moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Scan-AId: Artificial Intelligence Support in Novice's Decision-making for Assessing Ultrasound Fetal Weight Estimation - A Randomized Trial
Trial ID: NCT06232187
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-02-14
Completion date: 2024-09-01
Sponsor: Copenhagen Academy for Medical Education and Simulation
Collaborators: Slagelse Hospital; Technical University of Denmark
Conditions: Fetal Weight; Ultrasound
Interventions: Artificial Intelligence feedback for ultrasound EFW standard plane images
Locations: Rigshospitalet
Countries: Denmark
Enrollment: 75
Age range: 18 Years - None
Sex: ALL

Summary:
The SCAN-AID study is a prospective, randomized, controlled, and unblinded study that compares the performance of novices in ultrasound fetal weight estimation. The study evaluates the impact of two levels of AI support: a straightforward black box AI and a more detailed explainable AI.

Eligibility criteria:
Ultrasound novice participants:

Inclusion Criteria:

* Medical students with no former fetal or abdominal ultrasound training.
* The participants will have to understand spoken and written Danish or English.

Exclusion Criteria:

• Medical students who received formal fetal or abdominal training prior to the inclusion in this study.

Pregnant women;

Inclusion Criteria:

* The participants will have to understand spoken and written Danish or English.
* BMI \< 30
* Gestational age: 28-42

Exclusion Criteria:

* Age \> 40 years
* Fefal anomaly
* Oligohydramnion
* Gestational Diabetes, Diabetes type 1 or 2.

Outcomes:
Diagnostic accuracy
Prospective Evaluation of Image-based Artificial Intelligence Research and Development Tool for Precision Neoadjuvant Triple-Negative Breast Cancer Treatment
Trial ID: NCT05435352
Status: TERMINATED
Study type: OBSERVATIONAL
Start date: 2022-06-06
Completion date: 2025-07-25
Sponsor: Ourotech, Inc.
Collaborators: Barts & The London NHS Trust
Conditions: Triple Negative Breast Cancer; Triple Negative Breast Neoplasms
Interventions: Core needle biopsy
Locations: Barts Health NHS Trust; The Royal Marsden NHS Foundation Trust; Manchester University NHS
Countries: United Kingdom; United Kingdom; United Kingdom
Enrollment: 34
Age range: 18 Years - None
Sex: ALL

Summary:
Pear Bio has developed an organ-on-a-chip device together with a computer vision pipeline through which the response of an individual patient's tumor to different chemotherapy regimens can be tested simultaneously ex vivo. This study will recruit patients with early TNBC who are planned for standard of care neoadjuvant chemotherapy followed by surgery. The oncologist will be blinded to the response on the Pear Bio tool (the assay will be run in parallel with the patient's neoadjuvant chemotherapy). The primary objective of this study is to establish the sensitivity and specificity of Pear Bio's test against patient outcomes (pathological complete response).

Eligibility criteria:
Inclusion Criteria:

* Able to give written informed consent prior to admission to this study.
* Female or male aged ≥18 years.
* Histologically confirmed invasive primary breast cancer which is triple-negative by the most recent ASCO/College of American Pathologists (CAP) guidelines.
* Stage I-III breast cancer planned for neoadjuvant chemotherapy followed by surgery.
* Primary breast tumor size ≥10 mm. For patients with bilateral tumors both of the breast tumors have to be TNBC and at least one has to be ≥10 mm.
* Willing to undergo a mandatory additional core needle biopsy from the primary breast mass prior to starting neoadjuvant chemotherapy. Patients with bilateral breast cancer only need to have one tumor biopsied if both tumors are ≥10 mm.
* Willing to donate 40mL of whole blood (cohort B only)

Exclusion Criteria:

* Inflammatory breast cancer.
* Inoperable or metastatic TNBC.
* Patients who have already commenced neoadjuvant chemotherapy.
* Treatment concurrently or within 4 weeks of commencing neoadjuvant chemotherapy with any experimental therapies. Patients who are due to receive standard of care neoadjuvant chemotherapy on the control arm of a trial may be eligible after discussion with the medical monitor.
* Secretory or adenoid cystic histological subtypes of triple-negative breast cancer.
* Any other disease, metabolic dysfunction, physical examination finding, or clinical laboratory finding that, in the investigator's opinion, gives reasonable suspicion of a disease or condition that may affect the interpretation of the results, render the patient at high risk from treatment complications or interferes with obtaining informed consent.

Outcomes:
Pathological complete response correlation accuracy (specificity)
Retrospective Multicenter Multi-Reader, Multi-Case Diagnostic Accuracy Study of Carebot AI MMG Compared With Radiologists on 2D Full-Field Digital Mammography in Breast Cancer Screening
Trial ID: NCT07298252
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2025-01-01
Completion date: 2025-11-03
Sponsor: Carebot s.r.o.
Conditions: Breast Neoplasms; Breast Cancer Detection; Breast Cancer - Female; Breast Cancer Screening
Interventions: Carebot AI MMG software analysis
Locations: Poliklinika MEDICON Budějovická; Dolnooravská nemocnica s poliklinikou MUDr. L. N. Jégého; Nemocnica s poliklinikou Považská Bystrica; Ľubovnianska nemocnica
Countries: Czechia; Slovakia; Slovakia; Slovakia
Enrollment: 222
Age range: 18 Years - None
Sex: FEMALE

Summary:
This study evaluates the diagnostic performance of Carebot AI MMG, an artificial intelligence (AI)-enabled medical device for evaluating mammograms. The software analyzes standard full-field digital mammography (FFDM) images and classifies each examination as having no suspicious finding ("Low Risk"), a probably benign mass ("Medium Risk"), or a suspicious malignant mass ("High Risk").

The study is retrospective and observational. It uses anonymized mammography examinations from four screening centers, without any additional imaging or contact with patients. Three experienced breast radiologists independently read the same set of cases, and their assessments are used as the human benchmark. A histopathology-based reference standard, supplemented by radiologist consensus and follow-up information for negative cases, is used to determine whether cancer is present.

The main goal is to compare the AI system with human radiologists in terms of sensitivity and specificity for detecting breast cancer, and to assess whether the AI can achieve non-inferior performance at two predefined operating points: one favoring higher sensitivity and negative predictive value (rule-out) and one favoring higher specificity and positive predictive value (rule-in).

Eligibility criteria:
Inclusion Criteria:

* Female sex
* Age ≥ 18 years at the time of the screening mammogram
* Screening full-field digital mammography (FFDM) examination with all four standard views (LCC, RCC, LMLO, RMLO) available
* Sufficient image quality and complete DICOM metadata to allow retrospective analysis

Exclusion Criteria:

* Male sex
* Age \< 18 years
* Digital breast tomosynthesis (DBT/3D) examinations without a corresponding full 2D FFDM four-view series
* Incomplete mammography series (missing one or more of LCC, RCC, LMLO, RMLO)
* Corrupted or unreadable DICOM files
* Missing or inconsistent key metadata (e.g., laterality, view, acquisition date)

Outcomes:
Balanced accuracy (BA) of Carebot AI MMG for detecting malignant versus non-malignant examinations; Sensitivity (Se) of Carebot AI MMG versus histopathology-based reference standard
Optimal Oral Care Regimen With or Without Professional Mechanical Plaque Removal in Managing Gingivitis (Gingival Inflammation): A Non-inferiority Randomized Controlled Trial
Trial ID: NCT07549347
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-03-09
Completion date: 2027-03-01
Sponsor: Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University
Conditions: Gingivitis; Periodontitis
Interventions: Optimal oral care regimen; Periodontal Treatment; Sham Periodontal Treatment
Locations: Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine
Countries: China
Enrollment: 92
Age range: 18 Years - 64 Years
Sex: ALL

Summary:
Periodontitis is highly prevalent and develops from plaque-induced gingivitis. Managing gingivitis is key to preventing periodontitis and its complications. Professional mechanical plaque removal (PMPR) with oral hygiene guidance is effective for gingivitis, but large-scale implementation-especially in China-faces challenges such as workforce shortages. Meanwhile, patient compliance with daily oral hygiene remains poor. An optimal oral care regimen featuring an intelligent electric toothbrush (i-Brush) has shown promise in enhancing self-care adherence and efficiency. However, it remains unclear whether PMPR is still necessary when used in conjunction with this optimal oral care regimen. This study aims to verify whether the i-Brush-based regimen is non-inferior to the combination of PMPR and the regimen in improving gingival inflammation in gingivitis and stage I periodontitis.

Eligibility criteria:
Inclusion Criteria:

1. ≧18 years old and \< 65 years old
2. Gingivitis, Stage I and Localized Stage II Periodontitis
3. With more than 20 teeth
4. Agree to use the electronic I-brush with the Oral-B APP

Exclusion Criteria:

1. Presence of any systemic disease that can alter the outcome of periodontal treatment
2. Pregnancy or lactation during the study
3. Having received antibiotics within the previous 3 months
4. Having received professional periodontal treatment within the previous 3 months
5. With ongoing orthodontic treatment

Outcomes:
changes in full mouth bleeding score (FMBS)
Evaluation of the Effectiveness of Deep Learning Model in Detection and Classification of Pressure Injury
Trial ID: NCT06641258
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-01-27
Completion date: 2022-06-01
Sponsor: University of Beykent
Conditions: Knowledge; Satisfaction
Interventions: Control Group (Standard Procedure); Experimental Group
Locations: Beykent University
Countries: Turkey (Türkiye)
Enrollment: 60
Age range: 18 Years - 35 Years
Sex: ALL

Summary:
In the health care system, pressure injuries, which are among the quality indicators, are a serious patient safety problem that affects the length of hospital stay and the cost of care. Pressure injuries are generally defined as localized injuries caused by pressure on bony prominences or by shear force combined with pressure. This health problem reduces the quality of life of the patient and their family, causes the individual to be socially isolated , requires more intensive and prolonged nursing care, and can cause mortality , morbidity and nosocomial infections if appropriate treatment and care are not provided .

systematic staging of pressure injuries positively directs the treatment process and the patient's prognosis . Correct staging of pressure injuries not only affects patient care outcomes but also increases the quality of nursing care provided by providing a common language among nurses.Today, with the increasing use of technology, it is seen that larger data is needed to solve complex problems. In order to meet this need, Convolutional Neural Networks have emerged, which are used in many areas such as object recognition, speech recognition, and natural language processing, and can automatically learn from the symbols of data belonging to images, videos, audio, and texts, instead of learning with coded rules, unlike traditional machine learning methods, based on Artificial Neural Networks. Convolutional Neural Networks are one of the Deep Learning methods, which is a sub-branch of machine learning methods and has the ability to learn from examples. Convolutional Neural Networks are methods that can also learn from raw image or text data and whose prediction accuracy increases according to the size of the data. It has been proven in the literature that artificial intelligence and deep learning models are effective in the risk analysis of pressure injuries. However , no study has been found on the classification of pressure injuries. In light of this information, the study was conducted to develop a deep learning model in the detection and classification of pressure injuries and to determine the effect of the model on the knowledge and satisfaction levels of nurses.

Eligibility criteria:
Inclusion Criteria:

The nurse must;

* Be over 18 years of age
* Work as an intensive care or clinical nurse
* Agree to participate in the research verbally and in writing.

Exclusion Criteria:

The nurse;

* Being under the age of 18
* Working in a place other than intensive care and clinic (e.g. blood collection unit, laboratory, etc.)
* Not accepting to participate in the research verbally or in writing.

Outcomes:
Knowledge levels
Clinical Study on an Artificial Intelligence-Assisted Chest Radiograph Model Based on Big Data and Deep Learning for Early Detection of Kawasaki Disease
Trial ID: NCT07405658
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-02-01
Completion date: 2027-12-31
Sponsor: Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
Collaborators: Children's Hospital of Soochow University; Hunan Provincial People's Hospital; Women and Children Hospital of Qinghai Province; Yangzhou No.1 People's Hospital
Conditions: Kawasaki Disease; Chest X-ray for Clinical Evaluation; Mucocutaneous Lymph Node Syndrome
Interventions: AI-Based Early Warning System for Kawasaki Disease
Locations: Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine
Countries: China
Enrollment: 20000
Age range: 0 Years - 18 Years
Sex: ALL

Summary:
The goal of this observational study is to develop an AI-based early warning system for Kawasaki Disease (KD) using chest X-rays (CXR) in children diagnosed with Kawasaki Disease. The main question\[s\] it aims to answer are:

1. Can AI modeling of CXR features help identify high-risk KD patients earlier than current diagnostic methods?
2. Can the AI system predict the optimal IVIG treatment window and coronary artery risks in KD patients?

Participants will:

Provide retrospective data on chest X-rays and clinical data (CRP, coronary ultrasound, etc.) Allow analysis of CXR features using deep learning models to extract relevant patterns Have their data incorporated into a federated learning model to ensure privacy and data security

Eligibility criteria:
Inclusion Criteria:

1. Case group

   * The age of seeking medical treatment is less than or equal to 18 years old; ·The medical record system diagnosis contains the diagnosis of "Kawasaki Disease", "mucocutaneous lymph node syndrome" or "IVIG non-response Kawasaki disease"
   * At least one complete chest X-ray examination data (images and reports) is available during the same hospitalization
2. Control group

   * The age of seeking medical treatment is less than or equal to 18 years old
   * The same period as the case group
   * Fever lasts for 3 days or more
   * Rule out the possibility of diagnosing Kawasaki disease

Exclusion Criteria:

1. Case group

   * Chest X-ray quality issues: Severe artifacts, overexposure/underexposure leading to inability to assess key structures
   * Incomplete clinical information, including lack of chest X-ray examination, laboratory tests, and unclear days of fever Inability to determine the final diagnosis (such as loss to follow-up, diagnosis in doubt)
2. Control group

   * Chest X-ray quality issues: Severe artifacts, overexposure/underexposure leading to inability to assess key structures
   * Incomplete clinical information, including lack of chest X-ray examination, laboratory tests, and unclear days of fever
   * Inability to make a clear final diagnosis (such as loss to follow-up, questionable diagnosis)

Outcomes:
Area Under Curve; sensitivity; specificity
Identification of Pre-Malignant Lesions In Pediatric Patients With Neurofibromatosis Type 1 Using Novel Magnetic Resonance Imaging Techniques Paired With Artificial Intelligence
Trial ID: NCT04763109
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-07-08
Completion date: 2029-05
Sponsor: Nicole Baca
Conditions: Neurofibromatosis Type 1
Interventions: Whole-body Magnetic Resonance Imaging
Locations: Cedars Sinai Medical Center
Countries: United States
Enrollment: 15
Age range: 5 Years - 17 Years
Sex: ALL

Summary:
This is a single arm pilot trial of a novel whole-body Magnetic Resonance Imaging paired with artificial intelligence intervention, to evaluate feasibility defined as scan-rescan reliability, and to estimate the positive predictive value of changes in Magnetic Resonance Imaging scans from baseline to 12-month visit using an Artificial Intelligence algorithm, among 15 pediatric patients with neurofibromatosis type 1 at Cedars-Sinai Medical Center.

Eligibility criteria:
Inclusion Criteria:

* Between the ages of 5 and \<18 years at the start of study. If subject will turn 18 during the study, they will be allowed to enroll.
* Clinically or molecularly confirmed diagnosis of NF-1. Subjects with mosaic/segmental NF-1 also qualify for the study.

Exclusion Criteria:

* Requiring sedation for imaging.
* Implants and/or Devices: Mechanical, magnetic or electrical activated implants; Ferromagnetic implants and foreign bodies
* Claustrophobia, problems being in enclosed spaces, or inability to lie facing upwards.
* Allergy to animal dander or animal-instigated asthma.

Outcomes:
For scan re-scan reliability, utilize T2 imaging to evaluate the agreement between the baseline scan and the 1 Month scan, which is performed within 4 weeks of the baseline scan.
Using an AI-based Voice Assistant to Manage Insulin in Diabetes: a Randomized-Control Trial
Trial ID: NCT05081011
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-03-24
Completion date: 2022-12-01
Sponsor: Stanford University
Conditions: Medication Adherence
Interventions: Voice Assistant Device; Voice Assistant Device
Locations: Stanford University
Countries: United States
Enrollment: 39
Age range: 18 Years - None
Sex: ALL

Summary:
This study randomizes participants to have their basal insulin titrated either through standard of care or by receiving prompts through interactions with an AI-enabled smart speaker device. The primary objective of this study is to investigate the feasibility of an AI-enabled smart speaker device and whether such a device facilitates insulin titration management, increases insulin adherence and decreases time to optimal insulin dose. The secondary objective of the study is to explore whether the device improves glycemic control as defined by improvements in fasting blood sugar.

Eligibility criteria:
Inclusion Criteria:

* Patients with Type 2 Diabetes
* Patients clinically indicated to be taking daily long-acting insulin
* Patients currently taking long-acting insulin but necessitating active dose adjustments

Exclusion Criteria:

* Patients who do not speak English
* Patients who do not own a smart phone
* Patients who do not have stable wireless internet connection at home

Outcomes:
Time to Optimal Insulin Dose; Insulin Medication Adherence; Attitudes Toward Diabetes; Attitudes Toward Health Technology; Attitudes Toward Medication Adherence
Detection of Atrial Fibrillation Through Voice Signal Processing
Trial ID: NCT07212816
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-10-15
Completion date: 2025-11-30
Sponsor: Vital Audio Inc
Collaborators: Minneapolis Heart Institute Foundation
Conditions: Atrial Fibrillation (AF); Atrial Flutter
Interventions: Sauti
Enrollment: 40
Age range: 18 Years - None
Sex: ALL

Summary:
This study is testing a new technology that listens to a person's voice during short phone calls to help identify heart rhythm problems. The goal is to see if voice signal analysis can tell the difference between a normal rhythm (sinus rhythm) and an irregular rhythm (atrial fibrillation).

We are enrolling adults scheduled for a standard heart procedure called cardioversion, which is used to reset the heart to a normal rhythm. Each participant will provide voice recordings through three short phone calls: before cardioversion, just after the procedure, and at a follow-up visit. Results will be compared with standard heart rhythm tests (electrocardiograms or ECGs).

This research may lead to a simple, low-cost way to monitor heart rhythms remotely without requiring special devices.

Eligibility criteria:
Inclusion Criteria:

To participate in this study, patients must meet all of the following inclusion criteria:

1. Diagnosis of atrial fibrillation and presenting for cardioversion at Allina Health in atrial fibrillation
2. 3\. ≥ 18 years of age Written informed consent obtained from patient in English

Exclusion Criteria:

Patients who meet any of the following criteria must be excluded from this study:

1. History of a pacemaker
2. History of dysphasia or difficulty with word finding
3. History of vocal cord dysfunction
4. Patient with atrial flutter as presenting arrhythmia
5. Planned transesophageal echocardiogram (TEE) with cardioversion

Outcomes:
Accuracy of voice-based rhythm classification vs ECG; Accuracy of voice-based rhythm classification vs ECG
Exploration of Novel AI-enabled Blue Light Enhanced Cystoscopy
Trial ID: NCT07144319
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-03-25
Completion date: 2027-12
Sponsor: Photocure
Conditions: Bladder Cancer; Non-muscle Invasive Bladder Cancer
Locations: Rutgers Cancer Institute; Oslo University Hospital
Countries: United States; Norway
Enrollment: 500
Age range: 18 Years - None
Sex: ALL

Summary:
Blue light cystoscopy (BLC) is a diagnostic procedure in bladder cancer where the inside of the bladder is observed with a camera to detect bladder lesions. Unlike regular white light cystoscopy, blue light cystoscopy makes use of a drug that induces fluorescence under blue light preferentially in neoplastic and malignant cells that helps visualize bladder lesions during the cystoscopic procedure. Blue light cystoscopy has shown to improve detection of bladder cancer.

Cystoscopy, including blue light cystoscopy, is a procedure involving assessment of the visual appearance of the bladder surface, leading to decisions of taking biopsies, remove suspicious areas and assign treatment options. The assessment is subjective and has a large operator variability. These shortcomings show an opportunity for computer aided detection (CADe) medical device to add value to both clinicians and patients.

The objective of this data collection study is to build a high-quality, diverse data set of video, image recordings and relevant clinical data from BLC procedures performed as part of routine clinical practice to train a computer-aided detection (CADe) algorithm for real- time lesion detection during cystoscopy. The data will be used to support the training, non-clinical technical development and testing of such AI algorithms for use during cystoscopy and to provide documentation needed for training of such algorithms and to assist in guiding future validation of such algorithms.

Exploratory purposes of the study is to use data to explore future AI algorithms in bladder cancer, such as computer-aided diagnosis (CADx) AI algorithms, image enhancement and cystoscopy improvement algorithms, including bladder mapping, tumor visualization, cystoscopy documentation, and combination models of image and clinical data including risk assessment, clinical outcomes, and disease modeling

Eligibility criteria:
Inclusion Criteria:

* Age 18 or older
* Written informed consent, approved by relevant IRB/IEC, signed
* Hexvix/Cysview has been prescribed in the usual manner in accordance with the terms of the marketing authorization (see Appendix B)
* Physician has planned to do a blue light cystoscopy on the patient and to obtain biopsies, if clinically indicated, of suspicious lesions with video confirmation.
* Patient has not previously taken part in this study

Exclusion Criteria:

* None

Outcomes:
Video and image collection