moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Prediction of Pulmonary Function and Qi Deficiency Based on the Acoustic Analysis Before and After Step Test
Trial ID: NCT05739266
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-02-15
Completion date: 2023-06-30
Sponsor: China Medical University Hospital
Conditions: Qi-Xu; Qi Deficiency; Pulmonary Function; COPD
Interventions: 3 minutes step test
Locations: No. 2, Yude Road, North District
Countries: Taiwan
Enrollment: 100
Age range: 20 Years - None
Sex: ALL

Summary:
Acoustic analysis is a method of clinically non-invasive, low-cost, remote-operation, and can avoid direct face-to-face contact.

It has been proved by many researches in recent years that it has a high diagnostic rate in predicting diseases. Lung function is closely related to human vocalization and has the potential and need for sound analysis. From literature review, it can be inferred that the Qi deficiency constitution in TCM is related to the strength of heart and lung function.The purpose of this study is to investigate the correlation between cardiopulmonary function and Qi deficiency, and to predict the correlation between the data by computer algorithm and artificial intelligence. Furthermore, the artificial intelligence (AI) self-correction ability can be combined with sound analysis to simulate a sound analysis system that can predict cardiopulmonary function and degree of qi deficiency only through sound information.

Research motivation and questions:

1. Is the sound characteristic of an individual related to individual's qi deficiency and cardiopulmonary function?
2. Can lung function and qi deficiency be measured by sound analysis?
3. To understand the correlation between Qi deficiency and cardiopulmonary endurance index.
4. Is there any difference in sound characteristics before and after step test and does it help predicting cardiopulmonary function?

Eligibility criteria:
Inclusion Criteria:

* Age: over 20 years old.
* Agreed and signed Informed Consent Form.

Exclusion Criteria:

* Known heart diseases, acute severe respiratory infection, severe arthritis or other orthopedic disabilities, severe hypertension, or pregnant.

Outcomes:
FVC<80%; FVC>80%; Qi deficient; step test: heart rate recovery; Voice record
Laryngomalacia, Examinations and Quality of Life in Children Before and After Treatment With Follow-up After 1 Year
Trial ID: NCT06464757
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-01-01
Completion date: 2034-12-31
Sponsor: Oslo University Hospital
Collaborators: University of Oslo; Lovisenberg Diakonale Hospital
Conditions: Laryngomalacia; Sleep Apnea, Obstructive; Sleep Apnea; Ear, Nose and Throat Disorder; Pediatric Disorder; Surgery; Quality of Life
Locations: Oslo University Hospital; Lovisenberg Diaconal Hospital
Countries: Norway; Norway
Enrollment: 50
Age range: 1 Week - 52 Weeks
Sex: ALL

Summary:
Laryngomalacia is the most frequent cause of stridor in children under 1 year. The airway obstruction generates turbulent airway flow and creates the characteristic high-frequency stridor sound. In addition, the airway obstruction can cause apnea, a following drop in oxygen saturation and sleep disturbances. The symptoms of laryngomalacia are often worsened by activity, feeding, crying and lying flat on the back. The diagnosis is made with flexible laryngoscopy when the child is awake. The children are most often treated with expectation, information and guidance, observation with help with feeding and reflux treatment. Up to 20% of patients have a severe degree of laryngomalacia with apneas, which is an indication for surgical treatment. The investigators want to examine whether sleep examinations can help us deciding which child benefit from surgery, and follow-up the child again after 4-6 weeks and 1 year. The sleep examinations are carried out with polygraphy and/or polysomnography with simultaneous audio records and video monitoring and with Somnofy from VitalThings. The investigators want to use artificial intelligence and machine learning when analyzing the sleep examinations. The investigators also want to have a control group examining the sleep and breathing during night at home. In both groups the investigators want to examine the quality of life with the questionnaire ITQoL-SF47.

Eligibility criteria:
Inclusion Criteria:

* laryngomalacia
* stridor
* breathing difficulties

Exclusion Criteria:

\- none

Outcomes:
Apnea-hypopnea-index; Quality of life in toddlers
artifiCiAl Intelligence Model for Evaluating the Surgical Techniques of caRdiAc Surgeons (CAMERA)
Trial ID: NCT06739005
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-01-09
Completion date: 2027-07
Sponsor: China National Center for Cardiovascular Diseases
Conditions: CABG-patients; CABG; Cardiovascular Surgery; Surgeons; Artificial Intelligence (AI)
Locations: Fuwai Hospital, CAMS & PUMC
Countries: China
Enrollment: 284
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this study aims to investigate the use of artificial intelligence to analyze and evaluate the characteristics and proficiency of surgeons during vascular anastomosis in coronary artery bypass grafting (CABG) procedures. The main question it aims to answer is:

Consistency assessment between AI evaluation scores and human expert evaluation scores for surgeons during left anterior descending (LAD) artery anastomosis.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18 years
* Undergoing internal mammary artery-to-left anterior descending artery bypass grafting
* First-time recipient of isolated CABG surgery
* Signed written informed consent

Exclusion Criteria:

* Patients with acute coronary syndrome
* Patients with contraindications to coronary CT angiography or coronary angiography
* Patients with renal insufficiency or active liver disease, including those with persistently elevated serum transaminases of unknown cause or any serum transaminase levels exceeding three times the upper limit of normal.

Outcomes:
Consistency assessment between AI surgical evaluation scores and human expert scores.
Artificial Intelligence Use for the Detection of Atrial Fibrillation Drivers
Trial ID: NCT03401931
Status: WITHDRAWN
Study type: OBSERVATIONAL
Start date: 2018-01-09
Completion date: 2018-01-09
Sponsor: Hospital St. Joseph, Marseille, France
Conditions: Fibrillation, Atrial; To Evaluate the Intraoperative Efficiency of the AIFIB Software in the Detection of Atrial Fibrillation Driver During an Ablation Procedure
Locations: Hopital d'Aix en Provence; CHU Nice; Isala Hospital
Countries: France; France; Netherlands
Enrollment: 0
Age range: 18 Years - None
Sex: ALL

Summary:
pilot clinical study, multicenter, prospective, open, nonrandomized

Eligibility criteria:
Inclusion Criteria: major patient with an indication of AF or AT ablation -

Exclusion Criteria:

\-

Outcomes:
AF termination
Real-world Effectiveness Evaluation of Clinical Decision Support System Based on Artificial Intelligence (AI-CDSS) on Diagnosis
Trial ID: NCT05065931
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2019-03-01
Completion date: 2019-06-01
Sponsor: Peking University Third Hospital
Conditions: Medical Informatics Applications
Interventions: Auxiliary diagnostic system by AI-CDSS
Locations: Peking University Third Hospital
Countries: China
Enrollment: 34113
Age range: None - None
Sex: ALL

Summary:
This study intends to explore the accuracy of clinical diagnosis of AI based CDSS system and promotion of clinical work by comparing CDSS before and after the online.

Eligibility criteria:
Inclusion Criteria:

* all hospitalized patients in 6 clinical departments, Otolaryngology, Orthopaedic, Respiratory Medicine, General Surgery, Cardiology and Hematology from December 2016 to February 2019.

Exclusion Criteria:

* Missing data for key variables

Outcomes:
Accuracy Rate of Recommended Diagnosis by CDSS, up to 12 weeks; Patients' hospitalization time (days), up to 24 weeks; consistency between admission diagnosis and discharge diagnosis up to 12 weeks
Personalized Shared Eye-care Identification of Age-related Macular Degeneration Using Artificial Intelligence and Telemedicine in a Prospective, Exploratory, Multinational, Multicentre Cross-sectional Study Matching Optometrist Office-based Sites With Clinical Supervision
Trial ID: NCT06465212
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-07
Completion date: 2026-07
Sponsor: Medical University of Vienna
Conditions: Age-Related Macular Degeneration
Interventions: TopCon Maestro 2 OCT
Enrollment: 5000
Age range: 55 Years - 99 Years
Sex: ALL

Summary:
Office-based optometrist centres equipped with low-cost OCT devices can be used for screening of age-related macular degeneration. Matching next-door optometrist centres with clinical sites introduces a shared care service in an unprecedented and broad manner and offers timely and inclusive access to eye care for all citizens affected by the most frequent blinding disease in western countries.

The aim of the study is to detect AMD in a next-door office-based setting on an individual level using low-cost OCT, through a telemedicine feedback loop.

The specific aims of this study are:

* Setting up a network of optometrist centres matched to clinical sites to perform shared care to protect eyesight in the elderly population.
* Identify imaging biomarkers of age-related macular degeneration from OCT imaging.
* Give a risk estimation of progression using a one-time low-cost OCT scan

Eligibility criteria:
Inclusion Criteria:

* Age: 55-99 years old
* Preserved visual function, defined as visual acuity better than or equal to 0.3logMAR in one or both eyes

Exclusion Criteria:

* Refractive errors with mean spherical equivalent (MSE) greater than +/-6.00, with no limit to astigmatic component.

Outcomes:
Identification of different AMD stages on OCT images from a community-based setting to calculate the real-world prevalence of AMD
Developing Artificial Intelligence-based Algorism Based on Life-log Data Acquired From Wearable Device for the Prediction of Chemotherapy-induced Side Effects and Symptom
Trial ID: NCT05937477
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-06-08
Completion date: 2026-06-30
Sponsor: Sehhoon Park
Conditions: Lung Cancer; Head and Neck Cancer; Esophageal Cancer
Interventions: Fitbit smartwatch
Locations: Samsung Medical Center
Countries: South Korea
Enrollment: 500
Age range: 20 Years - None
Sex: ALL

Summary:
In this study, the investigators obtain wearable disease based biomarkers from patients diagnosed with cancer and undergoing chemotherapy, and simultaneously measure patient self-reported adverse events through an app to evaluate chemotherapy completion rates, emergency room visits, and frequency of CTCAE adverse events.

The investigators will develop an artificial intelligence-based algorism that can predict patients' side effects based on biomarkers alone.

Eligibility criteria:
Inclusion Criteria:

* 20 years of age or older
* Must have diagnosed with lung, head and neck, or esophageal cancer,

  1. scheduled to receive their first treatment in preoperative or postoperative chemotherapy.
  2. scheduled for systemic chemotherapy due to recurrence or metastasis.
* Eastern Cooperative Oncology (ECOG) performance status of 0 to 2.
* Patients who have access to a smartphone and can use the mobile app on their own.
* Understand the purpose of the study and agree to participate in the study.

Exclusion Criteria:

* Patients who, in the judgment of the researcher, have difficulty using the application.
* Patients who are judged by the medical staff to be unable to participate in the study.

Outcomes:
Developing artificial intelligence prediction algorism
Artificial Intelligence to Assist the Echocardiographic Identification of Transthyretin Cardiac Amyloidosis
Trial ID: NCT06328075
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-01-01
Completion date: 2026-01-01
Sponsor: Algalarrondo Vincent
Collaborators: Bichat Hospital; Bioquantis
Conditions: Amyloid Cardiomyopathy; Transthyretin Amyloid Cardiomyopathy; Transthyretin Cardiac Amyloidosis
Interventions: non interventional study
Locations: Bichat
Countries: France
Enrollment: 15000
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this study is to develop an algorithm using artificial intelligence (AI) to assist identification of potential ATTR-CM cases using routine transthoracic echocardiography.

The main questions it aims to answer are:

* is the algorithm able to diagnose ATTR-CM
* is the algorithm able to diagnose different types of ATTR-CM (ATTRv, ATTRwt)

This is a non interventional study. Participant' echocardiographies will be, after deidentification, used to train, valid and test the algorithm.

Eligibility criteria:
ATTR-CM patients:

Inclusion Criteria:

* Cardiac transthyretin amyloidosis diagnosed on the classic criteria:

  1. Absence of monoclonal immunoglobulin AND
  2. Presence of a bisphosphonate scintigraphy with enhancement in the cardiac area OR

  2-Presence of a cardiac biopsy showing transthyretin (Congo red positive) cardiac amyloidosis (demonstrated either by immunostaining or by mass spectrometry) OR 3-Presence of a peripheral biopsy showing transthyretin amyloidosis (see above) associated with cardiac infiltration (parietal thickness \>12mm without other cause of cardiac hypertrophy)
* No opposition to research

Non-inclusion criteria:

* Another cause of cardiac amyloidosis: AL AA amyloidosis…
* Mixed heart disease with associated presence of non-amyloid heart disease (ischemic heart disease, dilated, etc.)

Control patients:

Inclusion criteria:

* Indication for transthoracic echocardiography as part of cardiological follow-up
* Patient affiliated with social security
* Patient's agreement to participate in the research and signature of the consent form.
* Technical conditions of the examination and echogenicity allowing acquisition of good quality echocardiographic images, allowing post processing

Non-inclusion criteria:

* Presence of cardiac amyloidosis as defined above
* Presence of transthyretin amyloidosis even without demonstrated cardiac involvement
* Patient monitored for asymptomatic transthyretin mutation
* Minor patient or patient unable to give their consent (unconscious patient, under guardianship)

Outcomes:
Building and validating the diagnostic performance metrics curves of the AI algorithm to diagnose ATTR-CM :; Building and validating the diagnostic performance metrics of the AI algorithm to diagnose ATTR-CM :
Impact of Innovative ECG Training Using AI-Supported Clinical Scenarios on Knowledge, Clinical Reasoning, and Self-Efficacy Among Nursing Staff
Trial ID: NCT07455357
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-12-01
Completion date: 2026-02-10
Sponsor: Alexandria University
Conditions: Electrocardiogram Interpretation; Nursing Education; Clinical Reasoning; Artificial Intelligence; Clinical Decision-Making; Cardiovascular Care
Interventions: Artificial Intelligence Driven Scenario-Based Learning Software; Traditional Electrocardiogram Educational Program
Locations: Faculty of Nursing, Alexandria University
Countries: Egypt
Enrollment: 64
Age range: 18 Years - None
Sex: ALL

Summary:
Background and Purpose Accurate interpretation of an Electrocardiogram is a vital skill for nursing staff to ensure patient safety and timely intervention in cardiovascular care. Traditional training methods often lack the interactive and complex nature of real-life clinical situations. This study aims to evaluate the effectiveness of an innovative training program that uses Artificial Intelligence to create realistic clinical scenarios. The goal is to determine if this technology-enhanced approach improves nurses' knowledge, their ability to make clinical decisions (clinical reasoning), and their confidence in performing these tasks (self-efficacy).

Study Design and Methodology The researchers will conduct a study involving nursing staff to compare their performance before and after the training intervention. Participants will engage with Artificial Intelligence supported clinical scenarios specifically designed for Electrocardiogram interpretation.

Data Collection

To measure the impact of the training, the study will use four primary tools:

An Electrocardiogram Interpretation Knowledge Test to measure theoretical understanding.

An assessment of Nursing Decision-Making in Electrocardiogram Interpretation to evaluate practical clinical reasoning.

A Self-Efficacy Scale for Artificial Intelligence-based Electrocardiogram Training to measure the participants' confidence in their skills.

Focus group discussions will be held at the end of the study to gain deeper qualitative insights into the nursing staff's experiences and perceptions of using technology in their professional development.

Eligibility criteria:
Inclusion Criteria

* Nursing staff currently employed in clinical practice.
* Willingness to participate in the study and provide written informed consent.
* Ability to use basic computer software or mobile applications to interact with the Artificial Intelligence platform.

Exclusion Criteria

* Nurses who have attended advanced Electrocardiogram certification courses or specialized training within the past three months to avoid bias in the baseline knowledge assessment.
* Nurses who have previously participated in formal training or research studies involving Artificial Intelligence-driven educational platforms or clinical decision-support systems to ensure responses and perceived self-efficacy are not influenced by prior familiarity.

Outcomes:
Electrocardiogram Interpretation Knowledge Score
A Multicenter Study in Bronchoscopy Combining Stimulated Raman Histology With Artificial Intelligence for Rapid Lung Cancer Detection
Trial ID: NCT07045103
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-12-21
Completion date: 2026-05
Sponsor: Invenio Imaging Inc.
Collaborators: Memorial Sloan Kettering Cancer Center; University of North Carolina, Chapel Hill; M.D. Anderson Cancer Center; Mayo Clinic; University of California, San Diego; Montefiore Medical Center-Moses-Weiler; Corewell Health
Conditions: Lung Biopsy
Locations: UC San Diego; Mayo Clinic Jacksonville; Corewell Health; Memorial Sloan Kettering Cancer Center; Montefiore Medical Center; UNC Medical Center; MD Anderson Cancer Center
Countries: United States; United States; United States; United States; United States; United States; United States
Enrollment: 900
Age range: 22 Years - None
Sex: ALL

Summary:
The ON-SITE study represents a prospective, observational study focused on the training/tuning and pivotal validation of deep learning algorithms that detect cell/tissue morphology suspicious for cancer in biopsies of peripheral lung nodules/masses and mediastinal/hilar lymph nodes imaged with the NIO Laser Imaging System in the procedure room without requiring traditional sample processing.

The study includes four arms based on biopsy location and biopsy modality/tool:

1. Transbronchial forceps biopsy of peripheral lung nodules/masses (peripheral-TBBx)
2. Transbronchial needle aspiration biopsy of peripheral lung nodules/masses (peripheral TBNA)
3. Transbronchial needle aspiration biopsy of mediastinal/hilar lymph nodes (EBUS-TBNA)
4. Transbronchial cryo biopsy of peripheral lung nodules/masses (peripheral-CBx)

Eligibility criteria:
Inclusion Criteria:

1. The patient or legal guardian is willing and able to understand, sign and date the Ethics committee approved study specific Informed Consent Form.
2. The patient is 22 years of age or older.
3. The patient is scheduled for routinely indicated staging of the mediastinum (EBUS-TBNA) and planned peripheral lung biopsy procedure (peripheral TBBx/TBNA) with an intermediate to high pretest probability of lung cancer based on clinician suspicion.
4. The patient can tolerate the clinical procedure as indicated.

Exclusion Criteria:

1. Patient is a prisoner.
2. The participant, in the judgment of the Investigator, may be inappropriate for the intended study procedures

Outcomes:
Peripheral TBBx and CBx Co-primary Endpoints; Peripheral Lung Transbronchial Fine Needle Aspiration Endpoint; Lymph Node Endobronchial Ultrasound Transbronchial Fine Needle Aspiration Endpoint
Representations of Transplantation in Patients With Mucoviscidosis, in Their Assistants and in Their Medical Persons
Trial ID: NCT01285882
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2009-06
Completion date: 2012-12
Sponsor: Nantes University Hospital
Conditions: Cystic Fibrosis
Interventions: Representations
Locations: Nantes University Hospital
Countries: France
Enrollment: 14
Age range: 18 Years - None
Sex: ALL

Summary:
Main objective : This project aims to have a better understanding of the influence of representations of patients with mucoviscidosis, their assistants and their medical persons in the choice of patients to accept or not lung transplantation.

Specific objectives :

1. Identify the knowledge, representations, or feelings of patients which could constitute bridles or factors of motivation to use for transplantation.
2. Identify the knowledge, representations, or feelings of medical persons which could constitute bridles or factors of motivation to the "encouraging" proposal to patients of lung transplant.
3. Identify the knowledge, representations, or feelings of assistants which could constitute bridles or factors of motivation to the "encouragement" of lung transplantation at its close.
4. Identify the needs of patients, assistants and medical persons to successfully reduce these bridles to transplantation and increase the factors of motivation.
5. Compare the representations of patients, assistants and medical persons
6. Search representations associated with patients who refuse the transplantation and those who accept the transplantation

Eligibility criteria:
Inclusion Criteria:

* For patients :

  * Adult patient (those concerned by the suggestion of lung transplant).
  * At the time of transplant indication : after the first suggestion of the medical person, before or after the preoperative graft check-up but before registration on waiting list.
* For medical persons :

  * Medical persons involved in the suggestion of lung transplantation to the patients.
* For assistants :

  * Adult close to the patient and that the patient sees as a resource person and advisor in his choices for the transplant : reference person having a major influence for the patient (spouse, parent, sibling, friend ...)

Exclusion Criteria:

* For patients :

  * Patient with a psychiatric disorder
  * Patient in intensive care unit
  * Palliative care patient
  * Patient aged under 18 years old
  * Patient refusal
* For medical persons :

  * Medical person refusal
* For assistants :

  * Assistant or patient refusal

Outcomes: