moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
The Quantitative Study of the Habenula Based on Multi-channel Cascaded Neural Network and the Establishment of the Prediction Model of the Curative Effect in Patients With Depression
Trial ID: NCT05872607
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-01-01
Completion date: 2024-12-31
Sponsor: The First Hospital of Jilin University
Conditions: Depression
Locations: The First Hospital of Jilin University
Countries: China
Enrollment: 300
Age range: None - None
Sex: ALL

Summary:
Depression is the second leading cause of disease burden in our country. It has serious effects on the physical and mental health of human beings, and about 30% of patients with depression are unresponsive or respond poorly to antidepressant treatment. Clinical practice is in a tough position of wanting objective measures of assessing depression. The applicant and her team have devoted many years to the basic and clinical research on habenular nucleus (Hb) accumulating a significant amount of experience from animal experiments and patients' magnetic resonance (MR) studies. These studies have demonstrated that the habenular nucleus is the key target area that is responsible for the pathophysiological changes in depression as well as its treatment. Small volumes and unsatisfactory contrast have been knotty problems in the MR imaging of Hb. In addition, time-consuming manual segmentation and lack of quantitative standards in conventional studies has impeded the advancement of Hb research. Fortunately, the development of high-resolution multi-parametric quantitative MR imaging and the extensive use of artificial intelligence (AI) technology in medical imaging can just provide powerful support for the imaging, segmentation and quantification of Hb. This project proposes to use high resolution MR anatomy of Hb combined with multimodal fusion to 1) construct a model for automatic 3D segmentation of Hb MR images based on the densely connected multichannel dilated convolutional neural networks; 2) sift out the quantitative imaging signatures related to the antidepressants' efficacy using the radiomics methodology, and in combination with clinical information, construct an individualized prediction model for treatment efficacy.

Overall, this study focuses on the translation of basic research to clinical application in the hope of providing quantifiable objective imaging markers in clinical practice, facilitating clinical decision-making and bringing about individualized precise diagnosis and treatment.

Eligibility criteria:
Inclusion Criteria:

* Premiere depressed patients

Exclusion Criteria:

* Drug therapy modality change、Incomplete MR image data

Outcomes:
Multimodal MR data of habenula
Davos Alzheimer's Collaborative Healthcare System Preparedness to Increase Cognitive Assessment Rates for Older Adults
Trial ID: NCT06191952
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-02-01
Completion date: 2024-02-01
Sponsor: Ludwig-Maximilians - University of Munich
Collaborators: ki:elements; Hoffmann-La Roche; Eisai Limited; Johanniter-Unfall-Hilfe e.V.; University of Southern California
Conditions: Subjective Cognitive Decline; Alzheimer Dementia
Interventions: Cognigram; Subjective cognitive decline questionnaire (SCD-Q); blood-based biomarkers; ki:elements digital speech biomarker for cognition (ki:e SB-C)
Locations: Klinik und Poliklinik für Psychiatrie und Psychotherapie des LMU Klinikums
Countries: Germany
Enrollment: 500
Age range: 65 Years - None
Sex: ALL

Summary:
In this study the investigators explore a pragmatic strategy to increase cognitive screening rates in the community. The investigators will compare the monetary value of different combinations of SCD questionnaires, digital cognitive tests, and blood Alzheimer's Disease (AD) biomarkers to identify the best approach for primary care settings.

Eligibility criteria:
Inclusion Criteria:

* age 65 years or older

Exclusion Criteria:

* younger than 65 years

Outcomes:
Proportion of participating individuals screening positive for SCD
The Success of ChatGPT in Providing American Society of Anesthesiologist (ASA) Scores
Trial ID: NCT06321445
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-02-08
Completion date: 2024-05-01
Sponsor: Kanuni Sultan Suleyman Training and Research Hospital
Conditions: Artificial Intelligence; Preoperative Evaluation
Interventions: providing ASA score
Locations: Health Science University İstanbul Kanuni Sultan Süleyman Education and Training Hospital
Countries: Turkey (Türkiye)
Enrollment: 2851
Age range: None - None
Sex: ALL

Summary:
Patients applied to the anesthesia clinics of Health Science University Istanbul Kanuni Sultan Suleyman Training and Research Hospital and Basaksehir Cam and Sakura City Hospital were included in the study. Evaluation forms which will be filled in every preoperative examinations will be saved in the hospitals systems. Patients datas without indentification informations will be asked to ChatGpt to give anesthesiological risc scores. This scores will be compared with the scores already given by anesthesiologists.

Eligibility criteria:
Inclusion Criteria:

* Patient who admitted to anesthesia clinics for both hospital
* Patient with ASA scores between I-IV

Exclusion Criteria:

* ASA scores higher than IV
* Emergency cases patients have symptoms like upper and lower airway infections.
* patients haven't got enough informations to provide ASA scores.
* Patients with symptoms of an active upper or lower respiratory tract infection

Outcomes:
The Success of ChatGPT in Providing American Society of Anesthesiologist (ASA) Scores
Clinical Validation of an Artificial Intelligence-Based G-FAST Score in Patients With Stroke
Trial ID: NCT07538492
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-04-10
Completion date: 2028-12-31
Sponsor: Xuanwu Hospital, Beijing
Collaborators: Capital Medical University; People's Hospital of Beijing Daxing District; Beijing Tiantan Hospital; The First Hospital of Fangshan District,Beijing
Conditions: Stroke
Enrollment: 297
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to validate the clinical performance of an artificial intelligence (AI)-based automatic assessment system for the G-FAST score. The core comparison is the consistency and accuracy between AI-generated G-FAST results and standardized manual G-FAST assessments performed by trained professionals. The goal is to provide a convenient, efficient, and objective tool for acute stroke screening and early identification, reduce the subjective variability of manual scoring, and optimize the pre-hospital and in-hospital stroke assessment workflow.

Eligibility criteria:
Inclusion Criteria:

1. Aged ≥ 18 years, of either sex.
2. Clinically diagnosed with stroke, and confirmed by cranial CT/MRI to have ischemic or hemorrhagic stroke.
3. Onset within 7 days.
4. Alert and oriented, able to cooperate with standardized video and audio data collection.
5. The patient or their legally authorized representative understands the study and voluntarily provides written informed consent (including consent for audio-visual data collection).

Exclusion Criteria:

1. Neurological deficits caused by non-stroke etiologies (e.g., brain tumor, traumatic brain injury, encephalitis).
2. Patients with impaired consciousness, severe cognitive dysfunction, or psychiatric disorders that prevent cooperation with video collection and scale assessment.
3. Patients with severe visual or hearing impairment, or global aphasia, who are unable to follow instructions.
4. Critically ill patients requiring immediate cardiopulmonary resuscitation or endotracheal intubation, making video and audio data collection impossible.
5. Patients with severe facial or limb deformities, or large-area dressings that severely interfere with camera data collection.
6. Patients with unilateral or bilateral upper limb amputation, severe deformity, unhealed fracture, joint fixation, or severe contracture.

Outcomes:
Agreement between AI-generated and physician-scored G-FAST scale assessments
Possibility of Using a Mobile Application for Three-plane Gait Analysis.
Trial ID: NCT06289231
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2024-05
Completion date: 2024-08
Sponsor: Gdansk University of Physical Education and Sport
Conditions: Gait
Locations: Gdansk University of Physical Education and Sport
Countries: Poland
Enrollment: 50
Age range: 20 Years - 25 Years
Sex: ALL

Summary:
The main purpose of this study will be to assess the consistency and reliability of measurements made using the Vicon three-plane gait analysis device (Vicon Motion Capture System Ltd, Oxford, UK) and a mobile application based on image recognition technology with the help of artificial intelligence.

Eligibility criteria:
Inclusion Criteria:

* healthy people aged 20-25 years

Exclusion Criteria:

* orthopaedic disease
* neurological disease
* rheumatological disease
* pain in lower limbs in the last 6 months
* pain in lumbar spine in the last 6 months
* surgeries of lower limbs in the last 6 months
* surgeries of spine in the last 6 months
* musculoskeletal injuries in the lower limbs in the last 6 months
* musculoskeletal injuries in the lumbar spine in the last 6 months
* overweight and obesity, determined on the basis of the BMI value

Outcomes:
Gait analysis by the Vicon system; Gait analysis by the mobile image recognition application
Comparative Study Between Artificial Intelligence-Assisted/Computer-Guided Versus Conventional Ridge Splitting Utilizing Electromagnetic Mallet for Reconstruction of Horizontal Ridge Defects: A Randomized Controlled Clinical Study
Trial ID: NCT07256730
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-08-27
Completion date: 2026-03
Sponsor: Kafrelsheikh University
Conditions: Reconstruction of Horizontal Ridge Defects
Interventions: free-hand ridge splitting; AI/guided ridge splitting
Locations: Faculty of Dentistry, Periodontology Department
Countries: Egypt
Enrollment: 22
Age range: 18 Years - 40 Years
Sex: ALL

Summary:
The current trial aims to assess the efficacy of utilizing the electromagnetic mallet either by AI-assisted digital workflow or by the conventional freehand approach for reconstruction of horizontal ridge defects utilizing the ridge-split and expansion technique.

Eligibility criteria:
Inclusion Criteria:

1. The target population with inadequate bone volume for implant placement due to width insufficiency of maxillary anterior alveolar ridges.
2. Age ranges from 18 to 40 years of both sexes.
3. Absence of any complicating systemic condition that may contraindicate surgical procedures and implant placement.
4. Adequate oral hygiene.
5. Eligible participants should present good general health and agree to random assignment to any of the two parallel study groups.
6. Participants had a minimum 3-month post-extraction healing period and a horizontal defect in the maxillary esthetic zone with at least a bone width of 3 mm.

Exclusion Criteria:

1. Vertical ridge defect.
2. Undercut on the labial/buccal side.
3. Thick cortical bone without cancellous bone inside.
4. Uncontrolled systematic disorders as, diabetes mellitus, uncontrolled periodontal disease, history of head and neck radiotherapy, smokers, pregnancy, noncompliant patients, allergy to the used medications, uncooperative individuals or those unable to attend the study follow-up appointments.

Outcomes:
alveolar ridge width
Relationship Between Muscle Mass in Preoperative Computed Tomography and a Neuromuscular Blocking Agent Dose Required for Maintaining Deep Neuromuscular Blockage: a Prospective Observational Study.
Trial ID: NCT06769932
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-01-07
Completion date: 2027-01-07
Sponsor: Seoul National University Bundang Hospital
Conditions: Neuromuscular Blockade
Interventions: preoperative computed tomography
Locations: Seoul National University Bundang Hospital
Countries: South Korea
Enrollment: 90
Age range: 19 Years - 80 Years
Sex: ALL

Summary:
With the recent development of image analysis and artificial intelligence, it has become possible to measure the muscle volume from preoperative computed tomography (CT) images. Therefore, in this study, the investigators quantify the muscle volume from preoperative CT images and analyze the correlation with the dose of neuromuscular blocking agent, rocuronium, required for maintaining deep neuromuscular blockade.

Eligibility criteria:
Inclusion Criteria:

* Those who voluntarily agreed and signed the written consent form before participating in the study
* American Society of Anesthesiologists physical class (ASA class) 1-3
* Patients scheduled for elective colon surgery under general anesthesia
* Patients undergoing abdominal computed tomography for routine preoperative preparation

Exclusion Criteria:

* Pregnant women
* Decreased renal function (GFR\<60mL/min/1.73m2).
* Patients taking drugs that affect deep neuromuscular blockade
* Patients with a history of neuromuscular and muscle disorders

Outcomes:
muscle volume
Creating an Innovative AI-based (Artificial Intelligence) IN SILICO TECHNOLOGY TCRact to Launch a NEW SERVICE for Designing and Optimizing T-cell Receptors (TCR) for Use in Cancer Immunotherapies
Trial ID: NCT04994093
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-10-06
Completion date: 2023-12-28
Sponsor: Ardigen
Collaborators: National Center for Research and Development, Poland
Conditions: Colon Cancer; Cancer
Interventions: Collection of blood (PBMC), biopsy (FFPE) and stool samples.
Locations: Maria Sklodowska-Curie National Research Institute of Oncology
Countries: Poland
Enrollment: 118
Age range: 18 Years - None
Sex: ALL

Summary:
The PROJECT OBJECTIVE is to create an innovative AI-based (Artificial Intelligence) IN SILICO TECHNOLOGY for the design and optimization of T-cell receptors (TCRs) capable of recognizing specific tumor antigens presented on Human Leukocyte Antigen (HLA). The technology will be based on bioinformatics (including molecular modelling) and artificial intelligence (including predictive, generative and optimization models). PROJECT TASKS are dedicated to the implementation of individual technological components (including the database necessary for their preparation) and to performing IN VITRO evaluation rounds to optimize the technology.

Integration, within an IN SILICO TECHNOLOGY, of processes which currently require huge amounts of in vitro laboratory experiments that are necessary for bringing new TCR-based cancer immunotherapies into clinical trials:

* finding appropriate TCRs targeting cancer neoantigen presented on a HLA molecule (pHLA)
* testing for potential TCR toxicity (targeting self antigens presented on healthy tissues)
* optimization of pHLA:TCR binding affinity and toxicity.

Eligibility criteria:
Inclusion Criteria:

* Clinical diagnosis of advanced resectable colorectal cancer (Active stage II, III or IV)
* Histopathologically confirmed as adenocarcinoma
* Age 18 or over
* Being able to understand and sign the Informed Consent Form (ICF)
* Qualification for resection within 3 months from W0 or condition after primary tumor resection, not longer than 5 years from surgery

Exclusion Criteria:

* Mucinous colorectal adenocarcinoma
* Canal and anal edge cancer,
* Adenocarcinoma of the appendix,
* Patients treated with neoadjuvant therapy or radiation therapy in the 3 months prior to study enrollment.
* Patients treated with systemic chemotherapy in the 3 months prior to study enrollment.
* Histopathological diagnosis other than adenocarcinoma
* Remission
* History of inflammatory bowel diseases
* Pregnancy
* HIV infection
* Active infection with hepatitis B and C viruses
* Leukocytopenia

Outcomes:
Finding appropriate TCRs targeting cancer neoantigen presented on a HLA molecule (pHLA); Testing for potential TCR toxicity (targeting self antigens presented on healthy tissues); Optimization of pHLA:TCR binding affinity and toxicity
Batch Enrollment for an Artificial Intelligence-Guided Intervention to Lower Neurologic Events in Patients With Unrecognized Atrial Fibrillation (BEAGLE)
Trial ID: NCT04208971
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-11-02
Completion date: 2022-01-27
Sponsor: Mayo Clinic
Conditions: Atrial Fibrillation
Interventions: AI-enabled ECG-based Screening Tool for AF
Locations: Mayo Clinic
Countries: United States
Enrollment: 1225
Age range: 18 Years - None
Sex: ALL

Summary:
This is a prospective study to test a novel artificial intelligence (AI)-enabled electrocardiogram (ECG)-based screening tool for improving the diagnosis of unrecognized atrial fibrillation (AF) and stroke prevention.

Eligibility criteria:
Inclusion Criteria:

* Age ≥18 years
* Had a 10-second 12-lead ECG done at Mayo Clinic
* Men with CHA2DS2-VASc ≥2 or women with CHA2DS2-VASc ≥3

Exclusion Criteria:

* Diagnosed atrial fibrillation or atrial flutter
* Missing date of birth or sex in the electronic health record (EHR)
* A history of intracranial bleeding
* A history of end-stage kidney disease
* Have an implantable cardiac monitoring device, including a pacemaker, a defibrillator, or implanted loop recorder
* Deemed by research personnel to have limitations that would prevent them from being able to provide informed consent, use the patch, or complete interviews will not be included.

Outcomes:
Diagnosis of Atrial Fibrillation as Detected by Patch Application
Development of a Clinical Decision Support System With Artificial Intelligence for Cancer Care
Trial ID: NCT04675138
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2020-08-20
Completion date: 2024-12-31
Sponsor: National University Hospital, Singapore
Conditions: Gastric Cancer; Esophageal Cancer; Esophagogastric Junction Cancer
Interventions: No intervention will be provided to the subject
Locations: University Hospital Leipzig; National Cancer Centre Hospital East; National University Hospital; National Cancer Centre Singapore; Ng Teng Feng General Hospital; Tan Tock Seng Hospital; Seoul National University Hospital; Karolinska Institute Hospital; The University of Edinburgh
Countries: Germany; Japan; Singapore; Singapore; Singapore; Singapore; South Korea; Sweden; United Kingdom
Enrollment: 1000
Age range: 21 Years - None
Sex: ALL

Summary:
Clinical Decision Support Systems (CDSSs) to augment clinical care and decision making. These are platforms which aim to improve healthcare delivery by enhancing medical decisions with targeted clinical knowledge, patient information, and other health information.

In view of the benefit of developing a CDSS, we sought to develop an alternative CDSS for oncologic therapy selection through a partnership with Ping An Technology (Shenzhen, China), beginning with gastric and oesophagal cancer. This would be done in a piecemeal fashion, with the prototype platform utilizing only international guidelines and high-quality published evidence from journals to arrive at case-specific treatment recommendations. This platform would then be evaluated by comparing its recommendations with that from the multidisciplinary tumour boards of several tertiary care institutions to determine the concordance rate.

Eligibility criteria:
A. In discovery and internal retrospective validation part:

1. Patients with primary gastric adenocarcinoma including preinvasive carcinoma or
2. Patients with gastroesophageal junction cancers or
3. Patients with oesophageal cancer including adenocarcinoma, squamous cell carcinoma and preinvasive carcinoma subtypes.

B. In prospective validation part:

1. Patients with primary gastric adenocarcinoma including preinvasive carcinoma or
2. Patients with esophageal or gastroesophageal junction adenocarcinoma

Exclusion Criteria:

A. In discovery and internal retrospective validation part:

1. Patients with other primary cancers involving the stomach or oesophagus
2. Patients with other cancer subtypes
3. Patients with concomitant cancers of other organs

B. In prospective validation part:

1. Patients with esophageal squamous cell carcinoma
2. Patients who participate in clinical trials where the treatment modality is not standard of care

Outcomes:
Concordance Rate
Automatic Phenotyping of Patients on 2D Photography
Trial ID: NCT06219421
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-01-01
Completion date: 2028-03-01
Sponsor: Imagine Institute
Conditions: Dysmorphia; Orphan Diseases; Dysmorphies Craniofaciales
Interventions: Clinical data reuse
Locations: Necker - Hôpital des Enfants Malades
Countries: France
Enrollment: 22000
Age range: None - None
Sex: ALL

Summary:
The field of artificial intelligence is booming in medicine and in the field of diagnosis. The data can be varied: x-rays, pathology sections, or photographs.

It is considered that 30 to 40% of the 7000 rare diseases described to date cause craniofacial dysmorphia. Their detection sometimes requires the trained eye of a geneticist, because certain phenotypic traits are subtle. These diagnostic difficulties and the fact that certain diseases are extremely uncommon lead to considerable diagnostic delays

Eligibility criteria:
The patient inclusion criteria are:

* Patients followed in medical genetics,
* Patients undergoing maxillofacial surgery, or craniofacial surgery as part of the management of a pathology, of genetic origin or not, associated with dysmorphism of the head and neck,
* Patients for whom frontal and profile facial photographs are taken as part of their treatment.

The inclusion criteria for control subjects are:

* Patients followed in maxillofacial surgery, for a disease other than a rare disease associated with dysmorphia in the head or neck: acute pathology (wound) or chronic (gynecomastia).
* Patients for whom frontal and profile facial photographs are taken as part of their treatment.

The criteria for non-inclusion of patients are:

* Patients who have undergone facial or skull surgery before the first photo was taken.
* Person subject to a judicial safeguard measure.
* People objecting to the reuse of their health data.

The criteria for non-inclusion of control subjects are:

* Pathologies affecting facial symmetry (dental cellulitis, displaced fractures).
* Patient followed for dysmorphic syndrome or in whom dysmorphic syndrome has been suspected.
* Person subject to a judicial safeguard measure.
* People objecting to the reuse of their health data.

Outcomes:
Learning an algorithm on 2D front and profile photographs, by extracting geometric and textural features, to help the practitioner carry out a diagnosis.