moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Development of an Artificial Intelligence Platform / Digital Dental Hygienist to Improve Compliance in Periodontal Maintenance Patients
Trial ID: NCT05068778
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-09-10
Completion date: 2023-06-01
Sponsor: McGuire Institute
Collaborators: Asua, Inc
Conditions: Periodontal Diseases
Interventions: Artificial Intelligence Application
Locations: Metro West Orthodontics & Periodontics; Perio Health Professionals
Countries: United States; United States
Enrollment: 50
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
An Artificial Intelligence platform to develop an app which improves compliance in periodontal treatments

Eligibility criteria:
Inclusion Criteria:

1. Subjects 18 to 75-yrs of age
2. Subjects with diagnosis and treatment of Generalized and/or Localized Periodontitis Stages I, II, or III; grades A-C and or Peri-implantitis (Generalized Mild, Moderate, or Advanced Chronic Periodontitis) who are following a periodontal maintenance interval of every three months.
3. Subjects are able to provide self-care (oral hygiene) without disabilities requiring assistance with daily oral hygiene measures.
4. Subjects will have read, understood and signed an institutional review board (IRB) approved Informed Consent Form (ICF).
5. Subjects must be able and willing to follow study procedures and instructions.

Exclusion Criteria:

1. Subjects who do not own or are incapable of interacting with a smart phone or device
2. Subjects who are incapable of operating an application on a smart phone or device
3. Subjects who are not compliant with periodontal maintenance / recall intervals (Based on Wilson's classification as any lack of compliance with their recommended maintenance interval- therefore exclude subjects who demonstrate erratic compliance or noncompliance)18
4. Subjects who require active periodontal therapy during the study
5. Subjects who are on immunosuppressive medications, chemotherapy, or radiation therapy which would adversely affect their periodontal stability.
6. Subjects participating in other clinical studies involving therapeutic intervention (either medical or dental).
7. Subjects, who in the opinion of the investigator, for any reason other than those listed above, will not be able to complete the study per protocol.

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   Exclusion Criteria:

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Outcomes:
To identify the input variables the AI utilizes to arrive at CS assignment and message selection for subject interaction via DDH app
Network and Contingency Management for Alcohol Treatment
Trial ID: NCT00845208
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2002-03
Completion date: 2008-02
Sponsor: UConn Health
Collaborators: National Institute on Alcohol Abuse and Alcoholism (NIAAA)
Conditions: Alcohol Dependence
Interventions: Network Support
Locations: University of Connecticut health Center
Countries: United States
Enrollment: 210
Age range: 18 Years - None
Sex: ALL

Summary:
Relapse is the most serious problem in alcoholism treatment. The overall aim of the present study was to determine if a treatment directed at changing the patient's social network, from one that reinforces drinking behavior to one that reinforces sobriety, can create the conditions necessary for long-term treatment success. In addition, we intended to determine if explicit reinforcement for this change of social network (Contingency Management or ContM) would be more effective than the same network support intervention without contingent reinforcement for change.

Eligibility criteria:
Inclusion Criteria:

* meet DSM-IV criteria for Alcohol Dependence
* at least 18 years old
* Willing to accept random assignment to treatment conditions

Exclusion Criteria:

* acute medical or psychiatric problems that require inpatient treatment
* reading ability below the fifth grade level
* lack of reliable transportation to the treatment site
* excessive commuting distance to site
* current dependence on drugs (except marijuana)

Outcomes:
Proportion Days Abstinent
Assessing the Efficacy of ChatGPT in Detecting Surgical Site Infections Following Elective Colorectal Surgery
Trial ID: NCT06556017
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-08-15
Completion date: 2024-09-01
Sponsor: Hospital de Granollers
Conditions: Surgical Site Infection
Interventions: Diagnosis of SSI
Locations: Hospital General de Granollers
Countries: Spain
Enrollment: 122
Age range: 18 Years - None
Sex: ALL

Summary:
Epidemiological surveillance is one of the eight core components of the World Health Organization Infection Prevention and Control Programmes. These include surveillance programmes for surgical site infection (SSI).

At present, for SSI surveillance, infection control teams perform a manual time-consuming work, which could make a transition to automated surveillance leveraging the new information technology.

This study aimed to evaluate the ability of ChatGPT-4o to detect surgical site infection at the three anatomical levels.

Eligibility criteria:
Inclusion Criteria:

* Elective colorectal resection

Exclusion Criteria:

* Emergency surgery
* Infection present at operation
* Previous intestinal stoma

Outcomes:
Rate of surgical site infection
Visual Field Analysis With Artificial Intelligence: Establishing Normal Threshold Values for Healthy Participants Using a Visual Field Perimeter
Trial ID: NCT06920290
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-07-01
Completion date: 2026-08-31
Sponsor: Sight Intelligence Engineering Corporation
Conditions: Glaucoma; Visual Field Defect
Interventions: Visual Field Perimeter Optimized with Eye Gaze Capture and Artificial Intelligence
Locations: Sight Intelligence Engineering
Countries: United States
Enrollment: 100
Age range: 18 Years - 100 Years
Sex: ALL

Summary:
The purpose of this study is to analyze visual field data from healthy participants. This data will be used to develop and optimize a new visual field perimeter test. The test will incorporate an advanced algorithm, which may include the use of Artificial Intelligence (AI), to enhance accuracy and efficiency in identifying test points.

Eligibility criteria:
Inclusion Criteria:

* Healthy adults aged 18 years or older.
* Best corrected visual acuity of 20/40 or better in each eye.
* Normal optic nerve appearance upon clinical examination.

Exclusion Criteria:

* Best corrected visual acuity worse than 20/40.
* Cup-to-disc ratio greater than 0.5.
* Previously documented glaucoma or optic neuropathy.
* Any significant ocular or neurological condition that may impact visual field.
* Inability to comply with study procedures.

Outcomes:
Brightness Threshold; Duration Threshold
Artificial Intelligence-guided Prognostication and Cranial Radiotherapy Optimization in First-line Third-generation EGFR-TKI-treated EGFR-mutant Non-small Cell Lung Cancer With Baseline Brain Metastases: a Multicenter, Observational Study
Trial ID: NCT06604689
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-09-30
Completion date: 2025-10-01
Sponsor: Fudan University
Conditions: NSCLC (Advanced Non-small Cell Lung Cancer); Brain Metastasases
Interventions: third-generation EGFR TKIs (Almonertinib/Furmonertinib/Osimertinib)
Locations: Shanghai Cancer center
Countries: China
Enrollment: 800
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this observational study is to extract the imaging features of brain lesions and primary lung lesions in NSCLC patients with brain metastases by deep learning, as well as common clinicopathological parameters, which are used to construct a multimode model that can accurately predict the treatment efficacy and survival of the third-generation EGFR-TKI treatment, and to use the model to assist in screening high-risk populations suitable for upfront cranial radiotherapy.

Participants receiving third-generation EGFR-TKI treatment will be enrolled in our study and we will collect their regular contrast-enhanced chest CT and contrast-enhanced brain MRI for model construction.

Eligibility criteria:
Inclusion Criteria:

* Pathologically confirmed non-small cell lung cancer;
* clinical stage IV (AJCC, 8th edition, 2017);
* EGFR sensitive mutations: EGFR L858R, EGFR exon 19 deletion;
* age≥18 years old;
* KPS score≥70;
* brain metastases at diagnosis;
* complete systemic imaging (including brain MRI) before third-generation EGFR-TKI treatment;
* received standard third-generation EGFR-TKI therapy (monotherapy or combined with brain radiotherapy);
* willing to cooperate with the follow-up after third-generation EGFR-TKI treatment;
* informed consent of the patient.

Exclusion Criteria:

* Multiple primary or metastatic tumors (except early skin cancer, cervical carcinoma in situ that has been treated radically, with no recurrence or progression for more than 5 years);
* Pregnant or lactating women who, as judged by the investigator, were not candidates for brain MRI;
* EGFR sensitive mutations were negative or EGFR mutation status was not detected.
* Uncontrolled epilepsy, central nervous system disease, or history of mental disorders, judged by the researcher to potentially interfere with the signing of the informed consent form or affect patient compliance.

Outcomes:
Progression-Free Survival (PFS)
THE EFFECT OF CHATBOT USE ON NURSING STUDENTS' CONCEPT MAP CONSTRUCTION SKILLS: A RANDOMIZED CONTROLLED STUDY
Trial ID: NCT07310940
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-03-19
Completion date: 2025-04-14
Sponsor: Dokuz Eylul University
Conditions: to Examine the Effect of Chatbot Use on Nursing Students
Interventions: Chatbot-assisted concept mapping; Traditional concept mapping -Control Group
Locations: Dokuz Eylul University,
Countries: Turkey (Türkiye)
Enrollment: 108
Age range: 18 Years - None
Sex: ALL

Summary:
Concept maps are visual tools that help students organize information and understand how ideas are related to each other. Although concept mapping is widely used in nursing education, many nursing students find it difficult to create effective concept maps. Recently, artificial intelligence tools such as ChatGPT have begun to be used in education and may help support students during learning activities. However, it is not yet clear whether using chatbots improves students' ability to create concept maps. This study aimed to examine whether using a chatbot (ChatGPT) helps nursing students develop better concept map construction skills. The study was conducted as a randomized controlled trial with second-year nursing students taking a surgical nursing course during the 2024-2025 spring semester. A total of 108 students were randomly assigned to either an intervention group or a control group. All students received the same theoretical education about postoperative care of cardiac surgery patients from the same instructor. Students in the intervention group received brief training on how to use ChatGPT and were allowed to use the chatbot while creating case-based concept maps. Students in the control group created concept maps using only textbooks and course materials. Students' knowledge, concept map quality, and attitudes toward concept mapping were evaluated. The results showed no significant difference between the two groups in terms of postoperative care knowledge or overall concept map scores. However, students who used ChatGPT had more positive attitudes toward concept mapping. Students also reported that the chatbot helped them understand complex information more easily and supported them during the concept map creation process.In conclusion, using a chatbot did not improve concept map performance scores, but it had a positive effect on students' attitudes and learning experiences. Chatbot-assisted learning may be a useful supportive tool in nursing education. Further studies with larger groups of students and longer follow-up periods are needed to better understand its effects on learning outcomes.

Eligibility criteria:
Inclusion Criteria:

* -Taking the surgical nursing course for the first time

Exclusion Criteria:

* None

Outcomes:
Concept Map Construction Skill
Development and Validation of a Computer-aided Algorithm Using Artificial Intelligence and Deep Neural Networks for the Segmentation of Ultrasonographic Features of Lymph Nodes During Endobronchial Ultrasound
Trial ID: NCT03849040
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2019-04-08
Completion date: 2019-11-20
Sponsor: St. Joseph's Healthcare Hamilton
Conditions: Lung Diseases; Lung Neoplasm
Interventions: Endobronchial Ultrasound
Locations: St. Joseph's Healthcare Hamilton
Countries: Canada
Enrollment: 52
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to determine if a deep neural artificial intelligence (AI) network (NeuralSeg) can learn how to assign the Canada Lymph Node Score to lymph nodes examined by endobronchial ultrasound transbronchial needle aspiration(EBUS-TBNA), using the technique of segmentation. Images will be created from 300 lymph nodes videos from a prospective library and will be used as a derivation set to develop the algorithm. An additional100 lymph node images will be prospectively collected to validate if NeuralSeg can correctly apply the score.

Eligibility criteria:
Inclusion Criteria:

* must be diagnosed with confirmed or suspected lung cancer and be undergoing EBUS diagnosis/staging

Exclusion Criteria:

* None

Outcomes:
Development of computer algorithm to identify lymph node ultrasonographic features; Validation of computer algorithm to identify lymph node ultrasonographic features
Evaluation of Ultralow Dose PET Imaging for Detecting SSTR2 Radiotracer Uptake
Trial ID: NCT07284589
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: PHASE2
Start date: 2025-12-11
Completion date: 2030-04-30
Sponsor: Akiva Mintz
Conditions: Healthy (Controls); Healthy Volunteers; Neuroendocrine (NE) Tumors
Interventions: 68Ga-Dotatate
Locations: Nuclear Imaging Institute
Countries: United States
Enrollment: 200
Age range: 18 Years - 120 Years
Sex: ALL

Summary:
The goal of this clinical trial is to evaluate an investigational ultralow dose positron emission tomography (PET) imaging technique for neuroendocrine tumor detection and monitoring. The main question it aims to answer is:

Can the investigators optimize the timing, scan duration, and image reconstruction to reduce the radiation dose 10-100 fold of the current clinical standard? Participants will be injected with a radioactive tracer that binds to a tumor specific protein called somatostatin receptor 2 (SSTR2) and be imaged on a new type of high sensitivity PET scanner for up to 3 hours

Eligibility criteria:
Inclusion Criteria:

* Age ≥18 years.
* Ability to provide informed consent and comply with study procedures.
* For female participants: Must not be pregnant or breastfeeding; Negative pregnancy test required for women of childbearing potential.

Exclusion Criteria:

* Participants who have exceeded NRC regulation for annual radiation exposure from prior research-related scans, including this study (50 millisievert \[mSv\] total).
* More than four prior enrollments in this study.
* Participants with severe claustrophobia, chronic pain, or musculoskeletal conditions that prevent completion of the PET scan
* Medication \& Prior Treatment Exclusions: SSTR targeted therapies
* Pregnant or breastfeeding individuals (negative pregnancy test required)
* Inability to provide informed consent
* Any condition that, in the investigator's judgment, may compromise participant safety or study integrity.

Outcomes:
Signal-to-Noise Ratio (SNR); Contrast-to-Noise Ratio (CNR); Coefficient of Variation (COV); Spatial Resolution (Full Width at Half Maximum - FWHM); Target-to-Background Ratio (TBR); Qualitative Image Quality Score (Likert Scale); Inter-reader Agreement (Weighted Kappa Statistics)
A Growth Artificial Intelligence Algorithm for leNgth and Weight Study
Trial ID: NCT06920550
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-12-20
Completion date: 2026-03-01
Sponsor: Nutricia Research
Conditions: Length; Weight
Locations: Franciscus Gasthuis; Ginemedica; Hospital Universitario Puerta del Mar; Hospital Universitario de Jerez dela Frontera
Countries: Netherlands; Poland; Spain; Spain
Enrollment: 250
Age range: 0 Months - 24 Months
Sex: ALL

Summary:
This is a data collection and machine learning accuracy testing project that aims to a) collect training data to enhance, by machine learning, an artificial intelligence (AI) algorithm for measuring length in infants and young children and b) test the accuracy of the AI algorithm by comparing the AI predicted length with the gold standard measured length. Images and videos will be collected by care givers and healthcare professionals, together with physical length measurements. These data will be used to train the AI algorithm and to explore potential improvements. Other data to be collected is user experience feedback.

Eligibility criteria:
Inclusion Criteria:

1. Infants/young children aged 0 to 24 months old at enrolment;
2. Caregiver(s) have access to the internet and a smartphone or tablet to complete questionnaires, take images and videos, and upload these;
3. Written informed consent from one or both caregivers (according to local laws) or legally acceptable representative(s) aged ≥ 18 years at enrolment.

Exclusion Criteria:

1. Children unable to undergo length or weight measurements when applying standardized techniques recommended by the WHO (e.g. infants/children with structural abnormalities of the lower limbs or orthopaedic conditions (e.g. club foot, hip dysplasia)).
2. Research staff's uncertainty about the caregiver's ability or willingness to complete the study according to the instructions

Outcomes:
Accuracy length AI; Accuracy caregiver length; Accuracy caregiver vs AI length
Application and Validation of a Smartphone-based Deep Learning System for Oral Potentially Malignant Disorders (OPMD) and Oral Cancer Screening
Trial ID: NCT06862414
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-03
Completion date: 2025-12
Sponsor: National Taiwan University Hospital
Conditions: Cancer Screening; Oral Cancer; Oral Potentially Malignant Disorders
Interventions: Smartphone-based deep learning system
Locations: Department of Family Medicine, National Taiwan University Hospital
Countries: Taiwan
Enrollment: 954
Age range: 19 Years - None
Sex: ALL

Summary:
The goal of this clinical trial is to learn if smartphone-based deep learning system works to accurately detect oral potentially malignant disorders and oral cancer in adults. It will also learn about if it is as effective as assessments conducted by dentists and non-certified health provider.

We expect that the deep learning system will have higher sensitivity in detecting oral potentially malignant disorders and oral cancer, where as the dentists and non-certified health providers will exhibit higher specificity in screening.

Participants will be grouped into three arms: deep learning system (arm A) or board-certified dentist with deep learning system (arm B) or non-certified health providers (general practitioners) with deep learning system (arm C).

Oral cancer risk factors, such as habits of smoking or having chewed betel nut or alcohol drinking, would be recorded by anonymous questionnaires.

Eligibility criteria:
Inclusion Criteria:

* Adult patients (age ≥18) visiting cancer screening center

Exclusion Criteria:

* Unable to cooperate to fully open mouth/ navigate tongue
* Unable to cooperate for the assessment

Outcomes:
Effectiveness and accuracy
Evaluation of the Trustworthiness of the Application of Artificial Intelligence and Decision Support Systems for the Creation of Tumor Conference Protocols
Trial ID: NCT05681949
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-11
Completion date: 2025-07
Sponsor: Charite University, Berlin, Germany
Collaborators: German Research Center for Artificial Intelligence
Conditions: Hepatocellular Carcinoma; Fibrolamellar Hepatocellular Carcinoma; Cholangiocarcinoma, Perihilar; Intrahepatic Cholangiocarcinoma; Metastasis to Liver; Gallbladder Carcinoma
Interventions: ADBoard
Locations: Surgical Clinic, Campus Virchow-Klinikum / Campus Charité Mitte, Charité - Universitätsmedizin Berlin
Countries: Germany
Enrollment: 1200
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this observational study is to compare the recommendations of the artificial intelligence clinical decision support system 'ADBoard', with the recommendations of physicians by tumor conferences in patients with hepatobiliary tumors. The main questions it aims to answer are:

Can ADBoard achieve a high level of similar recommendations as physicians' tumor conferences? Can ADBoard consider a more complete set of patient-related data than in physicians' tumor conferences? Can ADBoard reduce the time between the first time the patient is discussed at the tumor conference and the start of the recommended treatment plan? Participants will have their hepatobiliary tumor treatments determined by either tumor conference with ADBoard, or tumor conference without ADBoard.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18 years
* Valid informed consent
* Patient information available in the hospital information system or Health Data Platform (HDP)
* Enrollment in the hepatobiliary tumor conference
* Diagnosis of any of the following entities: Colorectal liver metastasis, Gallbladder carcinoma, Hepatocellular carcinoma (HCC), mixed cell carcinoma, or fibrolamellar carcinoma, Perihilar cholangiocarcinoma (Klatskin tumors), Intrahepatic cholangiocarcinoma (iCC)

Exclusion Criteria:

* Patient does not consent / incapable of giving consent
* Missing findings in the hospital information system
* Patient is seeking for a second opinion and is not being treated at the study institution

Outcomes:
Concordance (yes/no) of interdisciplinary tumor conference treatment recommendation with ADBoard-supported recommendation; The reproducibility of the therapy recommendations made by ADBoard (yes/no)
Implementing a Corrective Annotation No Code Artificial Intelligence-based Software to Detect Several Radiographic Features Associated With Unsatisfactory Endodontic Treatment: A Randomized Controlled Trial
Trial ID: NCT06450938
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-07-30
Completion date: 2024-12-13
Sponsor: University of Copenhagen
Collaborators: Queen Mary University of London
Conditions: Endodontically Treated Teeth; Endodontic Underfill; Endodontic Overfill; Apical Periodontitis
Interventions: AI guidance for finding radiographic features
Enrollment: 80
Age range: 20 Years - 40 Years
Sex: ALL

Summary:
Developing neural network-based models for image analysis can be time-consuming, requiring dataset design and model training. No-code AI platforms allow users to annotate object features without coding. Corrective annotation, a "human-in-the-loop" approach, refines AI segmentations iteratively. Dentistry has seen success with no-code AI for segmenting dental restorations. This study aims to assess radiographic features related to root canal treatment quality using a "human-in-the-loop" approach.

Eligibility criteria:
Inclusion Criteria:

1.Being a last year dental student at the university of Copenhagen

Exclusion Criteria:

1. Having any previous AI-related experiences
2. Not accepting to sign the informed consent

Outcomes:
Accuracy; Sensitivity; Specificity