moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Safety and Efficacy of the Olympus Endoscopy Computer-Aided Detection (CADe) System in Detection of Colorectal Neoplasia's During Real-time Colonoscopy: A European Prospective, Multicenter, Randomized Controlled Trial (EuroCADe)
Trial ID: NCT05943288
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-04-25
Completion date: 2024-07-31
Sponsor: Olympus Europe SE & Co. KG
Collaborators: Cromsource
Conditions: Colorectal Cancer
Interventions: OIP-1
Locations: ZU Leuven; Evangelisches Krankenhaus Düsseldorf; Universitätsklinikum Ulm; Fundación Biomédica Galicia Sur; Hospital Universitari I Politécnic La Fe; Linköping Universitetssjukhus
Countries: Belgium; Germany; Germany; Spain; Spain; Sweden
Enrollment: 850
Age range: 45 Years - 80 Years
Sex: ALL

Summary:
This is a prospective, multicenter, randomized Trial to assess the safety and efficacy of the Olympus Endoscopy Computer-Aided Detection (CADe) system, OIP-1, in the detection of colorectal neoplasia's during real-time colonoscopy. The aim of this system is to improve physician performance in the detection of potential mucosal abnormalities during colonoscopy performed for primary Colorectal Cancer screening or postpolypectomy surveillance.

Eligibility criteria:
Inclusion Criteria:

1. Signed informed consent
2. 45-80 years
3. Undergoing colonoscopy for primary CRC screening or postpolypectomy surveillance

Exclusion Criteria:

1. Lack of informed written consent
2. History of CRC or inflammatory bowel disease or polyposis syndrome / familial adenomatous polyposis coli (FAP)
3. Prior failed colonoscopy

   * multiple historic prior failed colonoscopy due to poor bowel preparation OR
   * a single prior failed colonoscopy due to reasons other than poor bowel preparation
4. Biopsy/anesthesia/sedation contraindications
5. History of radio- and/or chemotherapy
6. Concurrent participation in another competing clinical study

Outcomes:
Adenoma Detection Rate (ADR); Positive Predictive Value (PPV)
Validation of a Smartphone App for Anemia Screening Among General Population: A Clinical Trial
Trial ID: NCT05490823
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2018-12-01
Completion date: 2022-12-01
Sponsor: Sun Yat-sen University
Conditions: Anemia
Interventions: A smartphone app for anemia screening
Locations: Zhongshan Ophthalmic Center, Sun Yat-sen University
Countries: China
Enrollment: 1000
Age range: 3 Years - 87 Years
Sex: ALL

Summary:
The current standard for anemia screening, complete blood count, presents invasiveness, infrastructure requirements and high costs, leading to serious underestimation of anemia prevalence and insufficient care for anemia patients. Here, the investigators established and validated an artificial intelligence system to achieve automatic detection of anemia based on patient-sourced images of fingernails and conjunctivae. This system has been integrated into a smartphone app to be further validated through hospital-based and population-based clinical trials.

Eligibility criteria:
Inclusion Criteria:

* Patients should be aware of the contents and signed for the informed consent.

Exclusion Criteria:

* Patients who underwent ophthalmic or fingernail surgery in the past 30 days

Outcomes:
The proportion of accurate, mistaken and missed detection of this smartphone app.
Fall Risk Assessment and Speech Intelligibility Enhancement Using In-ear Device
Trial ID: NCT05365646
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-05-23
Completion date: 2026-10
Sponsor: Stanford University
Collaborators: Starkey Laboratories, Inc
Conditions: Hearing Loss; Dizziness; Vertigo; Fall Related Injury Risk; Fall Prevention; Imbalance; Speech Intelligibility
Interventions: Hearing aids equipped with motion sensing abilities
Locations: Stanford Ear Institute
Countries: United States
Enrollment: 350
Age range: 55 Years - None
Sex: ALL

Summary:
The purpose of this study is to develop and validate methods to use hearing aids equipped with embedded sensors and artificial intelligence to assist in the assessment of fall risk and in the implementation of interventions aimed at reducing the risk of falling, as well as to improve speech intelligibility in quiet and in background noise, track physical activity, and social engagement. The investigators hope is that the knowledge that is generated through this study will ultimately translate to the clinical setting and will help reduce the likelihood that individuals experience a fall, and improve the quality of hearing in individuals who wear hearing aids.

Eligibility criteria:
Inclusion Criteria:

* Age 55 years or older
* Bilateral hearing aid user
* Able to consent in English
* Having access to a phone capable of running the mobile app required for the study
* Screen for fall risk ("Yes" to any question: Feels unsteady when standing or walking? Worries about falling? Has fallen in past year? If the participant has fallen: how many times? were they injured/hurt?)
* Ambulatory without need for a wheelchair

Exclusion Criteria:

* People with mobility restrictions (e.g., wheelchair bound) that would prevent the performance of the functional assessments.
* People with severe movement (e.g., Advanced Parkinson's disease) or cognitive (e.g., advanced dementia) disorders that would prevent the individual from either performing the functional assessments, or would lead to restricted activities that would prevent motion data from being collected according to the goal of the study.
* Inability to understand, consent, and complete requirements including verbal instructions and non-ambulatory/wheelchair-dependent.

Outcomes:
Fall risk assessment using CDC's STEADI protocol; Timed Up and Go Test; 30-Second Chair Stand Test; 4-Stage Balance Test; Consonant-Nucleus-Consonant (CNC); Quick Speech-in-Noise (QuickSIN); Words-in-Noise (WIN); Nonsense Syllable Test (NST)
A Cluster-Randomized Trial of Large Language Model-Enabled Coordinated Management for Hypertension, Diabetes, and Dyslipidemia in Community Settings (LLM-CoManage Trial)
Trial ID: NCT07350486
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-06-15
Completion date: 2027-12-31
Sponsor: First Hospital of China Medical University
Collaborators: Beijing Chao Yang Hospital; Zhejiang University; Shandong Provincial Hospital; Cangzhou Central Hospital
Conditions: Hypertension; Diabetes; Dyslipidemia
Interventions: LLM-Enabled Co-management
Locations: The First Hospital of China Medical University
Countries: China
Enrollment: 6800
Age range: 40 Years - None
Sex: ALL

Summary:
This study aims to evaluate the effectiveness of a large language model (LLM)-supported, community-based integrated management model in improving cardiometabolic multimorbidity control among adults with hypertension and coexisting diabetes or dyslipidemia. Adopting an interventional study design, eligible patients will be recruited to compare the disease control indicators between LLM-assisted management and conventional management, so as to verify the effectiveness and safety of the former.

Eligibility criteria:
Inclusion Criteria:

Participating communities must meet all of the following criteria:

* Have a designated community physician responsible for primary care who is capable of using a smartphone with stable internet access for clinical communication and LLM-assisted management;
* No plan for administrative merger or restructuring within the next three years;
* Located at least 2 kilometers away from adjacent participating communities (to minimize contamination between clusters).

Eligible participants must fulfill all of the following conditions:

* Aged 40 years or older;
* Uncontrolled hypertension, defined as systolic blood pressure (SBP) ≥140 mmHg or diastolic blood pressure (DBP) ≥ 90 mmHg;
* Coexisting diabetes (HbA1c ≥ 7.0%) or coexisting dyslipidemia (LDL-C ≥ 3.4 mmol/L\[130 mg/dL\]);
* Able to use a smartphone independently or with assistance from family members;
* Has resided in the participating community for at least 6 months;
* Has no plan to relocate in the next 3 years;
* Enrolled in basic medical insurance for urban and rural residents, employee medical insurance, or the New Rural Cooperative Medical Scheme;
* Not currently pregnant or planning pregnancy during the study period;
* Free from malignant tumors and deemed to have a life expectancy of at least 3 years, as judged by study physicians;
* Willing and able to provide written informed consent.

Exclusion Criteria:No specific exclusion criteria.

Outcomes:
The between-group difference in the proportion of participants achieving simultaneous control of hypertension and either diabetes or dyslipidemia.
Artificial Intelligence (AI) Assisted Real-time Adenoma Detection and Classification During Colonoscopies
Trial ID: NCT05244278
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-08-01
Completion date: 2027-01-01
Sponsor: Centre hospitalier de l'Université de Montréal (CHUM)
Collaborators: McGill University; University of British Columbia; Université de Sherbrooke; University of Alberta
Conditions: Artificial Intelligence
Interventions: Medtronic-GI genius (CADe system for detecting colorectal polyps)
Locations: Centre Hospitalier Universitaire de Montréal
Countries: Canada
Enrollment: 1596
Age range: 45 Years - 89 Years
Sex: ALL

Summary:
This is a pragmatic, double-blind, randomized, controlled trial, to evaluate the effect of implementing a Computer-assisted detection (CADe) system within the routine clinical practice of Canadian healthcare institutions.

The main hypothesis of this study is that the ADR in the operating room equipped with the GI genius CADe system will be significantly higher than the ADR in the ordinary operating room.

Eligibility criteria:
Inclusion Criteria:

* indication of undergoing a screening, surveillance, or diagnostic colonoscopy
* Age 45-89 years

Exclusion Criteria:

* Patients undergoing emergency colonoscopy
* Patients with a known familial polyposis syndrome or a known inflammatory bowel disease

Outcomes:
ADR
Comparison of The Adenoma Detection Rate Between Artificial Intelligence-assisted Colonoscopy With or Without Endocuff Vision and Standard Colonscopy: A Randomized Controlled Study
Trial ID: NCT05863208
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-05-02
Completion date: 2026-12-31
Sponsor: E-DA Hospital
Collaborators: Chung Shan Medical University
Conditions: Adenoma Detection Rate
Interventions: Artificial intelligence; Endocuff vision; High-definition endoscope
Locations: E-DA Hospital
Countries: Taiwan
Enrollment: 1000
Age range: 40 Years - None
Sex: ALL

Summary:
Adenoma detection rate (ADR) is considered the single most important quality measure in colonoscopy and a higher ADR can reduce the risk of interval colorectal cancer (CRC). Several kinds of new endoscopes and accessories have been accessed to investigate the abilities of improving the ADR. Artificial intelligence (AI) and Endocuff vision are promising new devices to improve the ADR. However, the effect of combining AI and Endocuff vision on ADR remains unclear. The aim of this prospective randomized study is to compare the ADR of AI plus Endocuff vision, AI alone and standard colonoscopy examination.

Eligibility criteria:
Inclusion Criteria:

Patients over 20 years old are undergoing outpatient sedative colonoscopy in the E-Da Hospital, E-Da cancer Hospital and Chung Shan Medical University Hospital in Taiwan

Exclusion Criteria:

* A prior history of of inflammatory bowel disease, colorectal cancer, previous bowel resection, Peutz-Jeghers syndrome, familial adenomatous polyposis or other polyposis syndromes
* Bleeding tendency
* For scheduled endoscopic treatment

Outcomes:
Adenoma detection rate
Use of Artificial Intelligence (AI) for the Prediction of Clinical Outcomes Such as Death and Complications in Patients Hospitalized for COVID Pneumonia During the 4 Pandemic Waves at the ASST of Lecco.
Trial ID: NCT06424925
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-06-01
Completion date: 2024-12-31
Sponsor: Azienda Ospedaliera di Lecco
Conditions: COVID-19 Pandemic
Interventions: Artificial intelligence for the prediction of clinical outcomes
Locations: Stefania Piconi
Countries: Italy
Enrollment: 5000
Age range: 18 Years - None
Sex: ALL

Summary:
Predictive models can be applied in different areas, during the emergency of the COVID-19 pandemic, in fact, they have proven important in supporting health systems in planning strategic decisions and in formulating health policies for the containment of the disease.

The Covid-19 pandemic, in particular, has represented a real challenge for our healthcare system. In Italy, it was divided into four main waves, each characterized by different types of patients and different therapeutic approaches progressively improved based on new scientific evidence.

The objective is to carry out a study on the data of patients hospitalized for COVID-19 at the ASST of Lecco during all four pandemic waves, with different degrees of severity of illness, collecting the data of interest and applying it to they use artificial intelligence to identify recurring patterns of clinical outcome in terms of survival and secondary infectious complications, so as to build new reliable predictive statistical models that can be used to predict the outcome of the patients themselves.

The strong ambition of this project is that the application of artificial intelligence to data of such significant quantity can allow us to build valid statistical models which can then be hypothetically applied to any patient to predict, based on anamnestic characteristics, blood chemical parameters. at baseline and at the set treatment, the probability of survival and complications

Eligibility criteria:
Inclusion Criteria:

* Age \> 18 years;
* Hospitalization for COVID-19 pneumonia at ASST Lecco.

Exclusion Criteria:

* Hospitalization or finding of asymptomatic SARS CoV-2 infection.

Outcomes:
Construction of predictive models
Pivotal Trial of Automated AI-based System for Early Diagnosis of Diabetic Retinopathy Using Retinal Color Imaging
Trial ID: NCT05324189
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-04-18
Completion date: 2024-12-01
Sponsor: The New York Eye & Ear Infirmary
Collaborators: iHealthScreen Inc
Conditions: Diabetic Retinopathy
Interventions: Referable versus Non Referable Diabetic Retinopathy diagnostic test
Locations: New York Eye and Ear Infirmary of Mount Sinai
Countries: United States
Enrollment: 1000
Age range: 18 Years - None
Sex: ALL

Summary:
In this pivotal trial, we aim to perform a prospective study to find the efficacy of iPredict, an artificial intelligence (AI) based software tool on early diagnosis of Diabetic Retinopathy (DR)in the primary care, optometrist and other diabetes-screening clinics. DR is one of the leading causes of blindness in the United States and other developed countries. Every individual with diabetes is at risk of DR. It does not show any symptom until the disease is progressed to advanced stages. If the disease is caught at an early stage, it can be prevented, managed or treated effectively. Currently, screening for DR is done by the Ophthalmologists, which is limited to areas with limited availability. This is also time-consuming and expensive. All of these can be complemented by automated screening and set up the screening in the primary care clinics.

Eligibility criteria:
Inclusion Criteria:

* Age of Subjects: Patients ≥ 18 years of age.
* Gender of Subjects: Both males and females will be invited to participate.
* Subjects with diabetes (A1C level 6.5 or higher) or Fasting Plasma Glucose (blood sugar level) 126 mg/dL (≥7.0 mmol/L)
* Subjects must be willing and are able to comply with clinic visit, understand the study-related procedures/provisions, and provide signed informed consent.
* asymptomatic patients with DR.

Exclusion Criteria:

* Subject has retinal degenerations and retinal vascular diseases such as age-related macular degeneration or having undergone prior retinal surgery.
* History of ocular injections,
* Subject has persistent visual impairment in any eye;
* History of macular edema or retinal vascular (vein or artery) occlusion;
* laser treatment of the retina, or intraocular surgery other than cataract surgery without complications;
* Subject is currently enrolled in an interventional study of an investigational device or drug;
* Subject has ungradable clinical reference standard photographs (i.e., not gradable quality image). If the patient image is not gradable automatically, we will suggest the patient to refer the ophthalmologist.

Outcomes:
Sensitivity of identification of referable and non-referable Diabetic Retinopathy (DR) for early diagnosis of DR; Specificity of identification of referable and non-referable Diabetic
Artificial Intelligence-Assisted/Computer-Guided Crestal Sinus Lifting With Immediate Implant Placement in Periodontally-Compromised Patients Utilizing Electromagnetic Mallet and Autologous Dentin Block Graft: Randomized Clinical Trial
Trial ID: NCT06887582
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-03-21
Completion date: 2026-04-10
Sponsor: Kafrelsheikh University
Conditions: Maxillary Sinus Disease; Alveolar Bone Loss
Interventions: Free-hand sinus lift augmanted by particulate autologous dentin graft + free-hand implant placement; AI-assisted/computer-guided sinus lift augmanted by particulate autologous dentin graft + computer-guided implant placement; Free-hand sinus lift with autologous dentin block graft +free-hand immediate implant placement; AI-assisted/computer-guided sinus lift with autologous dentin block graft +computer-guided immediate implant placement.
Locations: faculty of dentistry, kafrelsheikh University
Countries: Egypt
Enrollment: 44
Age range: 18 Years - 40 Years
Sex: ALL

Summary:
The integration of artificial intelligence and computer-guided technology offers the potential for improving surgical accuracy, reducing complications, and enhancing outcomes. Additionally, advancements such as electromagnetic mallets for bone manipulation and autologous dentin grafts provide promising alternatives for bone regeneration. This study aims to combine these technologies to assess the efficacy and clinical outcomes of AI-assisted, computer-guided crestal sinus lifting with immediate implant placement in periodontally compromised sites.

Eligibility criteria:
Inclusion Criteria:

* • Patients requiring sinus augmentation with insufficient bone height (4-7 mm).

  * Indicated for single or multiple implant placements.
  * No systemic contraindications for implant surgery.

Exclusion Criteria:

* • Severe sinus pathology (e.g., chronic sinusitis).

  * History of radiation therapy in the maxillofacial region.
  * Smokers
  * Presence of acute (active) periapical infection.
  * Large chronic periapical lesion.

Outcomes:
Bone gain
Artificial Intelligence Diagnostic Aid
Trial ID: NCT05675540
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-06-01
Completion date: 2024-03-30
Sponsor: Guy's and St Thomas' NHS Foundation Trust
Conditions: Wet Macular Degeneration; Diabetic Macular Edema; Retinal Vein Occlusion
Interventions: Macusense Assessment Software
Locations: Guy's and St Thomas's NHS Foundation Trust
Countries: United Kingdom
Enrollment: 422
Age range: None - 100 Years
Sex: ALL

Summary:
The investigators have worked with software designers to develop a software that allows us to analyse current adherence to guidelines on Ophthalmic conditions such as Age related Macular Degeneration (AMD), Diabetic Macular Edema (DMO) and Retinal vein occlusion (RVO). National guidelines state that those patients with fluid accumulation in their central macular, meeting criteria, are eligible for injections into the vitreous cavity of the eye (intravitreal).(1) As these condition are common the trial is relevant to the public and patients as future management may be affected by the outcomes of this trial. The investigators will trial the software which uses Artificial Intelligence (AI) algorithms to determine the most suitable review required for patients being managed in clinics, based on 'Vision' and 'Retinal Thickness' demographics. This will be done prospectively, in real time.

The question to be addressed is 'Can medical and non-medical practitioners accurately determine treatment and follow-up for patients assisted by an AI clinical decision support system, for the three most common chronic macular diseases - Wet Age-Related Macular Degeneration (wAMD), Diabetic Macular Oedema (DMO) and Retinal Vein Occlusion (RVO) - in a safe and clinically cost effective way?' Patients undergoing treatment for at least 12 months are eligible to participate, so long as they are able to provide consent for their data to be used. Participants will have no change to their care during the trial. The study, will take place at Guy's and St. Thomas' NHS FT (GSTT) from where participants will be recruited, and will last approximately 6 months of data collection.

The software will be used by the research Fellow, alongside the masked consultant. Therefore the patient pathway and management will not be impacted by this trial. Patients will be consented for data use.

Eligibility criteria:
Inclusion Criteria:

* Patients with a diagnosis of wet Age Related Macular Degeneration (wAMD), Diabetic Macular Oedema (DMO) and/or Retinal Vein Occlusion (RVO), who have already had a course of intravitreal treatment for at least one year.

Exclusion Criteria:

* Patients with more than one ophthalmic pathology will be excluded
* A patient will be excluded if, during the first year of treatment, they have needed cataract surgery or vitreoretinal surgery or have developed macular pathology other than wet AMD, DMO or RVO.
* Patients included in other trials during the period the trial is running, will be excluded from eligibility.
* Patients who do not wish to/are unable to consent will be excluded from the trial.
* Patients aged 100 years and over

Outcomes:
Percentage agreement