moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Study of Delivery Outcomes After AIDA (Artificial Intelligence Dystocia Algorithm) Analysis
Trial ID: NCT06664112
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-09-30
Completion date: 2026-06-30
Sponsor: Centro di Ricerca Clinica Salentino
Conditions: Delivery Complication; Delivery Problem; Delivery; Injury; Delivery; Injury, Maternal; Delivery;Breech;Stillbirth; Delivery Problem for Fetus
Interventions: AIDA Analysis
Locations: Ospedale Veris delli Ponti; Andrea Tinelli
Countries: Italy; Italy
Enrollment: 1000
Age range: 20 Years - 45 Years
Sex: FEMALE

Summary:
Aims:

Primary aim is to investigate outcomes of eutocic labor, evaluating intrapartum ultrasound parameters by AIDA method: Angle of progression (AoP), Asynclitism degree (AD), fetal head-symphysis distance (HSD), and midline angle (MLA).

Secondary aim is to investigate outcomes of dystocic labor, evaluating intrapartum ultrasound parameters by AIDA method: Angle of progression (AoP), Asynclitism degree (AD), fetal head-symphysis distance (HSD), and midline angle (MLA).

Tertiary aim is to investigate of neonatal outcomes of eutocic labor: Apgar scores at 1 min, Apgar scores at 5 min.

Quaternary aim is to investigate of neonatal outcomes of dystocic labor: Apgar scores at 1 min, Apgar scores at 5 min.

Eligibility criteria:
Inclusion Criteria:

All patients in pregnancy, nulliparae, candidates for spontaneous or induced labor, monitored by intrapartum ultrasound, collecting the ultrasound parameters of the labor progress.

1. pregnants in labor, at first pregnancy
2. gestational age ≥37 weeks of gestation

Exclusion Criteria:

Multiparas who are candidates for cesarean section, premature labor and patients who do not agree to participate in the study (missing data relevant for the study). Exclusion criteria encompassed: breech, transverse, or oblique presentations; twin pregnancies; abnormal placental implantation; HELLP syndrome; coagulation disorders; uterine hyperstimulation (often referred to as "terrified uterus"); non-reassuring fetal heart rate; thick meconium; and cephalopelvic disproportion.

1. Patients who are candidates for cesarean section.
2. Patients in premature labor.
3. Patients who do not agree to participate in the study.
4. Exclusion criteria encompassed: breech, transverse, or oblique presentations; twin pregnancies; abnormal placental implantation; HELLP syndrome; coagulation disorders; uterine hyperstimulation (often referred to as "terrified uterus"); non-reassuring fetal heart rate; thick meconium; and cephalopelvic disproportion
5. Missing data relevant for the study.

Outcomes:
Labor outcomes
The Efficiency of Writing Endoscopic Reports by Artificial Intelligence and Physicians: a Randomized Controlled Trial
Trial ID: NCT04275609
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-02-20
Completion date: 2020-03-30
Sponsor: Shandong University
Conditions: Gastrointestinal Disease
Interventions: AI generated report; physicians writing report
Enrollment: 80
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
The purpose of this study is to compare the efficiency of writing endoscopic reports by artificial intelligence and physicians through a randomized controlled trial.

Eligibility criteria:
Inclusion Criteria:

* patients aged between 18 and 80 years；
* patients undergoing gastroscopy and colonoscopy examination with anesthesia；
* patients able to give informed consent

Exclusion Criteria:

* patients with the contraindications to endoscopic examination;
* a history of gastrointestinal cancers;
* allergic to anaesthetics in previous medical history;
* the endoscopic procedure cannot be completed due to stenosis, obstruction, solid food or complications of anaesthesia.

Outcomes:
The time spent writing reports
Prospective Collection of PillCamTM SB3 Capsule Endoscopy and Device-Assisted Enteroscopy Procedures Data for Development of an Artificial Intelligence Software System to Apply to Endoscopy Procedures (GPS Study)
Trial ID: NCT05450393
Status: WITHDRAWN
Study type: OBSERVATIONAL
Start date: 2024-05-01
Completion date: 2026-04-30
Sponsor: Medtronic - MITG
Conditions: Small Intestine Disease; Small Intestine Cancer; Small Intestinal Ulcer Bleeding; Small Intestine Obstruction; Small Intestine Adenocarcinoma; Small Intestine Polyp
Enrollment: 0
Age range: 18 Years - None
Sex: ALL

Summary:
A prospective, multicenter, post-market, minimal risk, observational study designed to collect PillCamTM SB3 capsule endoscopy (CE) procedures and subsequent device-assisted enteroscopy (DAE) procedures data (double balloon enteroscopy (DBE), single balloon enteroscopy (SBE) and spiral enteroscopy (SE)) in medical centers in the United States (US).

Eligibility criteria:
Inclusion Criteria:

1. Adult male and female subjects (≥18 years)
2. The subject underwent a SOC PillCamTM SB3 CE procedure and is referred to a subsequent DAE procedure(s), as part of the routine care or The subject underwent a SOC PillCamTM SB3 CE procedure (not necessarily followed by a DAE) and abnormal findings were indicated in the CE report (optional).
3. The subject received an explanation and understands the nature of the study and provided oral consent.

Exclusion Criteria:

1\. The subject is considered to be part of a vulnerable population (e.g. prisoners or those without sufficient mental capacity).

Outcomes:
Prospective collection of PillCamTM SB3 capsule endoscopy and DAE procedures data
PeRfOrmance and ACcuracy of an artifiCial Intelligence Enhanced Smart Watch Single Lead ECG (PROCESS)
Trial ID: NCT05450809
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-11-05
Completion date: 2022-11-02
Sponsor: Mayo Clinic
Conditions: Healthy
Locations: Mayo Clinic
Countries: United States
Enrollment: 1000
Age range: 18 Years - 89 Years
Sex: ALL

Summary:
The purpose of this study is to show the artificial intelligence enhanced single-lead ECG Apple Watch has similar, robust performance comparable to an AI enhanced 12 lead ECG and AI enhanced single lead (LI) of a 12 lead ECG.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18 years and ≤ 89.
* Able to give verbal consent.
* Able to complete routine clinical 12 lead ECG tracing and single lead Apple Watch ECG tracing.

Exclusion Criteria:

* Individuals \< 18 and \> 89 years of age.
* Unable to given verbal consent.

Outcomes:
Comparison of 12 lead ECG features to single-lead watch-based ECG features; Arrhythmia comparison of 12 lead ECG to single-lead watch-based ECG
Effectiveness of Artificial IntelliGence-Driven Single-LEad Long-TerM Electrocardiograms MonItoring in Detecting New-Diagnosed Atrial FIbrillation
Trial ID: NCT06842147
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-03-01
Completion date: 2028-07-30
Sponsor: Beijing Anzhen Hospital
Conditions: Atrial Fibrillation (AF)
Interventions: 7-day single-lead long-term ECG monitoring device.; Standard Screening Group
Locations: Beijing Anzhen Hospital
Countries: China
Enrollment: 15360
Age range: 60 Years - None
Sex: ALL

Summary:
Abstract Purpose: Atrial fibrillation (AF) is a leading cause of stroke and heart failure, yet detection remains suboptimal in rural settings due to limited resources. This study evaluates whether an enhanced screening strategy using artificial intelligence (AI)-integrated 7-day single-lead electrocardiogram (ECG) patches improves AF detection and long-term clinical outcomes compared to routine care in rural China.

Methods: This cluster-randomized trial will be conducted across 128 village clinics in Quzhou, Zhejiang Province. Villages are randomized 1:1 to either enhanced or routine screening. Participants aged 60 years or older (approximately 120 per village) in both arms receive family-centered AF education and opportunistic assessments. The enhanced group undergoes screening via 7-day single-lead ECG patches, while the routine group utilizes standard 12-lead ECGs.

Results: The trial features two primary endpoints. The Phase 1 endpoint is the newly diagnosed AF detection rate during a 1-year screening period. The Phase 2 endpoint is a 3-year composite outcome of all-cause mortality, stroke or systemic embolism, and hospitalization for heart failure.

Conclusion: By integrating wearable AI technology into primary care, this trial seeks to overcome diagnostic barriers in resource-limited environments. The findings will determine if prolonged digital monitoring can significantly enhance AF detection and reduce major cardiovascular events in elderly rural populations.

Eligibility criteria:
Inclusion Criteria:

Age 60 years or older No previous history of atrial fibrillation (AF) Willing to participate in random assignment and follow-up

Exclusion Criteria:

Patients with a pacemaker or implanted cardioverter-defibrillator (ICD) Patients with cognitive impairment or unable to provide informed consent Patients with an estimated life expectancy of less than one year (e.g., advanced cancer or end-stage renal disease) Patients deemed unsuitable for the study by the investigator Patients who refuse to participate

Outcomes:
Detection rate of new atrial fibrillation diagnoses; Composite endpoints
Artificial Intelligence - SARS-CoV-2 Risk Evaluation
Trial ID: NCT04834934
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-02-16
Completion date: 2021-06-30
Sponsor: IRCCS San Raffaele
Collaborators: Regione Lombardia; Orobix Srl; Microsoft Corporation; ASST Bergamo Est; Centro Cardiologico Monzino; NVIDIA Corporation; PORINI Srl
Conditions: Covid19
Locations: IRCCS San Raffaele
Countries: Italy
Enrollment: 2000
Age range: 18 Years - 100 Years
Sex: ALL

Summary:
The management of COVID-19 patients in overwhelmed hospital facing the pandemic is a clinical challenge.

The improvement of decision making may allow a better allocation of available resources and a better treatment of patients at higher risk.

Chest CT has been widely adopted for COVID-19 pneumonia diagnosis. Several experiences documented the capability of Artificial Intelligence to improve and fasten COVID-19 pneumonia detection, mainly using chest X-ray.

Aim of the present study was to develop and validate an Artificial Intelligence approach integrating clinical and imaging data (automatically extracted through the adoption of dedicated neural networks) for the creation of a cloud platform capable of performing automatic patients risk stratification. Such an approach could be used for triage of COVID-19 patients in the emergency department, with the aim to improve healthcare personnel decision-making and allocation of resources during health emergencies.

Eligibility criteria:
Inclusion Criteria:

* confirmed SARS-CoV-2 infection with RT-PCR
* non contrast chest CT scan performed within 72 hours after admission to the emergency department

Exclusion Criteria:

* age \< 18 ys

Outcomes:
Training, testing and validation of an AI platform for predicting Italian first wave Covid-19 patients prognosis.
AI Integration in Academic Writing: a Study on Qualitative Research in Physical Therapy Education
Trial ID: NCT06732947
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-12
Completion date: 2025-01
Sponsor: Kafrelsheikh University
Conditions: Artificial Intelligence (AI); Qualitative Research
Locations: Kafrelsheikh University
Countries: Egypt
Enrollment: 100
Age range: 20 Years - 22 Years
Sex: ALL

Summary:
This study aims to explore the effect of AI use on different aspects of writing qualitative research papers by physical therapy students. By examining how AI tools influence various elements of the writing process, such as structure, clarity, knowledge presentation, and critical thinking, this research aims to provide insights into the benefits and potential drawbacks of AI integration in academic assignments. By recognizing the significance of this research, contribute to understanding how AI can be effectively utilized to enhance the educational experience and improve the writing skills of physical therapy students.

Eligibility criteria:
Inclusion Criteria:

* university students between the ages of 20 and 22.

Exclusion Criteria:

* no exclusion criteria

Outcomes:
percentage of AI-generated content within the text of each assignment.; Incidence of Structure & Organization (SO) by Sapling AI detector website; Rate Writing Clarity (WC) by Sapling AI detector website; Rate of Knowledge (Know) by Sapling AI detector website
Longitudinal Recovery Trajectories After an Acute Respiratory Distress Syndrome, a New Understanding. The TENACITY Study
Trial ID: NCT06083363
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-06-29
Completion date: 2026-12-31
Sponsor: Institut de Recerca Biomèdica de Lleida
Collaborators: Instituto de Salud Carlos III
Conditions: ARDS; ARDS Disease Progression
Locations: Hospital de Tortosa Verge de la Cinta; Hospital Universitari Arnau de Vilanova; Hospital Universitari Joan XXIII
Countries: Spain; Spain; Spain
Enrollment: 246
Age range: 18 Years - None
Sex: ALL

Summary:
COVID-19 resulted in the largest cohort of critical illness survivors in history, heightened awareness of the importance of the respiratory sequelae after an acute distress respiratory syndrome (ADRS). Despite the advancement of acute-phase ARDS management, it is unknown whether there are differences in the longitudinal recovery trajectories between patients with post-ARDS due to COVID-19 and due to other causes. The main objective of the study is to identify risk factors of pulmonary sequela (lung diffusing capacity) at long-term follow-up in survivors of ARDS. The investigators are also interested in describing the long-term longitudinal recovery trajectories at a multi-dimensional level (symptoms, quality of life, neurocognitive, other lung function parameters, exercise capacity, chest imaging and molecular profiles) of ARDS survivors, and compared between ARDS caused by COVID-19. The ultimate goal is to understand the pathobiological mechanisms associated with a severe lung injury at the long term, allowing the introduction of clinical guidelines for the management of post-ARDS patients and the assignment of personalized interventions.

Eligibility criteria:
Inclusion Criteria:

* Male and female patients aged ≥18 years
* Admission to the ICU
* Diagnosis of severe pneumonia and/or diagnosis of acute respiratory distress syndrome (ARDS) based on the 2023 definition due to any origin (infectious and non-infectious)

Exclusion Criteria:

* Life expectancy less than a year
* Transfer to another hospital during hospitalization or follow-up
* Stay in palliative care
* Severe mental disability that makes it impossible to carry out pulmonary function tests during follow-up

Outcomes:
Changes in lung diffusing capacity; Changes in lung diffusing capacity; Changes in lung volumes; Changes in lung volumes; Changes in chest CT findings
Developing a Balance Rehabilitation System for Older Adults, Based on Inertial Measurement Unit Sensing and Artificial Intelligence: Personalized Training and Preventive Strategies
Trial ID: NCT06596993
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-11-03
Completion date: 2026-12
Sponsor: National Taiwan University Hospital
Conditions: Community-dwelling Older Adults
Interventions: IMU-based balance training; general health education or exercise training
Locations: National Taiwan University, College of Medicine, School and Graduate Institute of Physical Therapy
Countries: Taiwan
Enrollment: 120
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
The aging physiological state of the elderly may lead to problems such as unstable gait, balance disorders, and falls. Previous research has confirmed that exercise training can help improve the physical function, quality of life, and reduce the risk of falls in the elderly. In order to achieve effective and continuous intervention training, somatosensory games have become a trend in recent years. Among them, the use of non-immersive virtual reality training methods not only provides training for the elderly, but also reduces the discomfort caused by the virtual environment; however, there are some limitations in clinical rehabilitation training methods, such as the lack of data-based evaluation and personalization. In order to solve the above problems, this research plan will use the inertial measurement unit as a tool for clinical monitoring and human movement assessment, and use artificial intelligence technology to evaluate and adjust the training plan according to its gait characteristics to achieve personalization Training and prevention strategies.

Eligibility criteria:
Inclusion Criteria:

Aged between 18 and 80 years capable of independent walking-

Exclusion Criteria:

1. history of lower limb orthopedic surgery, ankylosing spondylitis, rheumatoid arthritis, osteoarthritis, and other medical joint diseases
2. Those who cannot communicate or follow instructions, and those with severe visual or hearing impairments
3. the neurological impairment or vestibular disorders, such as stroke, spinal cord injury, Meniere's syndrome.

Outcomes:
Static Standing Balance Test; Single Leg Standing Test; Five Times Sit to Stand Test; Timed Up and Go Test; Six-Minute Walk Test; Over-ground walking; Walking on a treadmill; Delsys Trigno EMG analysis system; Vicon Bonita; Force plates
Clinical Evaluation of the Scopio Cell Morphology Technology on Pre-classification of Blood Cells in Peripheral Blood (PB) and Bone Marrow (BM)
Trial ID: NCT06574308
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-09-01
Completion date: 2025-09-01
Sponsor: Fondazione Policlinico Universitario Agostino Gemelli IRCCS
Conditions: Blood Cancer
Interventions: Blood cells classification
Locations: Fondazione Policlinico Universitario A. Gemelli IRCCS
Countries: Italy
Enrollment: 300
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
Digital morphology is currently expanding and developing new applications based on artificial intelligence software. We recently published a study comparing screen validation and microscopic reading of bone marrow aspirates. The aim of the current research will be the comparison between optical microscope reading and automatic pre-classification (AI based)

Eligibility criteria:
Inclusion Criteria:

* Patients suffering from hemopathies with bone marrow aspirate assessable under the optical microscope

Exclusion Criteria:

* Patients not affected by blood disorders
* Patients suffering from hemopathies with bone marrow aspirate that cannot be assessed under the optical microscope

Outcomes:
Optical microscope reading and automatic pre-classification
Validity and Reliability of an AI-based Physiotherapy Evaluation System for Oromandibular and Neck-Shoulder Range of Motion in Oral Cancer Patients
Trial ID: NCT07509619
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-04-07
Completion date: 2027-12-31
Sponsor: National Taiwan University Hospital
Conditions: Oral Cancer; AI (Artificial Intelligence)
Locations: School and Graduate Institute of Physical Therapy, College of Medicine, National Taiwan University
Countries: Taiwan
Enrollment: 20
Age range: 20 Years - 70 Years
Sex: ALL

Summary:
This study aims to evaluate the validity and reliability of a novel AI-based physiotherapy evaluation system for measuring oromandibular and neck-shoulder range of motion (ROM). Traditional ROM assessments rely on manual measurements, which may be influenced by rater experience and variability. The proposed AI system uses automated keypoint tracking to provide objective and standardized measurements.

In this cross-sectional study, healthy adult participants will perform standardized ROM tasks. Measurements obtained from the AI system will be compared with those from two independent raters using conventional clinical tools. Repeated measurements will be conducted to assess intra-rater and inter-rater reliability. The agreement between the AI system and human raters will be evaluated to determine the system's clinical applicability.

Eligibility criteria:
Inclusion Criteria:

* Healthy adults aged 20 to 70 years
* No trismus
* No history of head, neck, or shoulder injury or surgery
* No history of head and neck cancer-related radiotherapy or chemotherapy

Exclusion Criteria:

* Inability to communicate or follow instructions
* Any condition that may affect movement performance

Outcomes:
Agreement Between AI and Manual Measurements
Coronavirus Disease 19 Survival - The COVIVA Study
Trial ID: NCT04366765
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-03-19
Completion date: 2021-06-30
Sponsor: University Hospital, Basel, Switzerland
Conditions: COVID-19; SARS-CoV 2
Locations: University Hospital Basel
Countries: Switzerland
Enrollment: 1325
Age range: 18 Years - None
Sex: ALL

Summary:
The COVID-19 pandemic poses a major and imminent challenge for health care systems regarding patient triage and allocation of limited resources worldwide. The involved pathogenetic mechanisms as well as the clinical value of established and emerging biomarkers for early risk prediction are largely unknown.

To fill these gaps in knowledge, investigators designed the prospective, interdisciplinary, observational, case-control "COronaVIrus surviVAl (COVIVA)" study platform, aiming to deliver an open-source platform to i) perform extensive clinical and biomarker phenotyping in COVID-19 suspects presenting to the emergency department (ED) as well as admitted to the intensive care unit, ii) compare clinical and biomarker profiles of COVID-19 patients with a control group, iii) derive and validate personalized risk prediction models for early clinical decision support, and iv) explore pathophysiological mechanisms including but not limited to inflammatory, immunological and cardiovascular pathways.

Blood samples (serum) are routinely collected for bio banking both in cases and controls. Patients are followed 30 days after discharge. Personalized risk prediction models will be derived and validated based on advanced statistical models including machine-based learning incorporating a variety of clinical parameters and biomarker signatures (including digitally stored in-hospital data, e.g. imaging, ECG, ventilation parameters). Close cooperation with multiple other national and international COVID-19 cohorts is endorsed.

The personalized risk prediction models from the COVIVA study will support clinicians in the most challenging process of limited resource allocation in a timely fashion. In addition, pathophysiological mechanisms and differences in mild and severe variants of COVID-19 as well as in the control group can be extensively studied in a multidisciplinary approach.

Eligibility criteria:
Inclusion Criteria:

* Clinically suspected or confirmed SARS-CoV-2 infection triaged to the ED
* SARS-CoV-2 swab test performed (result may be pending at time of study enrolment)
* Age ≥18 years
* Patient or legally authorized representative is willing to sign local General consent form

Exclusion Criteria:

* Patient or legally authorized representative is unable or unwilling to participate in the study.

Outcomes:
short-term prognosis