moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Clinical Validation of a Computer-Aided Diagnosis (CAD) System Utilizing Artificial Intelligence Algorithms for Continuous and Remote Monitoring of Patient Condition Severity in an Objective and Stable Manner
Trial ID: NCT06237036
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-03-03
Completion date: 2023-10-23
Sponsor: AI Labs Group S.L
Collaborators: University Hospital of Torrejon; Ribera Salud Hospitals, Spain
Conditions: Skin Diseases; Skin Condition
Locations: Torrejón University Hospital
Countries: Spain
Enrollment: 160
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this observational study is to learn if an artificial intelligence (AI) tool, called Legit Health Plus, can track the severity of chronic skin conditions from a distance. The study included 160 participants who have various skin issues, such as acne, psoriasis, or atopic dermatitis (a type of eczema). The main questions it aims to answer are:

* Can this computer-aided diagnosis (CAD) system reliably track how a person's skin condition changes over time?
* Does using the tool lead to fewer in-person doctor visits?
* Are participants satisfied with using the tool at home?

Because this study focuses on evaluating the tool in a real-world setting, researchers did not use a comparison group.

What Participants Will Do

Participants will use a smartphone app for 6 months to help their doctors monitor their skin. They will:

* Take photos of their skin with their own smartphones and send them to their doctor through the app.
* Answer survey questions about their symptoms and how the condition affects their daily life.
* Complete surveys every two months to share if they are satisfied with the tool and if it is easy to use.

How Utility and Usability are Assessed

After the study, researchers and doctors will assess if the tool is practical and helpful for medical practice using several methods:

* Clinical Utility Questionnaire (CUS): Doctors use this to rate how well the AI tool tracks disease progression and helps them prioritize which participants need care first.
* Time Tracking: Researchers check if the tool lowers the time doctors spend on visits, allowing them to manage their workload more efficiently.
* System Usability Scale (SUS): Both doctors and participants use this to rate if the app is easy to navigate, simple to learn, and not too complex.
* Data Utility Questionnaire (DUQ): Doctors judge if the information collected by the app is useful for their regular practice and remote consultations.

Eligibility criteria:
Inclusion Criteria:

* Patients who have provided their informed consent for participation in the study.
* Patients who demonstrate proficiency in both written and spoken Spanish or English.
* Patients who possess a smartphone, defined as a phone equipped with internet access and an integrated camera, regardless of make, model, or technical specifications.

Exclusion Criteria:

* Patients who, as determined by the investigator, did not adhere to the study procedures.
* Patients who were already utilizing the tool under investigation prior to the commencement of the study

Outcomes:
Clinical Utility Questionnaire (CUS) Score
GastroBot: a New Artificial Intelligence-developed Software Bot to Improve Bowel Preparation and Colonoscopy Quality
Trial ID: NCT05836064
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-01-01
Completion date: 2025-04-28
Sponsor: Institute of Gastroenterology and Advance Endoscopy
Conditions: Colonic Polyp; Colonic Neoplasms; Colonic Disease; Colonic Adenoma
Interventions: GastroBot; Conventional explanation
Locations: Institute of Gastroenterology and Advanced Endoscopy (IGEA)
Countries: Argentina
Enrollment: 388
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
It is estimated that about 20% of colonoscopies have inadequate preparation. (5) This is associated with lengthy procedures and less detection of adenomas, reduces the screening intervals, and increases the costs and risks of complications. Several strategies have been proposed to improve the quality of bowel preparation. Mobile healthcare Apps have been developed to increase adherence to bowel preparation agents, improving the quality of bowel preparation. However, adherence to mobile healthcare Apps is also a quality criterion and a pending problem to solve with this new technology.

GastroBot is a new technology based on artificial intelligence that allows, through a software bot, to carry out a personalized follow-up of the patient's bowel cleansing, advising the patient to overcome contingencies that arise with the preparation, which in other circumstances could lead to the failure of it. The primary aim of this study is to determine the improvement in bowel preparation after GastroBot assistance compared with the traditional explanation. As a secondary aim, this study also pursues to determine adenoma and polyp detection rates (ADR and PDR, respectively), bowel preparation agents' tolerance, and GastroBot functionality.

Eligibility criteria:
Inclusion Criteria:

* Age under 18 and over 80 years old.
* Who agrees to participate in the study and can understand and provide written informed consent.
* Any colonoscopy indication: colorectal neoplasia screening, surveillance of colon pre-existing diseases, or diagnostic approach in symptomatic patients.
* Smartphone owners (any device) and WhatsApp users, independence of local or international mobile phone provider.

Exclusion Criteria:

* Scheduled colonoscopies with any therapeutic approach will be categorically excluded if it does not have a cecal intubation indication.
* Patients with difficulty understanding instructions for bowel preparation or not being able to use WhatsApp.
* History of diabetes mellitus with insulin therapy, heart disease, kidney, liver, or severe metabolic disorder.
* Phenprocoumon therapy or severe uncontrolled coagulopathy
* Pregnancy and lactation
* Prior history of colon resection, ileostomy, or colostomy

Outcomes:
Colonoscopy cleansing in terms of Boston bowel preparation score (BBPS); Re-scheduled colonoscopy
Efficiency of an Algorithm Derived From Corneal Tomography Parameters to Distinguish Highly Susceptible Corneas to Ectasia From Healthy
Trial ID: NCT04313387
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2012-01-01
Completion date: 2018-01-01
Sponsor: Gildasio Castello de Almeida Junior
Collaborators: Fundação de Amparo à Pesquisa do Estado de São Paulo
Conditions: Keratoconus, Artificial Intelligence, Support Vector Machine
Interventions: Corneal tomography multivariate index derived from a support vector machine (CTMVI).
Enrollment: 588
Age range: None - None
Sex: ALL

Summary:
The objective of this study was to identify and build an algorithm through an imaging process using a support vector machine (SVM) with the tomography variables of cases with, KC, highly susceptible corneas to ectasia (HSCE), and healthy corneas and to compare this algorithm to BAD-D (Belin\_Ambrosio Display) and PRFI (Pentacam Random Forest Index). The study included 148 eyes with KC, 351 with healthy corneas, and 88 eyes with HSCE.

Eligibility criteria:
Inclusion Criteria:

Patients were considered to be very asymmetric (VAE-NT) if the diagnosis of ectasia was confirmed in one eye based on the previously described criteria and the fellow eye had a normal front surface curvature (topometric) map. Objective criteria for considering normal topography was applied for defining the cases of VAE-NT, including objective front surface curvature metrics derived from Pentacam. Normal topography was rigorously considered based on objective criteria (27, 28) of a maximum curvature Kmax (Steepest Front Keratometry) \<47.2 diopters, a paracentral inferior-superior (I-S value) asymmetry value at 6 mm (3-mm radii) \< 1.45, and keratoconus percentage index (KISA%) score \< 60 and (29). The cutoff point used to discriminate normal corneas and VAE-NT from KC corneas was the maximal posterior elevation (\< 29 µm). This cutoff point had been determined in a previous study, using the same instrument and the same setting. The posterior elevation map was displayed with a 5-mm color-coded scale, and maximal posterior elevation was measured manually using the cursor in the central 5 mm.

Exclusion Criteria:

The following exclusion criteria were adopted: history of ocular trauma; chronic use of eye medication, glaucoma; dry eye syndrome; corneal scarring; neurotrophic keratopathy; severe meibomian gland dysfunction; vulnerable state owing to physical or mental illness and with language-related difficulties; pregnant or breastfeeding.

Outcomes:
CTMVI designed to screen patients prior to refractive surgery
Electrocardiogram Artificial Intelligence-Guided Screening for Low Ejection Fraction (EAGLE)
Trial ID: NCT04000087
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2019-06-26
Completion date: 2020-11-01
Sponsor: Mayo Clinic
Conditions: Asymptomatic Left Ventricular Systolic Dysfunction (Disorder); Heart Failure
Interventions: AI-enabled ECG-based Screening Tool
Locations: Mayo Clinic in Rochester
Countries: United States
Enrollment: 358
Age range: 18 Years - None
Sex: ALL

Summary:
This is a randomized controlled trial (RCT) to test a novel artificial intelligence (AI)-enabled electrocardiogram (ECG)-based screening tool for improving the diagnosis and management of left ventricular systolic dysfunction.

Eligibility criteria:
Inclusion Criteria:

* Primary care clinicians who are part of a participating care team that care for adult patients and have the ability to order ECG and TTE (this includes physicians, nurse practitioners, and physician assistants).

Exclusion Criteria:

* Primary care clinicians working in pediatrics, acute care, nursing homes, and resident care teams.

Outcomes:
New Diagnosis of Low Ejection Fraction (defined as ejection fraction ≤50%)
A Novel Simplified Endoscopic Score aiMed at Evaluating Ulcerative cOlitis Activity Through TXI, RDI and NBI Vascular Assessment and at prEdicting Clinical Outcome and Its Applicability in an arTificial Intelligence System: the MONET Study
Trial ID: NCT06709209
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-11-04
Completion date: 2027-09-30
Sponsor: University College Cork
Collaborators: Olympus
Conditions: Ulcerative Colitis (UC)
Interventions: Colonoscopy after bowel cleansing; Intestinal biopsies; blood sampling; Stool; Clinical follow-up
Locations: University of Leuven; Klinikum Luneburg; University College Cork; Ospedale S. Maria del Prato; University of Bari; Istituto Clinico Humanitas; University Vita-Salute San Raffaele; University of Naples; University of Pavia; Showa University; Changi General Hospital
Countries: Belgium; Germany; Ireland; Italy; Italy; Italy; Italy; Italy; Italy; Japan; Singapore
Enrollment: 300
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
This international multicentre prospective study aims to develop a new simple score using enhanced endoscopic techniques which focus on the vascular features of the colon and reliably distinguish between a quiescent and a mild inflammation in ulcerative colitis (UC). The diagnostic performance of the new score in defining disease activity/remission compared to existing endoscopic and histological scores and predict long-term clinical outcomes will be evaluated. The study also aims to adapt current artificial intelligence (AI) algorithms for enhanced endoscopic techniques to improve standardization in UC disease assessment and outcome prediction.

Eligibility criteria:
Inclusion Criteria:

* Adult patients aged 18 to 75 years old
* Established diagnosis of UC (for at least six months in duration), independently from their active treatment
* Undergoing endoscopy for disease activity assessment or cancer surveillance.

Exclusion Criteria:

* Contraindications to endoscopy (including toxic megacolon) and biopsies (including severe coagulopathy/thrombocytopenia)
* Poor bowel preparation (defined as total BBPS \<6 or BBPS \<2 in observed segment for sigmoidoscopy)
* Significant co-morbidities limiting life expectancy and conferring high risk of endoscopy
* Pregnant and breast-feeding subjects
* Inability to provide informed consent
* If the participant has been in a recent experimental trial, these must have been completed not less than thirty days prior to this study

Outcomes:
Diagnostic performance of the new scoring system
An Artificial-intelligence-powered Intervention to Aid the Informed Consent Process for Cataract Surgery for English Speaking and Bengali Speaking Patient Population Groups: A Mixed-methods Randomised Control Trial
Trial ID: NCT06528509
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-09-11
Completion date: 2026-09-11
Sponsor: Moorfields Eye Hospital NHS Foundation Trust
Collaborators: Medical Protection Society Foundation
Conditions: Cataract
Interventions: AI-Generated Informative Videos; Standard Written Information Brochures
Locations: Moorfields Eye Hospital Stratford; Moorfields Eye Hospital
Countries: United Kingdom; United Kingdom
Enrollment: 200
Age range: 18 Years - None
Sex: ALL

Summary:
This research focuses on enhancing the informed process for patients before undergoing cataract surgery. Currently, this process involves verbal discussions and written materials that may not fully address patients' understanding of the surgery, its risks, and benefits. This study aims to introduce an innovative approach using artificial intelligence (AI) to generate informative videos tailored to patients' needs, accounting for education level, ethnicity, and language barriers.

Why is this important? Informed consent is not just a legal requirement; but it's a fundamental patient right. Ensuring patients truly understand their treatment options leads to better satisfaction, both from the patient and clinician's perspective.

This study compares the effectiveness of AI-generated videos against traditional information delivery methods (information brochures). Patients scheduled for cataract surgery will experience either the new AI video approach or the standard process. The goal is to see if the videos lead to better understanding of the procedure and overall satisfaction. The investigators will be running the study in two parallel cohorts - with Cohort A involving an English speaking population, and Cohort B a Bengali speaking population with all patient facing materials translated into Bengali.

This research is crucial for patients seeking clarity about cataract surgery, for doctors aiming to improve patient care, and for researchers exploring patient education innovations. Funded by the Medical Protection Society (MPS), the investigators hope this intervention has broader effects such as reducing the number of complaints and litigations, improving the efficiency of consenting for surgery and delivering information, and improving theatre utilisation by reducing last minute cancellations.

Eligibility criteria:
Inclusion Criteria:

* Have capacity to consent
* Listed for first time cataract surgery
* English speakers for Cohort A
* Bengali speakers for Cohort B

Exclusion Criteria:

* Previously undergone cataract surgery
* Partner who has undergone cataract surgery
* Below the age of 18
* Unable to utilise a device for watching information videos and completing quiz

Outcomes:
Information understanding and retention
Retrospective Multicentric European Study In Patients With Vertebral Metastases (Train-Metastra)
Trial ID: NCT06220071
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-02-01
Completion date: 2024-12-31
Sponsor: Istituto Ortopedico Rizzoli
Conditions: Spinal Metastases
Interventions: Case series review of clinical and radiographic data
Locations: Charite - Universitaetsmedizin Berlin; Budai Egeszsegkozpont Zrt; Universitair Medisch Centrum Utrecht
Countries: Germany; Hungary; Netherlands
Enrollment: 356
Age range: 18 Years - None
Sex: ALL

Summary:
Train-METASTRA is a retrospective study that will be performed in order to collect a large and harmonised amount of clinical and imaging data concerning vertebral metastases, focusing in particular on the risk of fractures. This type of dataset will be created from the medical records of 2000 patients admitted in the last ten years in the four European clinical centers participating in METASTRA project: "COMPUTER-AIDED EFFECTIVE FRACTURE RISK STRATIFICATION OF PATIENTS WITH VERTEBRAL METASTASES FOR PERSONALISED TREATMENT THROUGH ROBUST COMPUTATIONAL MODELS VALIDATED IN CLINICAL SETTINGS", funded by the European Union under the call "HORIZON-HLTH-2022-TOOL-12-two-stage/Computational models for new patient stratification strategies". The project is coordinated by the University of Bologna (UNIBO) (PI prof. Luca Cristofolini) and involves 15 European partners, including Sarl Voisin Consulting Life Sciences VCLS, University of Szeged (Hungary), University of Sheffield (UK) and FrontEndART (Hungary).

This type of dataset is not currently available in the literature and it will be pivotal to the development of the METASTRA computational models for the stratification of the risk of fracture of patients affected by spinal metastases.

Eligibility criteria:
Inclusion Criteria:

* Alive or dead
* Age 18 -99 years old
* Both male and female
* Diagnosis of cancer metastases at the spine
* Any SINS score
* Admitted for hospitalisation or outpatient visit from Jan 2012 to Dec 2022 N ≥ 1 non-stabilised metastatic vertebra
* Presence of baseline CT scan, and/or MRI and x-rays, showing the presence of metastatic lesions that are not surgically treated
* At least one follow-up visit at ≥ 3 months after the first evaluation or treatment

Exclusion Criteria:

* Minor patients
* Patients with diagnosis of primary spine tumour, degenerative spinal diseases, deformity or trauma
* Patients without non-stabilised vertebral metastases
* Patients without baseline CT scan or MRI
* Patients without follow up visits

Outcomes:
risk factors for vertebral fracture; risk factors for vertebral fracture
Assessment of a Radiomics-based Computer-Aided Diagnosis Tool for Cancer Risk Stratification of Pulmonary Nodules
Trial ID: NCT05968898
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-01-09
Completion date: 2027-12-31
Sponsor: Abramson Cancer Center at Penn Medicine
Conditions: Lung Cancer; Pulmonary Nodule, Solitary
Interventions: Optellum Virtual Nodule Clinic
Locations: Penn Medicine University City; Perelman Center for Advanced Medicine; Penn Medicine Washington Square
Countries: United States; United States; United States
Enrollment: 300
Age range: 35 Years - 89 Years
Sex: ALL

Summary:
This is a pragmatic clinical trial that will study the effect of a radiomics-based computer-aided diagnosis (CAD) tool on clinicians' management of pulmonary nodules (PNs) compared to usual care. Adults aged 35-89 years with 8-30mm PNs evaluated at Penn Medicine PN clinics will undergo 1:1 randomization to one of two groups, defined by the PN malignancy risk stratification strategy used by evaluating clinicians: 1) usual care or 2) usual care + use of a radiomics-based CAD tool.

Eligibility criteria:
Inclusion Criteria:

1. Male or female, aged 35-89 years
2. Scheduled to be evaluated at a UPHS PN clinic
3. Newly discovered solid or part-solid indeterminate PN 8-30mm in maximal diameter on CT imaging within 60 days of index clinic visit
4. Chest CT imaging meeting the technical requirements for compatibility with Optellum Virtual Nodule Clinic software

Exclusion Criteria:

1. Chest CT imaging with discrete mediastinal or hilar lymphadenopathy by CT size criteria (\>10mm in maximal short-axis diameter on axial CT images)
2. PNs with popcorn calcification (consistent with benign etiology)
3. Pure ground-glass subsolid PNs (may be associated with lower risk of clinically significant malignancy)
4. PN previously seen on CT imaging \>60 days prior to most recent CT
5. More than one indeterminate PN 8-30mm in maximal diameter
6. History of lung cancer
7. History of active cancer within the previous 5 years
8. Presence of a thoracic implant that impedes PN visualization

Outcomes:
Appropriate management of pulmonary nodule
AI Ready and Exploratory Atlas for Diabetes Insights
Trial ID: NCT06002048
Status: ENROLLING_BY_INVITATION
Study type: OBSERVATIONAL
Start date: 2023-07-19
Completion date: 2027-01-01
Sponsor: University of Washington
Collaborators: National Institutes of Health (NIH)
Conditions: Type 2 Diabetes
Locations: University of Alabama, Birmingham; UC San Diego; University of Washington
Countries: United States; United States; United States
Enrollment: 4000
Age range: 40 Years - 85 Years
Sex: ALL

Summary:
The study will collect a cross-sectional dataset of 4000 people across the US from diverse racial/ethnic groups who are either 1) healthy, or 2) belong in one of the three stages of diabetes severity (pre-diabetes/diet controlled, oral medication and/or non-insulin-injectable medication controlled, or insulin dependent), forming a total of four groups of patients. Clinical data (social determinants of health surveys, continuous glucose monitoring data, biomarkers, genetic data, retinal imaging, cognitive testing, etc.) will be collected. The purpose of this project is data generation to allow future creation of artificial intelligence/machine learning (AI/ML) algorithms aimed at defining disease trajectories and underlying genetic links in different racial/ethnic cohorts. A smaller subgroup of participants will be invited to come for a follow-up visit in year 4 of the project (longitudinal arm of the study). Data will be placed in an open-source repository and samples will be sent to the study sample repository and used for future research.

Eligibility criteria:
Inclusion Criteria:

* Adults (≥ 40 years old)
* Patients with and without type 2 diabetes
* Able to provide consent
* Must be able to read and speak English

Exclusion Criteria:

* Adults older than 85 years of age
* Pregnancy
* Gestational diabetes
* Type 1 diabetes

Outcomes:
Best-corrected visual acuity; Contrast Sensitivity; Optical coherence tomography (OCT); fundus photography; fluorescence lifetime imaging ophthalmoscopy (FLIO); optical coherence tomography angiography (OCTA); Continuous Glucose Monitoring; Home humidity; Home temperature; Volatile Organic Compounds (VOC) in home; Fine particulate matter that are 2.5 microns or less in diameter (PM2. 5) in home; Montreal Cognitive Assessment (MoCA)
Artificial Intelligence-enabled Large-scale Electrocardiogram Feature Extraction and Exploring Association Between the Extracted Features and Mortality, Stroke or Various Health Outcome of Interest
Trial ID: NCT06179849
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-12
Completion date: 2025-12
Sponsor: Yonsei University
Conditions: Health Status(Death, Stroke Etc)
Locations: Yonsei University Health System
Countries: South Korea
Enrollment: 3000000
Age range: None - None
Sex: ALL

Summary:
* In this study, large-scale ECG data (Electrocardiogram data of all patients stored in the MUSE system by measuring standard 12-guided ECG at Severance Health Checkup at Severance Hospital from November 1, 2005 to October 31, 2022) are combined with electronic medical records, National Health Insurance Corporation data, and National Statistical Office death cause data, and the artificial intelligence algorithm is used to extract ECG features to analyze the association between death, stroke, and various health conditions, and to conduct external verification or transfer learning using public databases (e.g., UK Biobank data).
* Intended to use a web-based artificial intelligence platform to distribute computational loads generated during large-scale data processing and improve analysis accuracy and efficiency.

Eligibility criteria:
Inclusion Criteria:

All patients stored in the MUSE system after measuring a standard 12-guided electrocardiogram at Severance Health Checkup at Severance Hospital from November 1, 2005 to October 31, 2022

Outcomes:
Number of patients with Mortality; Number of patients with Stroke, Atrial Fibrillation, Dementia