moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Application of White Eye Imaging Health Care System on Diagnosis of Malignant Tumors
Trial ID: NCT04224298
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2019-02-01
Completion date: 2022-09-20
Sponsor: Peking University Cancer Hospital & Institute
Conditions: Feature Elongation; Cancer Patients
Locations: Yichen Dr. Xu
Countries: China
Enrollment: 1000
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
Based on the theory of traditional Chinese Medicine, this study collects eye features of white eyes through the White Eye Imaging Health Care System under visible light and establishes a database of eye-eye features corresponding to the disease. The computer image analysis and artificial intelligence technology are used to visualize the eye. The features were extracted and classified, and the corresponding changes in the characteristics of the traditional Chinese medicine and the disease were analyzed statistically to establish an analytical model corresponding to the disease.

Eligibility criteria:
Inclusion Criteria:

* Malignant tumor patients with clear pathological diagnosis;

  * Those who have no serious primary diseases of the cardiovascular, cerebrovascular, respiratory, endocrine, urinary, blood and other systems;

    * The main organs (hematopoiesis, heart, lung, liver, and kidney) have no obvious abnormal function; ④Age ≥18 years old, ≤75 years old; ⑤ No serious eye disease;

      * The patient volunteered to participate in this study.

Exclusion Criteria:

* Patients with severe blood loss (because when the bleeding exceeds 300ml, the white eye color of the patient is often pale white);

  * Poor patient compliance; ③ pregnant and lactating women;

    * Can't cooperate with inspection properly; ⑤ Cannot read or understand the scale.

Outcomes:
Model of white-eye ocular features in patients with malignant tumors
Application of Genomic Techniques and Image Processing Using Artificial Intelligence to Obtain a Predictor Model Risk of Melanoma
Trial ID: NCT02511119
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2015-07
Sponsor: Maimónides Biomedical Research Institute of Córdoba
Collaborators: Mount Sinai Hospital, New York
Conditions: Melanoma
Enrollment: 300
Age range: 18 Years - None
Sex: ALL

Summary:
The aim of this study is to develop a predictive model of risk of developing melanoma. The investigators will used artificial intelligence techniques to analysed images patterns obtained by clinical and dermoscopic pictures. The investigators want to defined a new predictive risk model obtained from genetic information and image analysis of pigmented lesions. This model could help to discriminate more accurately those patients who are most likely to develop melanoma in a high-risk population.

Eligibility criteria:
Inclusion Criteria:

* ≥18 years of age;
* Followed up patients in the Pigmented Lesions Unit at Reina Sofia University Hospital with high risk of developing melanoma (First-degree family/personal history of familiar melanoma and/or melanocyticnevi above 50 and/or dysplastic or atypical nevi);
* All nevi for which there is at least one dermatoscopy registry will be included.

Exclusion Criteria:

* not enough data in the patient clinical history;
* located lesions on the face, scalp, mucous membrane or on acral área;
* nodular or metastatic melanoma diagnosis
* type V or VI of Fitzpatrick phototyping scale

Outcomes:
Number of participants with Melanoma
Study on Treatment Decision-Making and Prognostic Follow-Up for Untreated Cerebral Cavernous Malformations
Trial ID: NCT06214767
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2020-09-01
Completion date: 2026-06-30
Sponsor: Beijing Tiantan Hospital
Collaborators: RenJi Hospital; Beijing Chao Yang Hospital; Beijing Friendship Hospital; Guangzhou Red Cross Hospital; Affiliated Hospital of Guangdong Medical University; Shanxi Provincial People's Hospital; Qilu Hospital of Shandong University; Second Xiangya Hospital of Central South University; First Affiliated Hospital of Harbin Medical University
Conditions: Hemangioma, Cavernous, Central Nervous System
Locations: Capital Medical University Affiliated Beijing Tiantan Hospital
Countries: China
Enrollment: 1200
Age range: None - None
Sex: ALL

Summary:
The goal of this observational study is to evaluate and predict the risk associated with cerebral cavernous malformations (CCMs) using advanced artificial intelligence and radiomics analysis technology. The study focuses on individuals who have been diagnosed with cerebral cavernous malformations (CCMs).

Main Questions to Answer:

How can AI-based radiomics features predict the risk of complications (such as bleeding or epilepsy) in individuals with CCMs? What are the most reliable imaging and clinical markers for assessing the prognosis of CCMs? Participants will be required to undergo regular medical imaging to gather traditional and radiomics imaging features.

Participants will provide clinical data, including past medical history and results of any laboratory tests.

Participants will be part of a three-year follow-up observation to monitor the progression or stability of CCMs.

Contribution of biological samples for advanced testing might also be requested.

This study aims to create an AI-based decision-making tool that will guide clinicians in the management of CCM, with the potential to significantly improve patient outcomes through personalized medical approaches.

Eligibility criteria:
Inclusion Criteria:

1. Diagnosis of CCM based on brain MRI (T1, T2, SWI, and T2-Fluid-Attenuated Inversion Recovery).
2. Patients who have not received invasive treatment (surgery, radiotherapy, or multimodal therapy) in the past.
3. Patients undergoing surgery, or their legal guardians, agree to collect lesion tissue samples for related studies and sign a consent form for the collection of biological samples.
4. Patients under conservative observation, or their legal guardians, agree to collect imaging data for related research and sign a consent form for the use of imaging data.
5. Willingness to participate in long-term follow-up.

Exclusion Criteria:

1. Patients with acute intracranial symptomatic hemorrhage requiring emergency surgery.
2. Patients with other intracranial diseases, such as aneurysms, tumors, or other vascular malformations, excluding developmental venous anomalies (DVA).
3. Patients with severe underlying diseases affecting their functional status and short-term life expectancy.
4. Patients with severe psychiatric or psychological disorders.
5. Incomplete clinical or imaging data.

Outcomes:
Functional Outcome:mRS
An Open-labelled Interventional Study With 25 IBS-M Patients in Which Group 1 (n=14) Followed Six Weeks of AI-based Microbiome Diet and Group 2 (n=11) Followed Standard IBS Diet
Trial ID: NCT04768387
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-10-05
Completion date: 2021-01-15
Sponsor: Gazi University
Collaborators: ENBIOSIS BIOTECHNOLOGIES; TC Erciyes University
Conditions: Irritable Bowel Syndrome Mixed
Interventions: Personalized microbiome diet
Locations: Gazi University
Countries: Turkey (Türkiye)
Enrollment: 25
Age range: 20 Years - 65 Years
Sex: ALL

Summary:
This study was designed as a pilot, open-labelled study. We enrolled consecutive IBS-M patients (n=25, 19 females, 46.06 ± 13.11 years) according to Rome IV criteria. Fecal samples were obtained from all patients twice (pre- and post-intervention) and high-throughput 16S rRNA sequencing was performed. Patients were divided into two groups based on age, gender and microbiome matched.

Six weeks of AI-based microbiome diet (n=14) for group 1 and standard IBS diet (Control group, n=11) for group 2 were followed. AI-based diet was designed based on optimizing a personalized nutritional strategy by an algorithm regarding individual gut microbiome features. An algorithm assessing an IBS index score using microbiome composition attempted to design the optimized diets based on modulating microbiome towards the healthy scores. Baseline and post-intervention IBS-SSS (symptom severity scale) scores and fecal microbiome analyses were compared.

Eligibility criteria:
Inclusion Criteria:

* Diagnosed with IBS by a medical doctor.
* BMI between 18.5-39.9 kg/m2
* No hospitalization in the last 12 months.
* No antibiotics use in the last 6 months.
* No cancer diagnosis by a medical doctor.
* No chronic complex diseases including diabetes and hypertension.

Exclusion Criteria:

* Not being diagnosed with IBS.
* Having a diagnosed chronic disease.
* Having a diagnosed mental or psychiatric disorder .
* Having endocrinal disorders.
* Being pregnant.
* Antibiotics use in the last 6 months.
* Hospitalization history in the last 12 months.
* Drug use.
* Being morbid obese.

Outcomes:
IBS-SSS change
Acquisition and Analysis of Medullary Trauma Patients Signs, With an Expert Model
Trial ID: NCT02833428
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2018-01-20
Completion date: 2023-12-20
Sponsor: Fondation Hôpital Saint-Joseph
Conditions: Spinal Cord Injuries
Interventions: Biomedical equipment (Eclipse Nim, Medtronic®)
Locations: Groupe Hospitalier Paris Saint Joseph
Countries: France
Enrollment: 1
Age range: 18 Years - None
Sex: ALL

Summary:
The aim of this study was to analyze using an artificial intelligence engine (IA) the influence of the pathophysiological environment (set parametric monitoring data, imaging, biology etc.) of acute spinal cord trauma on spinal pain. This project seeks to establish the principles of a new approach for studying spinal cord injury patients. It does not meet the usual criteria of clinical trials in so far as it does not test on patients the effect of a therapeutic

Eligibility criteria:
Inclusion Criteria:

* Patient with acute spinal cord injury between the cord opposite the vertebra C2 and marrow compared to the T12 vertebra.
* neurological lesion classified A, B, C or D according to Frankel classification at the initial examination (Annex 1).
* Patient able to give informed consent

Exclusion Criteria:

There are no exclusion criteria. All patients meeting the inclusion criteria are includable. However, it differs remarkable different situations:

* The clinical neurological assessment of patients may be difficult depending on the context: intubated patients admitted - ventilated multiple trauma.
* Neurological assessment via evoked potentials may be difficult depending on the environment for patient care: need for multiple investigations, resuscitation. Evoked potentials will be installed only after agreement of the healthcare team once the patient is stabilized.
* The patient can refuse to submit to one additional exam is not part of the standard care (i.e. PES / PEM); it will be offered to participate in the study without the completion of this review. It will be in this case a simple use of clinical and laboratory data collected retrospectively anonymized so for scientific purposes.

Outcomes:
Assessment of change of ASIA impairment scale (American Spinal Cord Injury Association)
Improving Stroke Care in North-Norway Through Artificial Intelligence
Trial ID: NCT05903898
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-06-01
Completion date: 2030-12-31
Sponsor: University Hospital of North Norway
Collaborators: Nordlandssykehuset HF; Helgeland Hospital Trust; Finnmarkssykehuset HF; Oslo University Hospital
Conditions: Ischemic Stroke
Interventions: AI software StrokeSens (Circle NVI)
Locations: University Hospital of North Norway
Countries: Norway
Enrollment: 1000
Age range: 18 Years - None
Sex: ALL

Summary:
Prospective observational multi-center intervention study. The study aims to evaluate whether an artificial intelligence (AI) support tool for radiological image processing (StrokeSens, Circle NVI) can accelerate decision making and increase detection rate in patients with an acute ischemic stroke caused by intracranial large vessel occlusion (LVO) or medium vessel occlusion (MeVO) in Northern Norway. Relevant outcomes will be compared between centres with and without available software during the study period.

Eligibility criteria:
Inclusion Criteria:

* Patients with acute stroke symptoms admitted to a participating hospital within 24 hours of symptom onset

Exclusion Criteria:

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

Outcomes:
Time interval from imaging completion to when a request for patient transfer is recorded
Precision Health in Autism: Exploring the Gut-Brain-Behavior Axis With Biomarkers, Probiotic Efficacy, and Artificial Intelligence Algorithms
Trial ID: NCT06677840
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-05-01
Completion date: 2028-04-30
Sponsor: National Taiwan University Hospital
Conditions: Autism Spectrum Disorder
Interventions: probiotic (GKB7/placebo)
Locations: National Taiwan University Hospital
Countries: Taiwan
Enrollment: 110
Age range: 4 Years - 15 Years
Sex: ALL

Summary:
This groundbreaking human study on the ASD microbiome includes probiotic clinical trials, investigation of treatment biomarkers, machine learning/deep learning platform development for ASD classification and prediction, and identification of diagnostic biomarkers. Upon completion, the investigators anticipate publishing at least 12 SCI papers and/or patents and establishing an auxiliary diagnosis platform for both clinical and academic purposes. The findings will offer new insights into the pathogenetic mechanisms, improving early detection, diagnosis, and treatment, ultimately advancing precision medicine for ASD.

Eligibility criteria:
Inclusion Criteria:

* Children and adolescents aged 4 to 15 who are clinically diagnosed with ASD according to DSM-5 criteria and confirmed by the ADI-R/ADOS.
* Caregivers cooperate with all the assessments and stool and blood collection.

Exclusion Criteria:

* A history of major psychiatric disorders (e.g., schizophrenia, bipolar disorder, major depression), neurological disorders, and substances use disorders.
* Difficulty following instructions.
* Consumption of antibiotics and yogurt or probiotic products two weeks prior to enrollment.

Outcomes:
Clinical Global Impression; Social Responsiveness Scale (SRS)
Study to Assess Artificial Intelligence-Assisted Insulin Titration System on Inpatients Glucose Control
Trial ID: NCT04517201
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-11-04
Completion date: 2021-12
Sponsor: Shanghai Zhongshan Hospital
Conditions: Type 2 Diabetes
Interventions: iNCDSS based insulin regime; Physician based insulin regime
Locations: Zhongshan Hospital; Zhongshan Hospital, Fudan University
Countries: China; China
Enrollment: 120
Age range: 18 Years - 99 Years
Sex: ALL

Summary:
This is a multi-center, open-labeled, parallel group, randomized controlled trial to access the effect and safety of the Artificial Intelligence Assisted Insulin Titration System (iNCDSS) in patients with Type 2 Diabetes Mellitus.

Eligibility criteria:
Inclusion Criteria:

* Men or women aged 18-99 years old;
* Inpatients who had been diagnosed with type 2 diabetes;
* Subjects who are on treatment with insulin for at least 3 months;
* HbA1c: 7.0%-10.0%.

Exclusion Criteria:

* Patients who were diagnosed with T1D, gestational diabetes or other specific types of diabetes.
* Subjects with acute complications of diabetes such as ketoacidosis or hyperglycemic hyperosmolar state;
* Patients with severe cardiac, hepatic, renal or general diseases;
* Subjects that are, in the judgement of the investigator, unlikely to comply with the protocol.
* Absence of informed consent.

Outcomes:
Fasting plasma glucose of T2D patients
A Deep-Learning-Enabled Electrocardiogram for Detecting Pulmonary Hypertension: A Randomized Controlled Trial
Trial ID: NCT07079592
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-02
Completion date: 2026-06-15
Sponsor: National Defense Medical Center, Taiwan
Conditions: Artificial Intelligence (AI); Artificial Intelligence (AI) in Diagnosis; Hypertension, Pulmonary
Interventions: AI-ECG Guidance
Locations: National Defense Medical Center
Countries: Taiwan
Enrollment: 8666
Age range: 50 Years - 85 Years
Sex: ALL

Summary:
This study aims to validate the use of an artificial intelligence-enabled electrocardiogram (AI-ECG) to screen for elevated PAP. We hypothesize that the AI-ECG model can early identify patients with pulmonary hypertension in high-risk patients, prompting further evaluation through echocardiography, potentially resulting in improving cardiovascular outcomes.

Eligibility criteria:
Inclusion Criteria:

* Men or women, ≥ 50 to 85 years of age
* At least one 12-lead ECG within 3 months

Exclusion Criteria:

* A diagnosis of PH WHO Groups 1, 2, 3, 4, or 5
* A diagnosis of hypertrophic cardiomyopathy, restrictive cardiomyopathy, constrictive pericarditis, cardiac amyloidosis, or infiltrative cardiomyopathy
* Prior heart, lung, or heart-lung transplants
* Any systolic pulmonary artery pressure \>50 mmHg by echocardiography before
* Echocardiography in 3 months before index ECG

Outcomes:
Pulmonary arterial pressure > 50 mmHg
Protocollo Sulla Valutazione Del RischiO cardioVAscolare Nel Crohn e ColIte Ulcerosa (PROVACI). Estimation of Cardiovascular Risk in Crohn's Disease and Ulcerative Colitis
Trial ID: NCT07095634
Status: ENROLLING_BY_INVITATION
Study type: OBSERVATIONAL
Start date: 2025-02-07
Completion date: 2027-06
Sponsor: IRCCS Policlinico S. Donato
Conditions: Ulcerative Colitis; Myocardial Infarction; Stroke; Ischemic Heart Disease; Atrial Fibrillation (AF); Thromboembolism; Crohn Disease; Transient Ischemic Attack (TIA); Heart Failure
Locations: IRCCS San Donato POliclinic
Countries: Italy
Enrollment: 600
Age range: 16 Years - None
Sex: ALL

Summary:
Inflammatory bowel diseases (IBD) are progressively increasing their prevalence worldwide. Up to half of patients may also have extraintestinal manifestation and cardiovascular diseases (CVDs) is one of the most relevant, being the second cause of death in these patients. The classical cardiovascular (CV) risk scores do not adquately capture the CV risk in IBD.

We are planning a prospective observational study to evaluate patients with IBD and a gender/age-matched cohort with the aims to investigate in 2-years follow-up

* Occurrence of major cardiovascular events (MACEs) in both cohorts;
* Predictive factors of risk for MACEs in IBD compared to controls;
* Multi-omics evaluations with the support of artificial intelligence of biomarkers associated with occurrence of MACEs in IBD

Eligibility criteria:
Inclusion Criteria:

Patients with confrimed diagnosis of Crohn's disease and Ulcerative colitis-

Exclusion Criteria:

No willing to sign the informe content and have 2 years of follow-up

Outcomes:
Occurrence of Major cardiovascular events
Research on New Intelligent Diagnosis and Treatment Technologies for Early Lung Cancer Based on Multimodal Imaging Bronchoscopy Navigation
Trial ID: NCT07000721
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-08-01
Completion date: 2024-12-31
Sponsor: Jisong Zhang
Conditions: Lung Cancer, Peripheral Pulmonary Nodules
Interventions: Preoperative navigation path planning using the SARS-pro navigation system; Preoperative navigation path planning using the VBN navigation system
Locations: Sir Run Run Shaw Hospital, Zhejiang University School of Medicine
Countries: China
Enrollment: 92
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
To verify the clinical effectiveness and safety of the airway tree navigation system constructed by artificial intelligence (AI) in the navigation diagnosis of peripheral pulmonary nodules (PPLs).

Eligibility criteria:
Inclusion Criteria:

1. Aged 18 years or above.
2. Patients with one or more peripheral lung nodules suspected to be lung cancer or poorly absorbing lesions on conventional anti-infective therapy.
3. Patients with nodule diameters ≤30 mm (diameters mentioned in the text are the average of the maximum and minimum diameters).
4. The nodules were pure ground glass nodules, partially solid nodules, or solid nodules.
5. The nodule is surrounded by lung parenchyma and is not visible in the bronchial lumen above the segment.

Exclusion Criteria:

1. Preoperative judgment that it is difficult for the patient to benefit from bronchoscopic biopsy (e.g., high risk of bleeding due to perivascular encasement of the lesion, difficulty in reaching the airway adjacent to the lesion due to previous lung surgery, etc.).
2. Those with incomplete clinical data.
3. Those with missing visits after biopsy.

Outcomes:
Diagnostic positive yield
Assessment of the Breast Cosmesis Using Deep Neural Networks: an Exploratory Study (ABCD)
Trial ID: NCT05450016
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2021-10-04
Completion date: 2026-09
Sponsor: Tata Memorial Centre
Conditions: Breast Cancer
Locations: Tata Memorial Centre
Countries: India
Enrollment: 720
Age range: 19 Years - 80 Years
Sex: FEMALE

Summary:
Surgery and radiotherapy in breast cancer patients can cause treatment changes and may affect the final breast appearance. In this study, we are trying to evaluate the post treatment breast photographs of the patients and subject these to Artificial Intelligence based program so as to classify into appropriate categories based upon changes from baseline. This automated solution will help in decreasing the time required to achieve this task by physicians in the clinic.

Eligibility criteria:
Inclusion Criteria:

* Confirmed diagnosis of primary breast cancer (invasive or in situ)
* Patient undergone breast conservation / Whole breast reconstruction
* Patient received breast RT
* Already provided written informed consent on earlier projects
* Patient provided photographs of both breasts
* Non-metastatic disease or oligometastatic
* Age \> 18 years
* Reconsent given

Exclusion Criteria:

* Mastectomy without whole breast reconstruction
* Bilateral breast cancer
* Partial breast irradiation
* Male patient
* Limited life expectancy due to co-morbidity
* Patients undergoing brachy boost

Outcomes:
Proportion of patients with excellent/good cosmesis