moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
A Pilot, Observational, Prospective Study to Evaluate the Performance of CHLOE Algorithm on the Prediction of Blastocyst Formation in Women Undergoing In-vitro Fertilization
Trial ID: NCT05455281
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-07-27
Completion date: 2023-07-27
Sponsor: Fairtility
Conditions: Fertility Disorders
Interventions: CHLOE
Locations: IVF Florida; Mayo clinic; Hadassah University Hospital
Countries: United States; United States; Israel
Enrollment: 55
Age range: 18 Years - None
Sex: FEMALE

Summary:
This is an observational, prospective, single-arm, multi-center, clinical study to evaluate the performance of CHLOE, an Artificial Intelligence (AI)-based software application, as adjunct information to support identifying embryos on Day 3 that are most likely to form blastocysts.

Eligibility criteria:
Inclusion Criteria:

* Women undergoing fresh IVF treatment using their own eggs
* At least 18 years of age
* Total antral follicle count (AFC) of at least 12 as measured by ultrasound prior to stimulation (in cases where AFC is performed)
* Basal day 3 follicle-stimulating hormone levels (FSH) \< 10 IU
* At least 8 normally fertilized eggs at pronuclear (2PN) stage
* Fertilization using only ejaculated sperm (fresh or frozen) - no surgically removed sperm
* At least 2 cells embryo
* At least part of the embryos were cultured until Day 5 (i.e., 114-116 hours)
* IVF treatment cycles were performed during 2020-2021

Exclusion Criteria:

* Use of re-inseminated eggs
* Embryos that underwent day 3 biopsy for preimplantation genetic testing (PGT)
* Gestational carriers
* Concurrent participation in another clinical study
* Previous enrollment in this clinical study (i.e., previous cycles of the same women)
* History of cancer
* Embryos that underwent biopsy at cleavage stage

Outcomes:
The association between the adjunct prediction using CHLOE of blastocyst outcome and the actual blastocyst outcome for a subset of good/fair embryos
Metabolic Characteristics of Plasma OxPLs in STEMI Patients and Its Value Analysis in Predicting MACEs Risk After PCI Surgery.
Trial ID: NCT07382700
Status: ENROLLING_BY_INVITATION
Study type: OBSERVATIONAL
Start date: 2025-01-01
Completion date: 2027-12-31
Sponsor: First Affiliated Hospital of Ningbo University
Conditions: STEMI - ST-segment Elevation Myocardial Infarction
Locations: First Affiliated Hospital of Ningbo University
Countries: China
Enrollment: 240
Age range: 50 Years - 70 Years
Sex: ALL

Summary:
The goal of this observational study is to investigate the structural composition and metabolic characteristics of OxPLs in STEMI patients. Explore OxPLs characteristic fingerprint biomarkers from different populations and analyze OxPLs fingerprint spectra. Based on the OxPLs fingerprint of STEMI patients, a machine learning classifier is used to establish an artificial intelligence assisted post PCI MACE risk prediction system, achieving the transformation of basic research into clinical application.

Eligibility criteria:
Inclusion Criteria:

* The patient meets the diagnostic criteria for STEMI (refer to the "Diagnosis and Treatment Guidelines for Acute ST Segment Elevation Myocardial Infarction (2021)")
* Agree to perform direct PCI
* The onset time is less than 12 hours and successfully undergoing emergency PCI treatment
* After admission, all STEMI patients are routinely treated with antiplatelet aggregation, anticoagulation, lipid-lowering, and anti myocardial remodeling drugs.

Exclusion Criteria:

* Previous history of myocardial infarction
* Accompanied by other cardiovascular diseases, such as severe heart valve disease, severe heart failure, congenital heart disease, cardiomyopathy, etc
* Merge with severe infection
* Merge with severe anemia
* Merge with severe liver and kidney dysfunction
* Combined with malignant tumors
* Accompanied by a history of autoimmune or hematological diseases
* Recent history of severe trauma or surgery
* Patients with incomplete clinical data
* The patient does not agree to undergo data collection

Outcomes:
Number of Participants with Treatment-Related Adverse Events as Assessed by CTCAE v5.0
Accuracy of CAD Eye in the Detection of Colonic Remaining Lesions After Endoscopic Mucosal Resection: a Pilot Study
Trial ID: NCT05542030
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-09-12
Completion date: 2024-09-12
Sponsor: Instituto Ecuatoriano de Enfermedades Digestivas
Conditions: Colorectal Dysplasia; Colorectal Neoplasms
Interventions: EMR with CAD-Eye™; EMR without CAD-Eye™; Follow-up colonoscopy with CAD-Eye™
Locations: Carlos Robles-Medranda
Countries: Ecuador
Enrollment: 60
Age range: 18 Years - 90 Years
Sex: ALL

Summary:
In the last decade, many innovative systems have been developed to support and improve the diagnosis accuracy during endoscopic studies. CAD-Eye™ (Fujifilm, Tokyo, Japan) is a computer-assisted diagnostic (CADx) system that uses artificial intelligence for the detection and characterization of polyps during colonoscopy. However, the accuracy of CAD-Eye™ in the recognition of remaining lesions after endoscopic mucosal resection (EMR) has not been broadly evaluated.

Finally, based on the importance of complete resection of the colonic mucosal lesions, namely suspicious high-grade dysplasia or early invasive cancer, the investigators aimed to assess the accuracy of CAD-Eye™ in the detection of remaining lesions after the procedure.

Eligibility criteria:
Inclusion Criteria:

* Patients referred to our center with an indication of colonoscopy and EMR for the treatment of lesions suspicious of high-grade dysplasia and early invasive cancer.
* Patients who authorize EMR and colonoscopy.
* Signed informed consent

Exclusion Criteria:

* Any clinical condition which makes EMR inviable.
* Poor bowel preparation score defined as the total Boston bowel preparation score (BBPS) \<6 and the right-segment score \<2
* Patients with more than one previous EMR
* Lost on a three-month follow-up after EMR
* Pregnancy or nursing

Outcomes:
Lesions recurrence after EMR; Lesions recurrence in a three-month follow-up after EMR
The Clinical Benefit of an Artificial Intelligence Software Implementation on Diabetic Retinopathy Screening
Trial ID: NCT04631653
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-12-01
Completion date: 2023-12-31
Sponsor: Taichung Veterans General Hospital
Conditions: Diabetic Retinopathy
Interventions: VeriSee®
Locations: Division of Endocrinology and Metabolism in Taichung Veterans General Hospital
Countries: Taiwan
Enrollment: 1000
Age range: 20 Years - None
Sex: ALL

Summary:
The investigators aim to improve the diagnostic accuracy and the clinical referral rate for diabetic retinopathy by using a deep learning-based software.

Eligibility criteria:
Inclusion Criteria:

* Adults
* Patients with diabetes
* Cooperation to fundal scopic examination

Exclusion Criteria:

* Diabetic duration \< 5 years in patients with type 1 diabetes
* Pregnancy

Outcomes:
diagnostic accuracy
Peking University People's Hospital Radiology
Trial ID: NCT05243121
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-02-28
Completion date: 2025-09-30
Sponsor: Peking University People's Hospital
Conditions: Breast Neoplasms; Magnetic Resonance Imaging
Interventions: MRI
Locations: Peking university people's hospital
Countries: China
Enrollment: 5000
Age range: None - None
Sex: FEMALE

Summary:
Use Convolutional Neural Networks Analysis for Classification of Contrast-enhancing Lesions at Multiparametric Breast MRI. Build an abbreviated protocal, and investigate whether an abbreviated protocol was suitable for breast magnetic resonance imaging screening for breast mass in Chinese women, which can shorten the examination time and avoid enhanced imaging while ensuring the accuracy of the diagnosis.

Eligibility criteria:
Inclusion Criteria:

* Patients with clinical symptoms (define as palpable mass, nipple discharge, asymmetric thickening or nodules, and abnormal skin changes)
* Patients undergoing full sequence BMRI examination
* Through the follow-up database, at least 6 months of follow-up results can be obtained to determine whether the diagnosis result is negative/benign/malignant; for patients who need pathological biopsy, the pathological biopsy results shall prevail to determine the lesion benign/malignant

Exclusion Criteria:

* The breast had received radiotherapy, chemotherapy, biology and other treatments before BMRI.
* There are contraindications for breast-enhanced MRI examinations such as allergy to contrast agents.
* A prosthesis is implanted in the affected breast.
* Patients during lactation or pregnancy

Outcomes:
Breast Cancer Screening
Optimizing Acute Ischemic Stroke Diagnostics Using Artificial Intelligence
Trial ID: NCT05652933
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-12-10
Completion date: 2025-12-31
Sponsor: Oslo University Hospital
Collaborators: Vestre Viken Hospital Trust; University Hospital of North Norway; University of Calgary
Conditions: Stroke, Acute
Interventions: Software for identifying vessel occlusion, infarct volume and penumbra
Locations: Vestre Viken Hospital Trust; Oslo University Hospital; Østfold Hospital Trust
Countries: Norway; Norway; Norway
Enrollment: 300
Age range: 18 Years - None
Sex: ALL

Summary:
Prospective observational multi-center study with the aim to organise and simplify the care pathway through a pragmatic approach to acute stroke imaging powered by cutting edge advances in image processing and artificial intelligence.

Eligibility criteria:
Inclusion Criteria:

* Patients with ischemic stroke.
* All stroke severities and vascular distributions are eligible.
* Informed written consent signed by the patient, verbal consent from the patient as witnessed by a non-participating health care person or consent by the signature of the patient's family must be provided before inclusion. Patients for whom no informed consent can be obtained will not be included in the study but will be treated according to standard guidelines.

Exclusion Criteria:

• Patients not available for follow-up assessments (e.g. non-resident).

Outcomes:
Time from the start of CT scan of patients at the local hospital to radiological diagnosis in acute stroke patients with large and medium vessel occlusion in periods with the use of AI software compared to periods with standard care.
Early Warning Model of Myocardial Remodeling After Acute Myocardial Infarction Based on Multimodal Feature Structure Technology
Trial ID: NCT06062316
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-10-10
Completion date: 2024-06-01
Sponsor: Xuanwu Hospital, Beijing
Collaborators: Beijing Institute of Technology
Conditions: Myocardial Infarction
Locations: Xuanwu Hospital, Capital Medical University
Countries: China
Enrollment: 4000
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
Acute myocardial infarction (AMI) is one of the most important diseases threatening human life. The existing MI prognosis prediction scales mostly predict the incidence of death, recurrent MI and heart failure through 6-8 clinical text indicators, and the data are collected relatively simply. Myocardial remodeling, as an adverse pathological change that can start and continue to progress in the early stage after myocardial infarction, is the main pathological mechanism of heart failure and death. However, there is no quantitative early-warning model of myocardial remodeling, and the clinical guidance of early intervention is lacking.

Our previous study found that cardiac magnetic resonance imaging can accurately quantify the necrotic area and recoverable myocardium in the edematous myocardium after myocardial infarction. In this study, machine learning algorithm, variable convolution network (DCN) and capsule network (capsnet) are used to build a new neural network architecture. Structural feature extraction of multi-modal clinical image data such as MRI and ultrasound is realized. Combined with the established database of 3000 patients with myocardial infarction, the multimodal feature matrix will be constructed, and a variety of classifiers such as support vector machine (SVM) and random forest (RF) will be used for quantitative prediction of myocardial remodeling, and the effects of different classifiers were evaluated. It is expected that this project will establish a quantitative early warning model of myocardial remodeling after acute myocardial infarction in line with the characteristics of Chinese people. The same type of data outside the database will be used for verification to establish an efficient and stable early warning model.

Eligibility criteria:
Inclusion Criteria:

* 1.Patients with acute myocardial infarction, aged 18-80 years; 2.The time from onset to treatment is less than 72h 3.Myocardial enzyme Tni/Tnt(+).

Exclusion Criteria:

* 1.Patients with malignant tumors; 2.Patients who could not receive conventional treatment 3.Patients who did not receive coronary angiography, lacking anatomical and imaging data; 4.Patients who have undergone cardiac surgery (except coronary artery bypass surgery)

Outcomes:
Novel convolutional neural network algorithm and cardiac magnetic resonance imaging to evaluate the occurrence of myocardial remodeling.remodeling after myocardial infarction.
Randomized Controlled Clinical Trial of Internal Fixation of Intertrochanteric Fractures of Femur Guided by Conventional Versus IF-AI Artificial Intelligence Program
Trial ID: NCT06195033
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-01
Completion date: 2024-12
Sponsor: Peking University Third Hospital
Conditions: Hip Fractures
Interventions: Surgical protocol guided by artificial intelligence software
Enrollment: 132
Age range: 60 Years - None
Sex: ALL

Summary:
Our team has developed an AI software to guide the surgical protocol for femoral intertrochanteric fractures. This is a prospective randomized controlled clinical study that will include patients with femoral intertrochanteric fractures combining medial posterior and lateral wall fractures, and will randomly divide the patients into a traditional surgical protocol group and an AI-guided surgical protocol group. The efficacy and safety indexes such as operation time, blood loss, operation cost, infection rate, hospitalization time, postoperative pain score, fracture healing time, internal fixation failure rate and mortality rate were compared between the two groups to verify the efficacy and safety of AI-guided surgical treatment plan.

Eligibility criteria:
Inclusion Criteria:

1. Over 60 years old, male or female.
2. Osteoporosis intertrochanteric fracture, which met the diagnostic criteria of X-ray intertrochanteric fracture, and osteoporosis T-value \<2.5.
3. Patients with 1mm resolution CT imaging data.
4. Willing to participate in the project, sign the informed consent, and be able to complete the follow-up work.

Exclusion Criteria:

1. Patients with pathological fractures, old fractures, stress fractures, and periprosthesis fractures;
2. Patients lacking 1mm resolution CT imaging data;
3. Severe malnutrition, heavy smoking, alcoholism and other systemic conditions affect fracture healing;
4. Patients with severe medical diseases that cannot be operated on;
5. Patients participating in other clinical studies.

Outcomes:
Fracture healing situation 36 weeks after surgery
ARTERY Outcomes: Tailored Drug Titration Through Artificial Intelligence - an Interventional Study
Trial ID: NCT04223934
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-02-18
Completion date: 2023-06-27
Sponsor: Optima Integrated Health
Collaborators: University of California, San Francisco
Conditions: Hypertension
Interventions: optima4BP
Locations: University of California San Francisco
Countries: United States
Enrollment: 164
Age range: 21 Years - 80 Years
Sex: ALL

Summary:
The purpose of the ARTERY Outcomes study is to compare optima-for-blood pressure (optima4BP), a clinical decision support system for hypertension (HTN) treatment optimization to standard of care in patients with essential HTN.

Eligibility criteria:
Inclusion Criteria:

* Two or more blood pressure (BP) readings of ≥ 140/90 mmHg during UCSF primary care or cardiology office visits within the last 12 months
* Therapy with medications from at least 1 anti-HTN pharmacological agents at the time of the last office visit
* At least minimally "tech-savvy" defined as Ownership of a compatible smartphone and Ability to access the internet
* Active Electronic Health Record MyChart account

Exclusion Criteria:

* Home anti-HTN medication therapy doesn't match the electronic health record medication list
* Anti-HTN medication therapy changed within 30 days prior to enrollment
* Inability to operate a BP cuff
* Incompatible smartphone device (Galaxy S5 Android 5.0)
* Less than minimally "tech-savvy" defined as Inability to use the Internet
* Non-compliance with medical follow-up (\>3 "no shows" in the previous 12 months)
* Planned coronary revascularization in the next 12 months
* Myocardial infarction, coronary revascularization, stroke, cardiac or aortic surgery in the previous 90 days
* GFR \< 30 (CKD stage IV/ V)
* Treating physician rules out the patient due to superseding health management concerns
* Treating physician rules out the patient due to other concerns

Outcomes:
Reduction in systolic BP [SBP] (mmHg) measured remotely [Time frame: 0 - 6 months]
Predicting Changes in Core Muscles During Female Sexual Dysfunction: A Comprehensive Analysis Using Machine and Deep Learning
Trial ID: NCT05833685
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-02-01
Completion date: 2023-04-15
Sponsor: Deraya University
Conditions: Female Sexual Dysfunction
Interventions: no intervention
Locations: Deraya university
Countries: Egypt
Enrollment: 100
Age range: 30 Years - 40 Years
Sex: FEMALE

Summary:
The purpose of this study is to Predicting changes in core muscles during female sexual dysfunction by A Comprehensive Analysis Using Machine and Deep Learning Female sexual dysfunction (FSD) is a common condition that affects womenof all ages. It is characterized by a range of symptoms, including decreased libido, difficulty achieving orgasm, and pain during intercourse. One potential cause of FSD is muscular weakness or changes in the core muscles. These muscles play an important role in sexual function, and changes in their strength or activation patterns can lead to FSD. Additionally, the development of a machine learning model for this purpose could pave the way for future studies exploring the use of artificial intelligence in the diagnosis and treatment of other musculoskeletal disorder and female health issues.

Eligibility criteria:
Inclusion Criteria:

* a number of parities ≤ three
* normal vaginal deliveries

Exclusion Criteria:

* History of a recto-vaginal or vesico-vaginal fistula, undiagnosed uterine bleeding urinary tract infection,
* diabetes,
* intrauterine device
* sexual disorder

Outcomes:
Diaphragm excursion
A Prospective, Multicentre, Randomised, Blinded Study of Obstructive Sleep Apnoea Detection Using the Sunrise Solution
Trial ID: NCT05204004
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-08-21
Completion date: 2023-03-01
Sponsor: Royal Brompton & Harefield NHS Foundation Trust
Collaborators: NHS Highlands
Conditions: Obstructive Sleep Apnea
Interventions: Sunrise; Polygraphy
Locations: Raigmore Hospital; Royal Brompton Hospital
Countries: United Kingdom; United Kingdom
Enrollment: 40
Age range: 18 Years - None
Sex: ALL

Summary:
A prospective, randomised, blinded pilot study comparing the Sunrise solution and overnight polygraphy for the diagnosis of OSA in adult patients. Patients under investigation for OSA will use both devices simultaneously for a single overnight sleep study. They will be randomised to receive their treatment decision based on either the Sunrise solution or polygraphy. A retrospective check of their diagnosis will be done.

Eligibility criteria:
Inclusion Criteria:

1. Aged ≥18
2. Ability and willingness to provide informed consent
3. Patient referred for suspected OSA
4. BMI \>28 kg/m2
5. Epworth Sleepiness Score \>12
6. Reported snoring (criterion may be ignored if patient sleeps alone)
7. Patient able to use a smartphone application and having an internet connection at home

Exclusion Criteria:

1. Patient already treated for OSA
2. Patient has PSV or HGV driving license
3. Unstable cardiac disease
4. Supplemental oxygen
5. Known secondary sleep pathology e.g. Periodic Limb Movement Syndrome, Narcolepsy, Circadian Disorder, Obesity Hypoventilation Syndrome
6. Concerns about sleepy driving or any other potentially dangerous symptom from physician or urgent referral from GP when patient has been told to stop driving
7. Pregnant woman, parturient, nursing mother, person deprived of liberty by judicial or administrative decision, person subject to a legal protection measure
8. Patient whose beard is too thick to wear the Sunrise device, and who does not wish to shave
9. Patient with acrylic or painted fingernails, who does not wish to remove acrylic or paint

Outcomes:
Time to treatment decision
Study of Social Cognition Processes With Multimodal MRI on Healthy Adult Patients.
Trial ID: NCT06164964
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2010-04-22
Completion date: 2010-10-28
Sponsor: IRCCS San Raffaele
Conditions: Social Cognition
Interventions: study of higher cognitive functions
Enrollment: 27
Age range: 18 Years - 35 Years
Sex: ALL

Summary:
The study of higher cognitive functions (learning, memory, language, reasoning, decision making) in humans constitutes an interdisciplinary field, which combines neurobiological and neurological, neuropsychological, as well as linguistic and artificial intelligence skills. Historically, the study of patients with cerebrovascular lesions or brain pathologies associated with primary and/or cognitive deficits has produced fundamental knowledge in this area. The development of modern non-invasive investigation techniques of the human brain - such as Structural Magnetic Resonance Imaging (MRI), Diffusion Tensor Imaging, Functional Magnetic Resonance Imaging (fMRI), Positron emission (PET), the study of event-related electrophysiological potentials (ERP) or magneto-encephalography (MEG) - has made it possible to carry out similar studies on healthy subjects, as well as the in-vivo analysis of adaptive and plastic mechanisms of the human brain. Recently, the interest of cognitive neuroscience - this is the name taken by this vast interdisciplinary sector which aims to understand the neural mechanisms underlying human cognitive processes - has been oriented towards the study of decision making in social contexts. Knowledge of the mechanisms underlying these processes is fundamental for the most complete description of the biological bases of human behavior and above all for the optimal development of treatments for brain pathologies, whether surgical, pharmacological or rehabilitation in general.

Eligibility criteria:
Inclusion Criteria:

* Age between 18 and 35 years old; Monolingual native speakers of Italian; Absence of neurological or psychiatric pathologies that could interfere with carrying out the exam.

Exclusion Criteria:

* Clinical and/or anamnestic evidence of significant neuropsychiatric or systemic pathologies; Previous head trauma with loss of consciousness

Outcomes:
behavioral study with neuroanatomical functional correlates consisting of cognitive activation experiments during functional magnetic resonance imaging