-Genius) on the Characterization of Diminutive Colorectal Polyps Within a Colorectal Cancer Screening Program: a Multicenter Randomized Controlled Trial (ODDITY Trial)
Trial ID: NCT05391477
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-02-27
Completion date: 2024-12
Sponsor: Hospital Universitario La Fe
Collaborators: European Society of Gastrointestinal Endoscopy; Medtronic
Conditions: Colorectal Neoplasms
Interventions: GI-Genius artificial intelligence
Locations: Hospital Universitari i Politècnic La Fe
Countries: Spain
Enrollment: 643
Age range: None - None
Sex: ALL

Summary:
Artificial intelligence is a promising tool that may have a role in characterizing colon epithelial lesions (CADx), helping to get a reliable optical diagnosis regardless of the endoscopist experience. Performances of the different CADx systems are variable but it seems that, in most cases, high accuracy and sensitivities are achieved. However, these CADx systems have been developed and validated using still pictures or videos, and a real-world accurate test is lacking. No clinical trials have tested this technology in clinical practice and, therefore, performance in real colonoscopies, practical problems, applicability, and cost are unknown.

Eligibility criteria:
Inclusion Criteria:

* Patients attending a colonoscopy within a population-based CRC screening program (FIT- or colonoscopy-based) or because of post-polypectomy surveillance,
* Written informed consent before the colonoscopy,

Exclusion Criteria:

* None, patient included
* Previous history of inflammatory bowel disease.
* Previous history of CRC
* Previous CR resection
* Polyposis or hereditary CRC syndrome
* Coagulopathy/Anticoagulants
* Unwillingness to participate

Outcomes:
Comparison of the AIOD and HOD accuracy of the post-polypectomy surveillance interval assignment with respect to the surveillance interval assigned by pathology; Comparison of the AIOD and HOD negative predictive value (NPV) for adenoma in rectosigmoid polyps ≤ 5 mm with respect to histology
A Single Center Study on the Effectiveness and Safety of Polyp Detection and Polyp Classification With Artificial Intelligence
Trial ID: NCT04216901
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2019-12-24
Completion date: 2021-06-30
Sponsor: Renmin Hospital of Wuhan University
Conditions: Colonic Diseases; Artificial Intelligence
Interventions: Endoscopists refer to AI for diagnosis
Locations: Renmin Hospital of Wuhan University
Countries: China
Enrollment: 70
Age range: 18 Years - None
Sex: ALL

Summary:
This is an artificial intelligence-based optical endoscopic polyp diagnosis system that can assist endoscopic doctors in diagnosing polyps and improve the quality of training in clinical Settings.

Eligibility criteria:
Inclusion Criteria:

1. male or female aged 18 or above;
2. colonoscopy and related examinations should be performed to further clarify the characteristics of digestive tract diseases;
3. be able to read, understand and sign the informed consent;
4. the researcher believes that the subject can understand the process of the clinical study, is willing and able to complete all the study procedures and follow-up visits, and cooperate with the study procedures;
5. patients with \> 1cm lesion detected by colonoscopy, requiring magnification staining or surgical resection.

Exclusion Criteria:

1. have participated in other clinical trials, signed the informed consent and have been in the follow-up period of other clinical trials;
2. drug or alcohol abuse or psychological disorder in the last 5 years;
3. pregnant or nursing women;
4. subjects with previous history of intestinal surgery;
5. the researcher considers that the subject is not suitable for colonoscopy and related examination;
6. high-risk diseases or other special conditions that the investigator considers inappropriate for the subject to participate in the clinical trial.

Outcomes:
Accuracy of evaluating the feasibility of selective-ESD; Accuracy of Vision location
Psychotherapeutic Text Messaging for Depression Pilot Study
Trial ID: NCT02872454
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2015-04
Completion date: 2016-04
Sponsor: University of Michigan
Conditions: Major Depression
Interventions: Text Messaging
Locations: University of Michigan Inpatient Psychiatry Unit
Countries: United States
Enrollment: 190
Age range: 18 Years - None
Sex: ALL

Summary:
Major depression is the leading cause of disability in the United States and is a major contributor to suicide, a leading cause of premature death. The majority of individuals with depression do not receive adequate pharmacologic or psychotherapeutic treatment due to difficulty accessing services or stopping treatment due to side effects, non-response, or the stigma associated with attending mental health clinic visits. Mobile health information technology services, such as text messaging, have the potential to provide effective self-management support for depression to nearly every adult in the US with depression. Guided self-help via text messaging has been shown to be effective for improving a range of health behaviors as well as symptoms of depression. However, previously studied depression text messaging services have not utilized the breadth of psychotherapeutic techniques shown to be effective for depression nor have they attempted to tailor the psychotherapeutic content to the individual in order to improve acceptability and outcomes. Advanced artificial intelligence methods (e.g., reinforcement learning) offers the capability to weed out ineffective messages and to target messages to individuals in order to substantially improve program effectiveness. This pilot study is the first step in towards developing an artificially intelligent text message service for depression.

The specific aims of the study are to: 1) demonstrate the feasibility of recruiting and enrolling participants from the general population of US adults and delivering a text-messaging intervention for depression, 2) determine whether there are differences in the perceived helpfulness of messages derived from different psychotherapeutic treatment modalities, and whether these differences are moderated by participant characteristics (e.g., age, gender, depression symptom severity), 3) determine whether messages derived from different psychotherapeutic treatment modalities or their perceived helpfulness are associated with changes in depression symptoms, and whether these relationships are moderated by participant characteristics.

Eligibility criteria:
Inclusion Criteria:

* US resident
* Age 18 or older
* PHQ-9 screening score of 10 or more
* Has a personal cellular phone with a text messaging plan that would allow for as many as 200 additional text messages per month, and agreement that the participant would be responsible for any related charges
* Has a valid e-mail address
* Fluent in English

Exclusion Criteria:

* Unable to provide voluntary informed consent for any reason

Outcomes:
Intervention Feasibility
Combined Gait Training and Transcutaneous Spinal Cord Stimulation to Enhance Lower Limbs Motor Recovery After Subacute SCI
Trial ID: NCT07375745
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-07-01
Completion date: 2029-07
Sponsor: Centre for Interdisciplinary Research in Rehabilitation of Greater Montreal
Collaborators: Mitacs
Conditions: Spinal Cord Injury; Spinal Cord Injury Subacute; Locomotion; Gait Disorder, Sensorimotor; Gait Disorders
Interventions: Protocol 1: Gait training combined with a sham stimulation; Protocol 2: Gait training combined with an effective tSCS
Locations: Institut de réadaptation Gingras-Lindsay-de-Montréal (IRGLM)
Countries: Canada
Enrollment: 40
Age range: 16 Years - None
Sex: ALL

Summary:
Spinal cord injury (SCI) often results in partial or complete loss of movement. In the subacute phase (\< 6 months), the central nervous system shows increased potential for neuroplasticity, making it more responsive to rehabilitation and external stimulation. Standard care in rehabilitation centers relies on activity-based therapy (ABT), which uses intensive, task-specific training to promote recovery. Although ABT can improve mobility, its effects are often limited due to the nature of SCI and the indirect activation of neural circuits.

Recent findings suggest that adding transcutaneous spinal cord stimulation (tSCS) to ABT in chronic SCI (\> 12 months) can enhance lower-limb motor recovery. This study will evaluate whether combining tSCS with gait training is safe and feasible in individuals with subacute SCI and whether it improves lower-limb motor outcomes compared with gait training alone.

The investigators hypothesize that pairing gait training with tSCS early after injury will be safe and feasible and that tSCS delivered during gait training will augment leg muscle activation and lead to greater functional improvements. The study will also assess the feasibility, safety and tolerability of implementing this combined intervention in a intensive functional rehabilitation setting.

Eligibility criteria:
Inclusion Criteria:

* paraplegic or quadriplegic participants with a subacute spinal cord injury (sSCI), defined as the stage of spinal cord damage occuring between 1 week and 6 months post-injury
* above 16 years old
* have a lesion between C1 - L2
* be considered as AIS A to D
* be able to stay at least 10 minutes standing with or without assistance
* can provide informed consent (no cognitive deficits by MoCA)
* be able to follow instructions in French or English

Exclusion Criteria:

* pacemaker
* active cancer on stimulation site or metastatic cancer
* unhealed wound, scar or pain which makes positioning of the electrodes impossible
* participants with specific contraindications to TMS (epilepsy, non-union cranial fracture and increased intracranial pressure) will be able to participate in the study, but will not receive TMS
* chronic and severe neuropathic pain
* pharmacology or implants that may impede with rehabilitation or spinal stimulation
* pregnancy

Outcomes:
Lower Extremity Motor Score