moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
The Impact of System Wide Pulmonary Embolism Screening Program on PERT Activation, Utilization of Advanced Therapies, and 90-Day Outcomes
Trial ID: NCT06246045
Status: WITHDRAWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-01-01
Completion date: 2026-01-01
Sponsor: University Hospitals Cleveland Medical Center
Collaborators: Inari Medical
Conditions: Pulmonary Embolism Subacute Massive
Interventions: Walking test
Locations: University Hospitals Cleveland Medical Center
Countries: United States
Enrollment: 0
Age range: 18 Years - 99 Years
Sex: ALL

Summary:
Pulmonary embolism (PE) remains a high mortality and morbidity disease state. The investigators have previously shown that use of a Pulmonary Embolism Response Team (PERT) can improve overall readmission, bleeding, and mortality outcomes. Unfortunately, PERT may still be underutilized from a national standpoint and may not be readily available in underserved areas.

The use of artificial intelligence (AI) may help streamline and systematically ensure unbiased mechanism for activation of PERT for discussion of patients with siginficant clot burden and hemodynamic abnormalities. AI algorithms have been FDA approved for use of triage of the PE patient. The institutional PERT program will adapt the use of an AI algorithm for activation as routine care; the efficiency of activation will be compared to our retrospective historical comparison for efficiency and appropriateness of activation.

The active phase of the study is designed to further differentiate between patients who are considered to be intermediate-high risk category but yet do not clearly qualify for invasive therapy (catheter-directed therapy, systemic thrombolysis, or invasive hemodynamic support). These patients will undergo walking test to further understand noninvasive hemodynamic compromise and undergo 2:1 randomization to early-invasive strategy versus mtranditional medical therapy.

Eligibility criteria:
Inclusion Criteria:

* Patients obtaining a CT at University Hospitals found to have a pulmonary embolism with evidence of right ventricular strain (RV:LV \> 0.9) along with an abnormal vital sign (HR \> 110 bpm, RR \> 30/min, SBP \< 100 mmHg, or O2 saturation \< 90%)
* Patients undergoing PERT discussion as deemed appropriate by the provider

Exclusion Criteria:

* Age \<18
* Pregnant patients. Women of childbearing age will be asked if they are pregnant before enrollment. No formal pregnancy test will be performed as a part of this study. If a patient does require invasive intervention, as part of the routine procedure for cardiac catheterization laboratory, women of childbearing age will either undergo pregnancy testing or opt-out per their own discretion
* Patients unable to perform 6MWT (e.g., lack of one or both legs) will not be included in the randomization. They will be included in the observation for routine treatment

Outcomes:
Time-to-activation
Motivational Evaluation and Intervention With Co-Adapt Application for Healthy Ageing
Trial ID: NCT05116553
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-11-11
Completion date: 2022-01-20
Sponsor: Idego srl
Conditions: Motivation; Self Efficacy
Interventions: SDT; APP + CA
Locations: Idego srl
Countries: Italy
Enrollment: 74
Age range: 50 Years - 67 Years
Sex: ALL

Summary:
The aim of this study is to identify effective methodologies to help people to improve their awareness of change, motivation, self-confidence and self-efficacy, compliance in behavioral change and psychological well-being.

Eligibility criteria:
Inclusion Criteria:

* whitecollar
* a sufficient level of education to understand study procedures and be able to communicate with site personnel
* 50 to 70 years old

Exclusion Criteria:

\- current use of narcotics or other substances

Outcomes:
Change in levels of General Self-Efficacy Scale-10 (GSE-10); Change in levels of Basic Psychological Need Satisfaction and Frustration Scale (BPNSFS)
Artificial Intelligence in Categorizing the Severity of Oxygenation by Incorporating the PEEP Into the Definition of ARDS in Ventilated Patients
Trial ID: NCT05371314
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-12-01
Completion date: 2023-01-30
Sponsor: National University Health System, Singapore
Conditions: ARDS
Interventions: P/FP ratio
Locations: National Universty Health System
Countries: Singapore
Enrollment: 5000
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
Artificial Intelligence in categorizing the severity of Oxygen in ventilated patients to predict initiation of treatment measures to improve mortality - P/FP ratio

Eligibility criteria:
Inclusion Criteria:

1\. Intubated patients

Exclusion Criteria:

1. pediatric and neonates
2. DNR patients
3. Non intubated patients

Outcomes:
Artificial intelligence in categorizing the severity of oxygenation of ARDS in ventilated patients
The Role of Wolters Pulpitis Classification, Apical Periodontitis, and Artificial Intelligence-Assisted Diagnosis in Predicting the Success of Full Pulpotomy in Symptomatic Permanent Mature Molars: A Randomized Controlled Trial
Trial ID: NCT07340606
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-01-17
Completion date: 2026-09-01
Sponsor: Misr International University
Conditions: Pulpitis - Irreversible
Interventions: Conventional root canal treatment; Full pulpotomy
Locations: Misr International University (MIU); Misr International University
Countries: Egypt; Egypt
Enrollment: 58
Age range: 20 Years - 45 Years
Sex: ALL

Summary:
This study aims to evaluate the effect of prognostic factors, such as the severity of pulpal inflammation, carious pulp exposure, and the presence of apical periodontitis, on the success of full pulpotomy in mature permanent molars with irreversible pulpitis. It also aims to investigate the role of artificial intelligence-assisted diagnosis in predicting the prognosis of full pulpotomy.

Eligibility criteria:
Inclusion Criteria:

* Mature permanent molars with irreversible pulpitis.
* Presence of bleeding from all canals.
* The tooth is not periodontally compromised.

Exclusion Criteria:

* Non-vital teeth.
* Uncontrolled bleeding from any of the canals after ten minutes of application of hemostatic agent.
* Molars with immature roots.

Outcomes:
Percentage of Successful Cases; Diagnostic Accuracy (Sensitivity and Specificity) of an artificial intelligence software in detecting carious pulp exposure
Retrospective Data Collection for SENSING-AI: a Wearable Platform for the Early Diagnosis of Emotional Disorders and Exacerbations in Patients With Long COVID Through the Use of Artificial Intelligence
Trial ID: NCT06247332
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-01-18
Completion date: 2022-02-25
Sponsor: Adhera Health, Inc.
Collaborators: Hospital Universitario Virgen Macarena
Conditions: Post-acute COVID-19 Syndrome
Interventions: Review of available clinical data sources related to use cases
Locations: Virgen Macarena University Hospital
Countries: Spain
Enrollment: 103
Age range: 18 Years - None
Sex: ALL

Summary:
The retrospective study will be used to develop an artificial intelligence model of risk stratification of physiological and psychological complications arising from the information available in the electronic medical record and first consultation report to support patients and healthcare professionals in better managing the healthcare process for patients diagnosed with long COVID.

Eligibility criteria:
Inclusion Criteria:

* Legal adult
* Diagnosed of long COVID-19 in the last year
* With the presence of any of these symptoms:
* Asthenia (Tiredness)
* Dyspnea
* Shortness of breath
* Anxiety
* Stress
* Depression
* Sleep disorder

Exclusion Criteria:

* Attended to specialized care consultation
* Was admitted in hospital in the last year due to a problem not related to the COVID complications

Outcomes:
Retrospective SENSING-AI cohort
A Retrospective Cohort Study of Multimodal Electrocardiography and Chest Radiography for Prediction of Progression to Moderate or Severe Regurgitant Valvular Heart Disease
Trial ID: NCT07573852
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2017-07-14
Completion date: 2025-12-31
Sponsor: Shanghai Zhongshan Hospital
Conditions: Valvular Heart Diseases; Valvular Heart Disease Stenosis and Regurgitation; Regurgitation, Aortic Valve; Regurgitation, Mitral
Locations: Zhongshan Hospital
Countries: China
Enrollment: 116380
Age range: 18 Years - None
Sex: ALL

Summary:
This retrospective multicenter cohort study aims to develop and validate an artificial intelligence model integrating electrocardiography (ECG) and chest radiography (CXR) to predict future progression of regurgitant valvular heart disease (rVHD), including aortic, mitral, and tricuspid regurgitation. Adult patients with ECG, CXR, and echocardiography obtained within 60 days, together with follow-up echocardiographic data, are included. The primary objective is to determine whether multimodal ECG+CXR modeling improves prediction of progression to moderate or severe regurgitation beyond ECG-only or CXR-only models. Secondary objectives include evaluation of clinical utility, risk stratification, and model interpretability. This study is intended to assess whether routinely acquired ECG and CXR can be used to support surveillance echocardiography and risk-directed management in patients at risk of future rVHD progression.

Eligibility criteria:
Inclusion Criteria:

Adult patients aged ≥18 years. Underwent routine electrocardiography (ECG), chest radiography (CXR), and echocardiography within 60 days.

Had subsequent follow-up echocardiographic data available for outcome ascertainment.

Patients were identified from routine clinical practice in inpatient admission or outpatient evaluation settings.

Exclusion Criteria:

Age below the adult threshold. Missing any of the required baseline examinations (ECG, CXR, or echocardiography within 60 days).

No follow-up echocardiographic data available. For future-risk evaluation in the test cohort, samples with moderate or severe regurgitation at baseline were excluded.

Outcomes:
Incident Regurgitant Valvular Heart Disease
Effect of Early Mobilization Exercises Guided by Wearable Technology on Functional Capacity After Coronary Artery Bypass Graft
Trial ID: NCT06801314
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-02-01
Completion date: 2025-09-30
Sponsor: Cairo University
Conditions: Coronary Artery Bypass Graft CABG
Interventions: aerobic exercise using cycle ergometer in addition to usual care; resisted exercise using weights and dumbbells in addition to usual care; usual care using mobilization and breathing exercise
Locations: Faculty of physical therapy, Cairo university
Countries: Egypt
Enrollment: 84
Age range: 45 Years - 65 Years
Sex: ALL

Summary:
purpose: This study aims to assess the effects of early mobilization exercises guided by wearable technology on functional capacity after coronary artery bypass graft Methods: Patients will be randomly assigned into three groups: Group A will receive usual care, Group B will receive usual care in addition to aerobic exercise, and Group C will receive usual care in addition to resistance exercise. both the aerobic exercise group and the resistance exercise group will be guided by wearable artificial intelligence ECG monitors for real-time support and guidance, and they will stop exercise when any adverse events occur.

Exercises will be started early from post operative CABG until patients discharge and measurements will be taken at baseline and before discharge

Eligibility criteria:
Inclusion Criteria:

1-post -operative clinically stable patients with hemodynamic stability through measurement of respiratory rate( \>8\<30) breaths/min , Heart rate (\>40\<130) beats /min , Spo2\>90% 2-both lower limbs will be physically functional 3-CABG surgery 4-Their age will be ranged from 45-65 years old

\- Exclusion Criteria:

1. Respiratory insufficiency after surgery manifesting hypoxemia with partial pressure of oxygen in arterial blood \<60 mmHg
2. Cardiogenic shock
3. acute renal failure
4. contra-indicated to submaximal exercise test
5. cardiac arrhythmia
6. locomotor/neurological limitation to ambulation
7. physiological compensatory tachycardia in case of (anemia, high tempreture and infection)
8. high blood pressure at rest (systolic blood pressure \>160mmHg or diastolic blood pressure \>100 mmHg -

Outcomes:
Functional capacity
A New Conception About Individualized Treatment Allocation for HCC-Using Machine Learning
Trial ID: NCT06008548
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2010-01-01
Completion date: 2021-10-01
Sponsor: Tang-Du Hospital
Collaborators: Air Force Military Medical University, China; Army Medical University, China; The First Affiliated Hospital of Shanxi Medical University; Sun Yat-sen University; Shaanxi Provincial People's Hospital; Xijing Hospital; Xi'an Jiaotong University
Conditions: HCC; Transarterial Chemoembolization; Resection
Interventions: TACE
Enrollment: 4991
Age range: 18 Years - 100 Years
Sex: ALL

Summary:
The current guidelines on hepatocellular carcinoma (HCC) aimed to build effective prognostic stratification strategies to guide therapeutic allocation; however, the current guidelines did not consider the simultaneous comparison of distinct therapies in similar populations. Here, the investigators aimed to develop and validate a new, integrated prognostic scheme for HCC patients using artificial intelligence (AI) to simulate the survival outcomes of patients allocated to different treatments.

Eligibility criteria:
Inclusion Criteria

* Diagnosed with HCC
* Receiving LR or TACE therapy
* Complete clinical information
* Preserved liver function (Child-Pugh Score \[CPS\] ≤ 7)
* PS 0-1

Exclusion Criteria

* Vascular invasion or extrahepatic spread;
* Ecompensated liver cirrhosis ;
* Younger than 18 years;
* Duration of follow-up of fewer than 30 days.

Outcomes:
overall survival
Visualization Engineering Platform for TCM Pulse Diagnosis - Pulse Diagnosis Based on Federated Learning to Diagnose Slippery and Choppy and Other Pulses Waveform Image Features to Assist in the Study of TCM Pathological Logic Analysis
Trial ID: NCT05630248
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-12
Completion date: 2024-09
Sponsor: China Medical University Hospital
Conditions: Slippery Pulse; Choppy Pulse
Interventions: Recurrent Neural Network
Locations: China Medical University Hospital
Countries: Taiwan
Enrollment: 240
Age range: 20 Years - 90 Years
Sex: ALL

Summary:
The diagnoses processes of Traditional Chinese Medicine (TCM) focus on the following four main types of diagnoses methods consisting of inspection, olfaction, inquiry, and palpation. The most important one is palpation also called pulse diagnosis which is to measure wrist artery pulse by TCM doctor's fingers to detect patient's health state. The pulse diagnosis has three parts, namely 'Chun', 'Guan' and 'Chy', with the location. Wrist measurements correspond to different parts of the body's organs. In this project, it is to classify pulse types by using specialized pulse measuring instruments. The measured pulse wave (Measured Pulse Wave, MPW) was segmented into arterial pulse wave curves (APWC) by the image suggestion method. The research object of this project is to collect and group patients diagnosed by traditional Chinese medicine practitioners, namely slippery pulse, choppy pulse group and normal pulse control group, with at least 80 cases for each group. The research purpose of this project is mainly to carry out the visualization engineering platform of TCM pulse diagnosis - based on the pulse diagnosis of federated learning to diagnose the pulse waveform image features such as slippery pulse and choppy pulse to provide auxiliary TCM pathological logic analysis research and back-end cross-federal learning of TCM pulse diagnosis Implementation of the node system. In other words, it is expected that the pulse wave characteristics measured by TCM physicians who cooperate with experts in the field can be collected from many TCM pulse diagnosis federated learning nodes, and analyzed by the Multiple-Expert Repertory Grid Elicitation (MERGE) method. Finally, the artificial intelligence model based on FL is trained to carry out TCM pathological logic analysis and related research. The results will be provided to TCM physicians as an important reference to assist clinical diagnosis.

Eligibility criteria:
Inclusion Criteria:

1. Already sign test consent permit.
2. More than 20-year-old.

Exclusion Criteria:

1\. There is a wound or inflammation at the wrist skin measurement.

Outcomes:
Slippery Pulse
An Artificial Intelligence System for Multimodal, Multi-class Diagnosis of Pancreatic Cystic Lesions Based on Endoscopic Ultrasonography
Trial ID: NCT07543263
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-04-20
Completion date: 2028-06-30
Sponsor: Qilu Hospital of Shandong University
Collaborators: Liaocheng People's Hospital; Taian City Central Hospital; Qilu Hospital of Shandong University (Qingdao); Binzhou People's Hospital; Shandong Provincial Hospital; The Affiliated Hospital of Qingdao University; Qianfoshan Hospital; Shengli Oilfield Hospital; Binzhou Medical University
Conditions: Pancreatic Cystic Lesion (PCL)
Interventions: iEUS-PCL(intelligent endoscopic ultrasound system- pancreatic cystic lesion)
Locations: Qilu Hospital of Shandong University
Countries: China
Enrollment: 176
Age range: 18 Years - None
Sex: ALL

Summary:
The aim of this study is to develop and validate an artificial intelligence system named iEUS-PCL (intelligent endoscopic ultrasound system-pancreatic cystic lesions） for detecting and multimodal, multi-class diagnosing pancreatic cystic lesions (PCL) during endoscopic ultrasound (EUS) examination.

Eligibility criteria:
Inclusion Criteria:

\- 1. Patients aged ≥18 years scheduled for EUS with suspected pancreatic cystic lesions based on clinical symptoms, medical history, laboratory tests or radiological examinations, and who agree to participate in the research and voluntarily sign the informed consent.

2\. Patients with no prior history of treatment for pancreatic lesions.

Exclusion Criteria:

\- 1. Patients with absolute contraindications to EUS examination. 2. Pregnancy or lactating. 3. Uncorrectable coagulopathy(PTT\>50 seconds or INR\>1.5) and/or uncorrectable thrombocytopenia(platelet count\<50×109/L). 4. Upper gastrointestinal obstruction. 5. Patients who underwent surgical treatment or anatomical alterations of the pancreas due to lesions in other thoracic and/or abdominal organs, as well as patients with congenital anatomical abnormalities.

6\. Patients who have undergone biliary/pancreatic duct stent placement. 7. Patients who refuse to sign the informed consent.

Outcomes:
The accuracy of iEUS-PCL for pancreatic cystic lesions; The sensitivity of iEUS-PCL for pancreatic cystic lesions; The specificicy of iEUS-PCL for pancreatic cystic lesions; The postive predictive value of iEUS-PCL for pancreatic cystic lesions; The negative predictive value of iEUS-PCL for pancreatic cystic lesions
Histological Validation of dGEMRIC Indices as a Quantitative Biomarker for Cartilage Damage in the Hip Joint
Trial ID: NCT06185036
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-09-01
Completion date: 2025-12-31
Sponsor: Insel Gruppe AG, University Hospital Bern
Conditions: Cartilage Damage; Osteoarthritis, Hip
Locations: University Hospital of Bern (Inselspital)
Countries: Switzerland
Enrollment: 25
Age range: 18 Years - None
Sex: ALL

Summary:
In this study, the investigators seek to validate 3D model-based dGEMRIC (delayed gadolinium enhanced MRI of cartilage) values against histologic grading of cartilage quality, using osteochondral samples of the femoral head collected during hip replacement surgery.

Eligibility criteria:
Inclusion Criteria:

* Patients with advanced hip osteoarthritis.
* Preoperative hip MRI including biochemical sequence (MP2RAGE).
* Indication for hip replacement.

Exclusion Criteria:

* Patients without informed consent.
* Age under 18 years.
* Insufficient language understanding in German, French, or English.
* Previous hip surgeries.
* Neoplastic and inflammatory comorbidities of the hip.
* Post-traumatic or pediatric deformities.
* Avascular necrosis of the femoral head.
* Inadequate MRI image quality (motion artifacts, extra-articular contrast media administration, delay (\>45 minutes) between contrast injection and MRI imaging).

Outcomes:
Correlation coefficient of dGEMRIC indices and histological cartilage quality
Better Real-time Information on Documentation of Goals of Care for Engagement in Serious Illness Communication
Trial ID: NCT07147023
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-09-15
Completion date: 2026-10-31
Sponsor: Dana-Farber Cancer Institute
Conditions: Advanced Cancer; Cancer; End of Life
Interventions: LLM SIC Summary Email
Locations: Brigham and Women's Hospital; Dana-Farber Cancer Institute
Countries: United States; United States
Enrollment: 60
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this study is to test the accuracy of Large Language Model-generated serious illness communication (SIC) summaries, the feasibility of delivering the SIC summaries, and to collect perspectives on the SIC summaries from clinicians and participants with cancer. Large Language Models (LLMs) are artificial intelligence programs that can perform various natural language processing tasks.

Eligibility criteria:
Participant Inclusion Criteria:

* Dana-Farber Cancer Institute participants
* Admission to an inpatient solid tumor medical oncology service at BWH (including beds that are considered to be DFCI beds within BWH)
* Age ≥ 18 years
* Mortality prediction ≥ 40%
* Admitted on a Sunday after 4pm, Monday or Tuesday before 4pm. These time restrictions are necessary for the workflow of this pragmatic pilot trial, as an individual will need to send out the intervention emails and interview patients approximately 48-72 hours after admission, both of which need to occur during the work week.

Participant Exclusion Criteria:

-Patients with elective inpatient admissions (typically for chemotherapy or other treatments)

Clinician Inclusion Criteria:

-The clinician fills one of the following roles for the enrolled patient:

* inpatient attending on the medical oncology team
* outpatient Dana-Farber medical oncologist

Outcomes:
Serious Illness Conversation (SIC) summary accuracy