moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Diagnostic Accuracy of Artificial Intelligence, CBCT, and Clinical Examination in Detecting Number of Root Canals in Conventional and Retreated Maxillary and Mandibular Molars
Trial ID: NCT06712160
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-01-20
Completion date: 2024-02-20
Sponsor: Misr International University
Conditions: Number of Root Canals
Interventions: Artificial Intelligence
Locations: Misr International University
Countries: Egypt
Enrollment: 212
Age range: 18 Years - 40 Years
Sex: ALL

Summary:
The study compares the effectiveness of Artificial Intelligence (AI), CBCT, and clinical examination in detecting root canals in upper first, upper second, and lower first molars. Results show AI detects more molars with three or four canals in conventional treatment cases and retreatment cases.

Eligibility criteria:
Inclusion Criteria:

* Male and female patients who were capable of providing informed consent
* Age between 18 to 40 years old.
* A restorable tooth.

Exclusion Criteria:

* Patients that underwent vital pulp therapies.
* Patients with calcifications in pulp space.
* Open apex/immature roots.
* Teeth restored by full coverage crowns.
* Pregnant women by taking adequate history from patient and pregnancy test that was done in the first visit

Outcomes:
The number of canals detected
Application of Artificial Intelligence and Iron Metabolism Markers in Predicting ICU Outcomes for Critically Ill Cancer Patients
Trial ID: NCT07408661
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2015-01-01
Completion date: 2025-12-01
Sponsor: Tongji University
Conditions: Cancer
Enrollment: 1137
Age range: 18 Years - 100 Years
Sex: ALL

Summary:
This study aimed to develop a more accurate way to predict the 30-day survival of cancer patients admitted to the intensive care unit (ICU). The researchers focused on markers of iron metabolism, as imbalances in iron are common in cancer and severe illness.

The study analyzed data from 1,137 critically ill cancer patients. Using artificial intelligence (AI), specifically a model called TabPFN, the study combined these iron markers with other routine clinical data (like blood cell counts and lactate levels) to create a new prediction tool.

Eligibility criteria:
Inclusion Criteria:

1. Adult patients (age ≥ 18 years).
2. Diagnosis of any type of cancer, as recorded in the hospital database.
3. First ICU admission during the hospital stay (only the first ICU stay is considered for patients with multiple admissions).

Exclusion Criteria:

1. Length of ICU stay less than 24 hours.
2. Missing or unavailable data for the key study variables, specifically iron metabolism markers (ferritin, serum iron, total iron-binding capacity) or essential clinical parameters needed for analysis.

Outcomes:
All-cause Mortality at 30 Days
Self-monitoring of Hearing Aids Using Artificial Intelligence
Trial ID: NCT07255586
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-07-01
Completion date: 2028-08-01
Sponsor: Sodra Alvsborgs Hospital
Conditions: Hearing Loss
Interventions: Self-monitoring of hearing aids using artificial intelligence; Traditional hearing care at our clinics.
Enrollment: 120
Age range: 18 Years - None
Sex: ALL

Summary:
This project investigates how self-monitoring of hearing aids through an AI-based mobile app can complement physical and digital healthcare visits. The aim is to increase patient involvement in rehabilitation, enable hearing aid adjustments in everyday sound environments, and improve access to hearing care.

Eligibility criteria:
Inclusion Criteria:

* Individuals who are eligible for a new or renewed hearing rehabilitation process at four of the Hearing organizations audiology clinics

Exclusion Criteria:

* Not fullfilling the inclusion criteria

Outcomes:
Hearing Handicap Inventory for the Elderly-Screening
Application of the Artificial Intelligence-Based Gene Mutation Prediction Tool DeepGEM in Patients With Non-Small Cell Lung Cancer (NSCLC): A Prospective, Multicenter, Randomized Controlled Trial
Trial ID: NCT07110259
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-07-31
Completion date: 2028-07-31
Sponsor: Jianxing He
Collaborators: Guangzhou Kingmed Diagnostics Co., Ltd.
Conditions: Non Small Cell Lung Caner
Interventions: DeepGEM-guided Molecular Testing and Treatment; Standard Diagnostic Pathway
Enrollment: 950
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
This prospective, multicenter, randomized controlled trial aims to evaluate the clinical utility of DeepGEM, an artificial intelligence (AI)-based mutation prediction tool based on histopathological whole-slide images, in patients with non-small cell lung cancer (NSCLC). The study will assess whether DeepGEM can facilitate molecular testing, increase targeted therapy utilization, and improve survival outcomes in a real-world clinical setting. Patients with stage II-IV treatment-naïve NSCLC and qualified pathology slides for DeepGEM analysis will be enrolled. Eligible participants with AI-predicted EGFR, ALK, or ROS1 mutations will be randomized in a 4:1 ratio to either the DeepGEM-informed group (clinicians can access AI results to guide further testing and treatment) or the standard care group (clinicians are blinded to AI results and follow routine care).

Eligibility criteria:
Inclusion Criteria:

* Age between 18 and 75 years, inclusive, at the time of enrollment.
* Histologically or cytologically confirmed non-small cell lung cancer (NSCLC) with clinical stage II-IV as per the 8th edition of the AJCC staging system.
* Availability of qualified histopathological whole-slide images that can be reviewed through the KindMED system(DeepGEM).
* Successful mutation prediction of EGFR, ALK, or ROS1 by the DeepGEM AI tool.
* No prior systemic anti-cancer therapy, including chemotherapy, targeted therapy, or immunotherapy.
* Willing and able to comply with study requirements, including follow-up and treatment; written informed consent must be provided.

Exclusion Criteria:

* Prior systemic anti-tumor therapy (chemotherapy, radiotherapy, targeted therapy-including but not limited to monoclonal antibodies or tyrosine kinase inhibitors) before enrollment.
* Failure of DeepGEM analysis or unqualified histopathological image quality.
* History of any other malignancy within the past 5 years, except for adequately treated basal cell carcinoma of the skin or in situ carcinoma (e.g., cervical carcinoma in situ).
* Cognitive or psychological barriers to understanding or accepting AI-based prediction or molecular testing.
* Pregnant or breastfeeding women, or women of childbearing potential who are not using effective contraception.
* Any other clinical condition that, in the opinion of the investigators, may interfere with the study protocol or compromise participant safety, including poor compliance with study procedures.

Outcomes:
Overall Survival (OS); Targeted Therapy Utilization Rate
Rebooting Infant Pain Assessment: Using Machine Learning to Exponentially Improve Neonatal Intensive Care Unit Practice
Trial ID: NCT05579496
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2020-11-01
Completion date: 2026-12
Sponsor: York University
Collaborators: MOUNT SINAI HOSPITAL; University College, London; University College London Hospitals; University of Calgary; McMaster University
Conditions: Acute Pain
Locations: Mount Sinai Hospital; University College London Hospital
Countries: Canada; United Kingdom
Enrollment: 400
Age range: 27 Weeks - 33 Weeks
Sex: ALL

Summary:
A multi-national multidisciplinary team will be working collaboratively to build a machine learning algorithm to distinguish between preterm infant distress states in the Neonatal Intensive Care Unit.

Eligibility criteria:
* QUALITATIVE INTERVIEWS
* Inclusion Criteria:
* parents of a child currently in the NICU or
* health professionals currently working in the NICU.
* Exclusion Criteria:
* Participants who cannot communicate fluently in English
* QUANTITITATIVE DATA CAPTURE (video, eeg, ecg, SPo2)
* Inclusion Criteria:
* Infants born between 28 0/7 weeks 32 6/7 weeks gestational age
* Infants who are within 6 weeks postnatal age
* Infants who are undergoing a routine heel lance
* Exclusion Criteria:
* Infants with congenital malformations
* Infants receiving analgesics or sedatives at the time of study (aside from sucrose),
* Infants with history of perinatal hypoxia/ischemia at the time of study.
* Infants with diaper rash or excoriated buttocks

Outcomes:
Behavioural Correlate of Distress; Cortical Correlate of Distress; Cardiac Correlates of Distress; Oxygen Saturation Correlate of Distress
Hospitalised COVID-19 Patients Cohort Study in the EuCARE Project
Trial ID: NCT05463380
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-02-23
Completion date: 2026-10-13
Sponsor: Euresist Network GEIE
Collaborators: Karolinska Institutet; Centro Hospitalar Lisboa Ocidental; Heinrich-Heine University, Duesseldorf; ASST Santi Paolo e Carlo; Imperial College London; Vilnius University Hospital Santaros Klinikos; University of Rome Tor Vergata; Bach Mai Hospital; Kenya Medical Research Institute; Hospital Regional de Alta Especialidad "Dr. Juan Graham Casasus"; University Hospitals Dorset NHS Foundation Trust
Conditions: COVID-19
Interventions: standard of care
Locations: Heinrich-Heine-Universität Düsseldorf; ASST Santi Paolo e Carlo; University of Rome Tor Vergata; Kenya Medical Research Institute (KEMRI); Vilnius universiteto ligoninė Santaros klinikos; Regional Hospital Dr. Juan Graham Casasús, Villahermosa; Centro Hospitalar Lisboa Ocidental; Karolinska Institutet; Poole University Hospitals, Dorset; Bach Mai Hospital
Countries: Germany; Italy; Italy; Kenya; Lithuania; Mexico; Portugal; Sweden; United Kingdom; Vietnam
Enrollment: 22000
Age range: 18 Years - None
Sex: ALL

Summary:
The WP3 hospitalized cohort in EuCARE is an observational multicentre study including collection of retrospective (historical) and prospective data from hospitalized COVID-19 patients followed at 12 clinics from 11 countries from 4 continents. In a subset of patients, peripheral blood, viral isolates and/or viral sequences are collected for analysis in WP2 with regards to neutralising antibodies, cellular immunity and SARS-CoV-2 diagnostics. Data and results from analysis of biological material will be analysed by biostatistical methods and with artificial intelligence in WP5. This analysis will focus on the impact on clinical outcome of viral variants / viral sequences as well as the vaccines used and the vaccination schedules.

Eligibility criteria:
Inclusion Criteria:

* Hospitalized adult (\>18 years) patients including patients in the emergency ward
* Positive for SARS-CoV-2 by PCR any time from 14 days before admission date until hospital discharge. Both first and recurrent episodes of COVID-19 will be included.
* Having a signed informed consent when required by ethical approval

Exclusion Criteria: none

Outcomes:
Severity of SARS-CoV-2 infection; In-hospital mortality; ICU Admissions and Discharges; Rate of Invasive and Non invasive mechanical ventilation
Development of Muscle-specific Multi-modal AI for Sarcopenia Diagnosis: Effects of Rehabilitation Training on Ant-muscle Aging
Trial ID: NCT07477574
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-03-01
Completion date: 2028-02-28
Sponsor: Seoul National University Bundang Hospital
Conditions: Sarcopenia
Interventions: Rehabilitation training
Enrollment: 75
Age range: 65 Years - 85 Years
Sex: ALL

Summary:
This study aims to develop a muscle-specific multimodal artificial intelligence (AI) model for the diagnosis of sarcopenia and to investigate the effects of rehabilitation training on muscle aging. Clinical, functional, and imaging data will be collected from participants with muscle function decline. Multimodal data, including muscle function measurements and clinical assessments, will be integrated to develop and validate an AI-based diagnostic model for sarcopenia. In addition, the effects of rehabilitation training on muscle function and muscle aging-related outcomes will be evaluated. The results of this study are expected to contribute to the development of digital biomarkers and precision rehabilitation strategies for sarcopenia.

Eligibility criteria:
Inclusion Criteria:

* Adults aged 65 years or older
* Individuals with reduced muscle function or at risk of sarcopenia
* Able to participate in rehabilitation training
* Able to understand the study procedures and provide written informed consent

Exclusion Criteria:

* Severe cardiovascular, neurological, or musculoskeletal conditions that limit participation in exercise
* Severe cognitive impairment
* Participation in other clinical trials that may influence study outcomes
* Any medical condition judged by the investigator to make participation unsafe

Outcomes:
AI model performance
Screening Trial for Glaucomatous Optic Discs in Portugal
Trial ID: NCT05875090
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-04-13
Completion date: 2024-12-31
Sponsor: Centro Hospitalar Universitário Lisboa Norte
Collaborators: Unidade Local de Saúde do Alto Minho; ACES Lisboa Norte; Equipa Regional dos Programas de Rastreio da Região de Saúde de Lisboa e Vale do Tejo; ACES Alto Minho
Conditions: Glaucoma
Interventions: Glaucoma Screening
Locations: Equipa Regional dos Programas de Rastreio da Região de Saúde de Lisboa e Vale do Tejo; Centro Hospitalar Lisbon Norte; Unidade Local Saude Alto Minho
Countries: Portugal; Portugal; Portugal
Enrollment: 6000
Age range: 55 Years - 65 Years
Sex: ALL

Summary:
Two primary care-based screening systems will be used to identify subjects with referrable glaucoma to hospital care.

Subjects between 55 to 65 years old living in two primary care areas (urban area in Lisbon ; countryside setting in Minho) will be invited to a one-time assessment of optic disc and intraocular pressure (IOP).

Criteria for referral will differ between centers, with one arm (Lisbon) using an artificial intelligence (AI) reading software of the optic disc picture, the other (Minho) will base their referral based on optical coherence tomography (OCT) retinal nerve fibre layer abnormality.

A masked reading center will be established to set the ground truth for diagnosis.

This pilot screening trial will explore the level of agreement between both systems as well as their cost effectiveness and identify diagnostic composite scores that could maximize the screening process. Secondary analyses will include the identification of population characteristics that increase effectivity of screening process as well as determining the population less likely to adhere to screening programmes.

Eligibility criteria:
Inclusion Criteria:

* subjects age between 55 to 65 years old registered in the primary care area of either ACES Lisbon Norte (Lisbon arm) or in the area of ACES Alto Minho (Minho arm)
* willingness to sign an informed consent for the screening process

Exclusion Criteria:

\- none

Poor quality in screening image will be included in the intention to treat analysis, but excluded from the diagnostic comparator outcome.

Patients with a known glaucoma diagnosis will not be excluded from the screening

Outcomes:
Diagnostic agreement between referring decision and reading center decision
Study of an Artificial Intelligence Algorithm for the Classification of Digital Tomosynthesis Breast Images for Automated Breast Cancer Diagnosis
Trial ID: NCT05858762
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-10-20
Completion date: 2023-12-31
Sponsor: Regina Elena Cancer Institute
Conditions: Breast Cancer Diagnosis
Interventions: Breast digital tomosynthesis
Locations: Università degli Studi di Napoli Federico II; "Regina Elena" National Cancer Institute
Countries: Italy; Italy
Enrollment: 200
Age range: 18 Years - None
Sex: FEMALE

Summary:
Mammography is a two-dimensional imaging technique which involves the tissues overlapping under the projective image; dense glandular tissue above or below the lesion can reduce the visibility of the lesion.

The trouble could be the interpretation of the image obtained which may lead to the inability to visualize a fist stage cancer and the probability that to a healthy person will be diagnosed a pathology that is not present (false positive). The introduction of an almost three-dimensional technique imaging called breast digital tomosynthesis (DBT) can overcome most limitations. In the last 5 years image analysis methods based on Artificial Intelligence (, AI) have also been massively introduced in breast cancer detection. The study is a prospective observational study based on Artificial intelligence whose the mail goal is to develop a method to identify a lesion.

Eligibility criteria:
Inclusion Criteria:

* Patients who refer to the Regina Elena for diagnostic mammography tests
* Informed consent

Exclusion Criteria:

* presence of prostheses, artifacts, outcomes of a study in the breast intervention under the study

Outcomes:
Artificial Intelligence system to detect a lesion
Construction of an Artificial Intelligence System for the Remote Automatic Supervision of Shoulder's Rehabilitation Exercises
Trial ID: NCT05026346
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-04-01
Completion date: 2025-01-30
Sponsor: Istituto Ortopedico Rizzoli
Conditions: Rotator Cuff Tears
Interventions: Pathologic Exercise
Locations: IRCCS-Istituto Ortopedico Rizzoli
Countries: Italy
Enrollment: 100
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
The current historical phase and the growing need for rehabilitation in the world make tele-rehabilitation systems, and e-Health in general, fundamental tools for increasing patient engagement and compliance with care, crucial elements for the preservation of the NHS from a perspective expenditure review and resource optimization. In particular, the rehabilitation patient has on average an adherence to the Home Exercise Program (HEP) between 30-50%, to which is frequently added a reduced effectiveness of motor learning due to the lack of feedback on the accuracy of the gesture, as is the case. it happens in the hospital or outpatient setting under the supervision of a therapist.

The new computational approaches for the analysis of data on human movement, aimed at the development of algorithms to automatically supervise the accuracy of the patient's gesture during home self-treatment exercise such as those based on Artificial Intelligence (AI) and Machine Learning (ML), especially those of the latest generation, called sub-symbolics (or connectionists) can help.

Among the most promising approaches are. Given the importance of the Home Exercise Program in shoulder disease, it was decided to select a population of patients affected by the main pathologies affecting this joint.

The main objective of the study is to create and validate a software tool for the automatic and expert analysis of the correct execution of the main rehabilitation exercises for the functional recovery of the shoulder following orthopedic pathologies.

Eligibility criteria:
Inclusion Criteria:

* Healthy subjects group:

  * Adult patients\> 18 years old
  * Patients with no known shoulder pathologies
* Group of subjects with shoulder pathology operated on

  * Adult patients\> 18 years
  * Suffering from orthopedic pathologies affecting the shoulder such as: outcomes of ultrasound-guided percutaneous treatment for tendon calcification, outcomes of ultrasound-guided detachment in adhesive bursitis, outcomes of proximal humerus fractures, repair of the rotator cuff, interventions for scapulo-humeral instability.

Exclusion Criteria:

* Patients with a history of opioid drug dependence or a history of substance abuse
* Patients suffering from orthopedic pathologies affecting the upper limbs in the presence of clear detectable surgical complications
* Patients with cognitive disorders (MMSE Mini Mental State Examinantion greater than or equal to 24/30).
* Patients suffering from major anamnestic or current neurological or psychiatric pathologies, severe cardiopulmonary, hepatic or renal pathologies that contraindicate participation in the study.

Outcomes:
Correctness of the shoulder movement
Assessment of AI Performance for the Detection of Bone Fractures in Children Aged Less Than 2 Years Old in Suspected Child Abuse Setting.
Trial ID: NCT05538403
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-05-01
Completion date: 2022-12-30
Sponsor: University Hospital, Montpellier
Conditions: Fracture
Locations: University hospital
Countries: France
Enrollment: 210
Age range: 0 Years - 2 Years
Sex: ALL

Summary:
The artificial intelligence (AI) software BoneView (GLEAMER Company, Paris, France) has been designed, tested and validated to detect and locate recent or semi-recent fractures on standard radiographs.

The objective will be to assess the AI performance for the detection of bone fractures in children aged less than 2 years old in suspected child abuse setting.

These patients benefit from a whole body radiography with a double blind reading by a "generalist" radiologist and a radiologist with expertise in child abuse. This readings will be compared with the AI results.

Hypothesis is that AI is effective for child fractures detection and could be of help especially for radiologists who are not experts in child abuse.

Eligibility criteria:
Inclusion criteria:

* aged less than 2 years old
* whole body radiography performed for suspected child abuse setting
* report available with a double blind reading (generalist radiologist and radiologist with expertise in child abuse)

Exclusion criteria:

* Radiograph not interpretable ( poor quality)
* AI not applicable

Outcomes:
Percentage of fracture detected by AI on radiographs
Etiological DiagnOsis of caRdiac Diseases Based on echoCardiograpHIc Images and Clinical Data.
Trial ID: NCT05942729
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-01-01
Completion date: 2027-01-01
Sponsor: Hospices Civils de Lyon
Conditions: Hypertension Arterial; Hypokinetic; Cardiomyopathies; Left Ventricular Hypertrophy
Interventions: Determine the etiology of hypokinetic and hypertrophic heart disease on transthoracic echocardiography data alone
Locations: Hopital Lyon Sud
Countries: France
Enrollment: 1000
Age range: 18 Years - None
Sex: ALL

Summary:
Research hypothesis - Recent studies have shown that high-dimensional descriptors of the cardiac function can be efficiently exploited to characterize targeted pathologies. In this project, the investigators hypothesize that echocardiograms possess a wealth of information that is currently under-exploited and that, combined with relevant patient data, will allow the development of robust and accurate digital tools for etiological diagnosis.

Objectives - Based on key advances recently obtained in image analysis, notably by members of the consortium, the objective of this project is to develop rigorous and explainable cardiac disease prediction models from echocardiography based on the transformer paradigm (AI). The strength of this study lies in the development of a strong AI framework to model the complex interactions between high-quality image-based measurements extracted from echocardiograms and relevant patient data to automatically predict etiological diagnosis of cardiac diseases

Eligibility criteria:
Inclusion Criteria:

* Patients with transthoracic echocardiography with satisfactory image quality (sufficient echogenicity)

Exclusion Criteria:

* Minor patients
* Patients under curatorship or guardianship

Outcomes:
the comparison of the performance of the etiological diagnosis obtained by the artificial intelligence with the etiological diagnosis already established and validated by a physician from the complementary examinations performed on the targeted patients.