moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Construction of an Artificially Intelligent Model for Accurate Detection of HCC by Integrating Clinical, Radiological, and Peripheral Immunological Features
Trial ID: NCT06637059
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-01-01
Completion date: 2024-10-01
Sponsor: Zhejiang University
Conditions: Hepatocellular Carcinoma (HCC)
Interventions: observational study
Locations: [None]
Countries: China
Enrollment: 1092
Age range: None - None
Sex: ALL

Summary:
Purpose: Integrating comprehensive information on hepatocellular carcinoma (HCC) is essential to improve its early detection. The investigators aimed to develop a model with multi-modal features (MMF) using artificial intelligence (AI) approaches to enhance the performance of HCC detection.

Experimental Design: A total of 1,092 participants were enrolled from 16 centers. These participants were allocated into the training, internal validation, and external validation cohorts. Peripheral blood specimens were collected prospectively and subjected to mass cytometry analysis. Clinical and radiological data were obtained from electrical medical records. Various AI methods were employed to identify pertinent features and construct single-modal models with optimal performance. The XGBoost algorithm was utilized to amalgamate these models, integrating multi-modal information and facilitating the development of a fusion model. Model evaluation and interpretability were demonstrated using the SHapley Additive exPlanations method.

Eligibility criteria:
Inclusion Criteria:

* Benign liver diseases, including but not limited to, hemangiomas, hepatic cysts, focal nodular hyperplasia, and cirrhosis

Exclusion Criteria:

* Participants who had undergone previous treatment for HCC or benign liver diseases,
* had taken medications affecting the hematological system within 2 weeks
* those who had received a blood transfusion within 6 months

Outcomes:
Diagnosis of liver disease through CT imaging
Assessment of Artificial Intelligence-based Anatomy Identification Software (Nerveblox) Designed for Ultrasound-guided Peripheral Nerve and Plane Block Scanning Procedures
Trial ID: NCT05807737
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-10-15
Completion date: 2020-11-15
Sponsor: Smart Alfa Teknoloji San. ve Tic. A.S.
Collaborators: Gazi University
Conditions: Ultrasound Imaging
Interventions: Ultrasound scanning with artificial intelligence
Locations: Gazi University School of Medicine
Countries: Turkey (Türkiye)
Enrollment: 40
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this observational study is to assess the accuracy of Artificial Intelligence (AI) software to assist ultrasound scanning prior to peripheral nerve blocks. 40 healthy volunteers are going to be accepted to this study in which ultrasound scanning will be performed for four different block types.

The main questions it aims to answer are:

* Can Nerveblox, the artificial intelligence software highlight and find the key anatomical landmarks on the provided nerve or plane block correctly?
* Can the user obtain a correct view of provided nerve or plane block, when the AI software scan success reaches 100%?

Participants will be evaluated under four nerve block regions to identify their key anatomical landmarks using ultrasound-guided artificial intelligence software.

Three residents with different levels of Ultrasound-guided Regional Anesthesia(UGRA) experience but eligible to perform UGRA techniques will collect the ultrasound images when the artificial intelligence software scan success fed by ultrasound reached 100%. After collecting US images, each pair of US images(highlighted and raw ) will be evaluated by 2 experts for the accuracy of AI assistance, independently and blindly.

Eligibility criteria:
Inclusion Criteria:

* Volunteers over the age of 18
* Able to accept and sign the Informed Consent Form before participating in the study

Exclusion Criteria:

* Volunteers below the age of 18
* Unwilling to accept or having psychiatric/neurological diseases to sign an Informed Consent Form before participating in the study
* Inability to lie flat
* Anatomical deformity in the selected regions

Outcomes:
Accuracy of AI to identify key anatomical landmarks on real-time ultrasound image prior to ultrasound-guided peripheral nerve and plane blocks
Real-World Evaluation of Eko Algorithms in a Point of Care Setting
Trial ID: NCT05459545
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-04-15
Completion date: 2023-06-30
Sponsor: Eko Devices, Inc.
Conditions: Murmur, Heart; Innocent Murmurs; Heart Murmurs; Pathologic Murmur; Atrial Fibrillation
Interventions: Use of Eko CORE and Eko CORE 500 electronic stethoscope
Locations: Pentucket Medical Associates; Pentucket Medical Associates; Maria Medical Center; Edgewater Medical Center; Hometown Medical PLLC
Countries: United States; United States; United States; United States; United States
Enrollment: 371
Age range: 65 Years - None
Sex: ALL

Summary:
The purpose of this research is to prospectively test and validate the utility of Eko artificial intelligence (AI) plus Eko Murmur Analysis Software (EMAS) murmur characterization in algorithm in a real world, point-of-care setting.

Eligibility criteria:
Inclusion Criteria:

* Patient consents to participation
* Willing to have heart sounds recorded with an electronic stethoscope
* Willing to undergo echocardiography
* Willing to undergo a 12-lead electrocardiogram
* Adults aged 65 years and older
* History of at least one of the following: hypertension, BMI ≥ 30, diabetes mellitus, hyperlipidemia, atrial fibrillation, myocardial infarction, stroke/TIA, previous coronary surgery, or previous coronary angiography
* No prior diagnosis of valve disease or heart murmur

Exclusion Criteria:

* Patient is unwilling or unable to give written informed consent
* Patients experiencing a known or suspected acute cardiac event
* Under the age of 65 years old
* Prior diagnosis of valve disease or heart murmurs

Outcomes:
Sensitivity and Specificity of Eko's AI relative to ground truth; Sensitivity and Specificity of Eko's AI relative to PCP auscultation and ground truth; Positive and negative predictive values for AI detecting new VHD; Positive and negative predictive values for PCP detecting new VHD
A Skin Image Reference Tool to Aid Healthcare Providers' Diagnosis of Commonly Encountered Dermatologic Diseases
Trial ID: NCT07033169
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-09-15
Completion date: 2027-12
Sponsor: Wake Forest University Health Sciences
Collaborators: BelleTorus Corporation
Conditions: Dermatologic Disease
Locations: Wake Forest University Health Sciences Department of Dermatology
Countries: United States
Enrollment: 400
Age range: 10 Years - None
Sex: ALL

Summary:
Consented patients will have three images taken of their dermatologic conditions within the Belle.ai software. These images will be uploaded and saved within the Belle software system where a single AI-generated differential list will be generated based on the three photos. All photos uploaded will be de-identified. The software will not have any unique identifiers of participants saved in the system. The photos will be named based on participant enrollment numbers or unique code numbers and no unique identifiers will be attached to the photos. There will be no data collection form necessary for this study

Eligibility criteria:
Inclusion Criteria:

* Patient must present to an Advocate Health dermatology clinic
* Patient must have the ability and willingness to provide informed consent and comply with study procedures and visits
* Participant dermatologists must have access to the required technology (e.g., smartphone with internet access) and be capable of using it for the required image capture

Exclusion Criteria:

* Patients who are unable to comply with study procedures due to physical or mental health limitations (as assessed by study coordinator)
* Pediatric, adolescent, and teen patients who present with dermatological conditions on their genitalia will not be included in the study (in support of patient privacy concerns).

Outcomes:
concordance of Belle.ai diagnoses with physician diagnoses.
Automatic Segmentation of Mediastinal Lymph Nodes and Blood Vessels in Endobronchial Ultrasound (EBUS) Images Using a Deep Neural Network
Trial ID: NCT05739331
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-05-01
Completion date: 2027-12-01
Sponsor: Norwegian University of Science and Technology
Collaborators: Helse Nord-Trøndelag HF; SINTEF Health Research
Conditions: Artificial Intelligence; Endobronchial Ultrasound; Lung Cancer
Interventions: machine learning algorithm
Locations: Department of Pulmonology, Levanger Hospital, North Trøndelag Hospital Trust; Department of Thoracic Medicine, St Olavs Hospital
Countries: Norway; Norway
Enrollment: 50
Age range: 18 Years - None
Sex: ALL

Summary:
To evaluate the usefulness of Deep neural network (DNN) in the evaluation of mediastinal and hilar lymph nodes with Endobronchial ultrasound (EBUS). The study will explore the feasibility of DNN to identify lymph nodes and blood vessel examined with EBUS.

Eligibility criteria:
Inclusion Criteria:

* Subjects referred to thoracic department in any of the participating hospitals with undiagnosed enlarged mediastinal and hilar lymph nodes.
* Subjects have to be ≥ 18 years of age

Exclusion Criteria:

* Pregnancy
* Any patient that the Investigator feels is not appropriate for this study for any reason.

Outcomes:
Capability
Computer-aided Radiology for Cancer Detection and Therapy Stratification - Benign or Malignant Ovarian Tumors.
Trial ID: NCT05174377
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-04-05
Completion date: 2025-08-01
Sponsor: Gynaecologisch Oncologisch Centrum Zuid
Collaborators: The Netherlands Cancer Institute; Eindhoven University of Technology; Amsterdam UMC, location VUmc; Leiden University Medical Center; Amphia Hospital
Conditions: Ovarian Cancer
Interventions: CT-scan algorithm
Locations: Catharina hospital; Netherlands Cancer Institute; Leiden University Medical Center; Amsterdam medical center; Amphia hospital
Countries: Netherlands; Netherlands; Netherlands; Netherlands; Netherlands
Enrollment: 600
Age range: 18 Years - None
Sex: FEMALE

Summary:
In women with an ovarian tumor, it is often unclear whether the tumor is benign or malignant. To differentiate, tumor markers (CA125 and CEA), a transvaginal ultrasound and, depending on the ultrasound image and the CA125 concentration, a CT scan are performed. The quality of radiological imaging in diagnosing abdominal pathology is often not accurate enough, making additional interventions no-dig for proper classification and interpretation of the tumor.

Objective: To improve accuracy for distinguishing benign from malignant disease in patients presenting with an ovarian mass by using a computer aided detection algorithm.

Eligibility criteria:
Inclusion Criteria:

* patients with an ovarian tumor of which it is unknown whether it is benign or malignant (Risk of Malignancy Index (RMI) \>200)
* underwent surgery
* histological proof of tumor

Exclusion Criteria:

* indefinite pathology report
* lack of correct description of staging in OR report when applicable

Outcomes:
Sensitivity and specificity of CADx algorithm
A Project to Enhance Dermatology E-consultations in Primary Care Centers Using Artificial Intelligence Tools.
Trial ID: NCT07429123
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-06-24
Completion date: 2024-01-10
Sponsor: AI Labs Group S.L
Collaborators: Fundación para la Investigación Biomédica, Hospital Universitario Puerta de Hierro; Puerta de Hierro University Hospital
Conditions: Skin Condition; Skin Abnormalities; Malignancy; Melanoma (Skin Cancer); Skin Cancer
Interventions: Primary care practitioners aided by Legit.Health Plus
Locations: Puerta de Hierro Majadahonda University Hospital
Countries: Spain
Enrollment: 131
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this observational study is to learn if an artificial intelligence (AI) tool helps primary care practitioners better identify skin conditions. The study focuses on adults with suspected skin pathologies, including tumor, inflammatory, and infectious diseases.

The main questions it aims to answer are:

* Does using the AI tool help doctors make more accurate diagnoses for multiple skin conditions?
* Does the tool help doctors better decide which patients need a referral to a dermatologist and which can be managed in primary care?
* Are doctors satisfied with how well the tool works and how easy it is to use in their daily work?
* Can the tool help doctors more accurately differentiate between benign lesions and skin cancer?

Participants will:

* Visit their primary care doctor for a regular skin checkup.
* Have photos taken of their skin condition using a smartphone or a dermatoscope.
* Provide informed consent for their photos and basic health information (such as age and sex) to be analyzed by the AI tool.
* Receive standard care from their doctor, with the tool providing a second opinion to assist in the clinical decision-making process.

Eligibility criteria:
Inclusion Criteria:

* Tumor pathology:
* Benign:
* Histiocytoma
* Seborrheic keratosis
* Angiomas
* Precancerous:
* Actinic keratosis
* Suspected malignancy:
* Basal cell carcinoma
* Squamous cell carcinoma
* Pigmented lesions:
* Melanocytic nevus
* Malignant melanoma
* Inflammatory pathology:
* Psoriasis
* Atopic dermatitis
* Urticaria
* Hidradenitis suppurativa
* Lichen planus
* Infectious pathology:
* Viral warts
* Molluscs
* Herpes simplex
* Patients aged 18 years or older.
* Patients who have signed the informed consent for the study.

Exclusion Criteria:

* Patients under 18 years of age.
* Pregnant patients.
* Patients who, in the opinion of the researcher, will not comply with the study procedures.

Outcomes:
Referral appropriateness