moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Construction of AI-enabled Models for Predicting the Risk of Sepsis After Major Abdominal Surgery: a Retrospective Multicenter Clinical Study
Trial ID: NCT06684340
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2014-01-01
Completion date: 2024-07-31
Sponsor: Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
Collaborators: Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine
Conditions: Sepsis; Postoperative Complications
Interventions: Exposure to major abdominal surgery
Enrollment: 22646
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this observational study is to identify the risk factors and build the early warning system of sepsis and septic shock after major abdominal surgery based on artificial intelligence. The main questions it aims to answer are:

What are the high risk factors of postoperative sepsis? Which factors can accelerate the progression of sepsis? Researchers will collect perioperative characteristics to construct predictive models of postoperative sepsis in a retrospective abdominal surgical population based on artificial intelligence, and the accuracy of the models were tested in an external dataset.

Eligibility criteria:
Inclusion Criteria：

1. Patients undergoing major abdominal surgery.
2. The surgeon performing the operation has extensive experience in specialized surgeries such as upper gastrointestinal, hepatobiliary and pancreatic, colorectal, gynecological, and urological surgeries, having completed at least 50 corresponding specialized surgical cases.
3. The anesthesiologist performing the operation has more than 5 years of clinical experience.
4. All surgical patients receive general anesthesia.
5. Patients are aged ≥ 18 years. Exclusion Criteria：

1\. Pregnancy-related surgeries (cesarean section, abortion surgery). 2. Patients with known preoperative infection or suspected infection. 3. Severely malnourished patients (BMI reference values are 17 kg/m² for patients \< 70 years old and 17.8 kg/m² for patients \> 70 years old).

4\. Patients who have previously undergone abdominal surgery. 5. Patients without sufficient data, including demographic variables (such as age, gender), surgical details (surgery duration, surgical grade, anesthesia grade, anesthesia duration), and hospitalization details (such as ICU stay, in-hospital mortality).

Outcomes:
Sepsis within 28 days after surgery.
Assessing a Mask Region-based Convolutional Neural Network in Follicle Identification and Measurement During Ovarian Stimulation
Trial ID: NCT04545918
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-02-01
Completion date: 2021-05-20
Sponsor: Cycle Clarity
Conditions: Infertility, Female; Ovarian Follicular Cyst
Interventions: Follicle Clarity
Locations: Coastal Fertility Specialists
Countries: United States
Enrollment: 80
Age range: 21 Years - 42 Years
Sex: FEMALE

Summary:
A prospective cohort trial studying patients with infertility undergoing an ovarian stimulation with exogenous gonadotropins. Ovarian monitoring will be performed with a combination of transvaginal ultrasound and 2 dimensional human measurements of the follicle development on the right and left ovaries along with SonoAVC. Human and SonoAVC measurements will then be compared to mask region-based convolutional neural network in follicle identification and measurement during during the ovarian stimulation.

Eligibility criteria:
Inclusion Criteria:

* Infertile women ages 21-42 years old undergoing ovarian hyperstimulation as part of the treatment care.

Women with at least one ovary visible with transvaginal ultrasound. Women with at least one follicle 10 mm or greater in average diameter.

Exclusion Criteria:

* Women less than 21 years of age or greater than 42 years of age. Women without a visible ovary by transvaginal ultrasound. Women without a follicle at least 10 mm in average diameter

Outcomes:
Median size (mm) of ovarian follicles in CCAI compared to SonoAVC
Detection of Reduced Left Ventricular Ejection Fraction and Atrial Arrhythmias With Single Lead ECG Using Artificial Intelligence
Trial ID: NCT04400435
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-07-15
Completion date: 2022-04-29
Sponsor: Eko Devices, Inc.
Conditions: Left Ventricular Dysfunction
Interventions: Use of Eko DUO electronic stethoscope
Locations: Los Alamitos Cardiovascular; MedStar Cardiovascular Network; Albert Einstein Medical Center
Countries: United States; United States; United States
Enrollment: 1258
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of this research is to prospectively test and validate the single-lead Low EF algorithm in outpatients in order to test the performance of a single-lead ECG based algorithm to identify people with decreased left ventricular EF.

Eligibility criteria:
Inclusion Criteria:

* English-speaking adults who are 18 years and older
* Able and willing to provide informed consent
* Complete a clinical echocardiogram within 7 days before or after study procedures

Exclusion Criteria:

* Unwilling or unable to provide informed consent
* Patients who are hospitalized

Outcomes:
Single-lead ECG based algorithm development
Development of Assist Tool for Breast Examination Using the Principle of Ultrasonic Sensor : A Development and Validation Study
Trial ID: NCT06255808
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-10-05
Completion date: 2025-04-05
Sponsor: Seoul National University Hospital
Conditions: Breast Cancer; Breast Neoplasm; Artificial Intelligence; Ultrasonography; Benign Breast Disease
Locations: Seoul National University Hospital
Countries: South Korea
Enrollment: 200
Age range: 18 Years - 80 Years
Sex: FEMALE

Summary:
The accuracy of breast examinations and ultrasonography performed clinically to detect breast mass varies greatly depending on the physician\&#39;s skill level, and the accuracy of breast examinations by non-experts is particularly low. In this study, we aimed to validate whether the concurrent use of ultrasound sensor technology is an efficient strategy for the purpose of improving the sensitivity of detecting breast masses through breast examination.

Eligibility criteria:
Inclusion Criteria:

* female patients between 18 and 80 years of age who are scheduled for surgery after a tumor has been confirmed on breast ultrasound examination

Exclusion Criteria:

* Patients diagnosed with breast cancer after biopsy with non-mass enhancement or calcification
* Inflammatory breast cancer
* Patients whose cancer has invaded the skin and broken through
* Patients with skin diseases
* Women who refused to participate in the study

Outcomes:
Device performance
Automatic Assessment of Difficult Ventilation and Intubation From Automatic Face Analysis and Artificial Intelligence
Trial ID: NCT02022397
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2012-03
Completion date: 2024-12-23
Sponsor: University of Lausanne Hospitals
Collaborators: Ecole Polytechnique Fédérale de Lausanne
Conditions: Adverse Effect of Other General Anesthetics, Sequela
Locations: Dpt of Anesthesiology, University of Lausanne CHUV
Countries: Switzerland
Enrollment: 6000
Age range: 16 Years - None
Sex: ALL

Summary:
General anaesthesia mandates artificial ventilation and tracheal intubation in order to provide patients with artificial breathing. Difficulties related to ventilation and intubation remain the leading cause of morbidity and mortality in general anaesthesia, essentially due to inaccuracies in pre-operative detection of anatomical factors predisposing to difficult airways. In this project investigators will develop image and video-processing technologies software solutions to allow automatic recognition of anatomical features playing a key role in identification of difficult ventilation and intubation, leading to modifications in pre-operative anaesthesia management assessment and therefore increase patients' safety.

Eligibility criteria:
Inclusion Criteria:

* adult patient (15 years of age)
* patients necessitating endotracheal intubation for general anesthesia

Exclusion Criteria:

-patient refusal

Outcomes:
Computerized classification of difficult intubation
CO-ADAPT: Adaptive Environments and Conversational Agent Based Approaches for Healthy Ageing and Work Ability
Trial ID: NCT04809090
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-03-09
Completion date: 2022-01-30
Sponsor: Tommaso Ciulli
Collaborators: Università degli Studi di Trento
Conditions: Work Related Stress; Psychologic Adaptation
Interventions: SMT; App CA
Locations: Idego srl
Countries: Italy
Enrollment: 60
Age range: 40 Years - 70 Years
Sex: ALL

Summary:
The purpose of this study is to identify effective methodologies to help people improve their ability to adapt to psychological stress.

Eligibility criteria:
Inclusion Criteria:

* whitecollar
* Normal or moderate levels of anxiety and stress
* A sufficient level of education to understand study procedures and be able to communicate with site personnel
* at least 40 to 70 years old

Exclusion Criteria:

* High levels of depression combined with other psychological risk indices (such as suicidal thoughts)
* Current use of narcotics or other substances

Outcomes:
An improvement of the scales contained in the OSI questionnaire; An average improvement in Perceived Stress Scale (PSS) test scores
Evaluating the Effect of the Automatic Surveillance System on Surveillance Rate of Colorectal Postpolypectomy Patients: A Prospective, Multicenter Study
Trial ID: NCT05751824
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-02-26
Completion date: 2029-12-31
Sponsor: Renmin Hospital of Wuhan University
Collaborators: Jiangmen Central Hospital
Conditions: Artificial Intelligence; Surveillance
Interventions: AI based automatic surveillance (AS) system (ENDOANGEL-AS); Manually remind the patients to review.
Locations: Jiangmen Central Hospital; Renmin Hospital of Wuhan University
Countries: China; China
Enrollment: 867
Age range: 18 Years - None
Sex: ALL

Summary:
In this study, we proposed a prospective study about the effect of the automatic surveillance system on surveillance rate of colorectal postpolypectomy patients. The enrolled patients were divided into group A with intelligent surveillance system, group B with manual reminder, and group C with natural state. The surveillance among the three groups were compared.

Eligibility criteria:
Inclusion Criteria:

* Male or female aged 18 years or older who undergo colonoscopy.

Exclusion Criteria:

* 1)No pathological result.
* 2\) No or invalid contact information.
* 3\) The surveillance interval cannot be determined according to the surveillance guidelines, including poor bowel preparation, colorectal cancer or suspicious malignance, surgery or colorectal ESD history, those who fail to complete colonoscopy due to unbearable, pathological indications of not polyps, hamartoma or lymphoma polyps, history of ulcerative colitis, and so on.
* 4\) Has participated in other clinical trials, signed the informed consent form and is in the surveillance period of other clinical trials.
* 5\) Have drug or alcohol abuse or psychological disorder in the past five years.
* 6）Pregnancy.
* 7）Not suitable for recruitment after investigator evaluation because of other high-risk conditions.

Outcomes:
surveillance rate
A Survey of Patient Preferences on Empathetic Communication in Outpatient Palliative Care
Trial ID: NCT06832891
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2025-04-21
Completion date: 2025-06-30
Sponsor: Mayo Clinic
Conditions: Preference, Patient
Interventions: Survey
Locations: Mayo Clinic in Rochester
Countries: United States
Enrollment: 202
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to evaluate whether patients have different preference patterns for empathetic communication through AI vs human-being when knowledge of authorship is known vs blinded.

Eligibility criteria:
Inclusion Criteria:

* Patients over the age of 18 being seen in palliative care clinic who have already established care and with diagnosis of any malignancy

Exclusion Criteria:

* Inability to consent defined as: Acute mental status changes (delirium/encephalopathy), acute substance intoxication, intellectual disability, dementia, patient with active legal guardian
* Major psychiatric disorders including, but not limited to, bipolar disorder, psychotic disorders, active substance use disorder, and patients with active suicidal or homicidal thoughts.

  * NOTE: Patients with history of normal grieving reactions, major unipolar depressive disorder, posttraumatic stress disorder or generalized anxiety disorder would NOT be excluded.
* Inability to read in English

Outcomes:
Preference patterns for empathetic communication
AI-Based Video-Mediated Cognitive Preparation in Skin Prick Testing: A Prospective, Randomized Controlled Trial
Trial ID: NCT07453615
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-03-15
Completion date: 2026-09-15
Sponsor: Antalya Training and Research Hospital
Conditions: Anxiety; Fear Anxiety; Allergic Rhinitis; Asthma (Diagnosis)
Interventions: AI-Generated Storytelling Video; Standard Clinic Care
Locations: Health Sciences University Antalya Training and Research Hospital
Countries: Turkey (Türkiye)
Enrollment: 100
Age range: 4 Years - 10 Years
Sex: ALL

Summary:
This study evaluates the effectiveness of an AI-generated therapeutic storytelling video on pre-procedural anxiety and fear levels in children aged 4 to 10 years undergoing their first Skin Prick Test (SPT). The study aims to determine if watching a cognitive preparation video at home prior to the appointment reduces anxiety compared to standard care. Participants will be randomized into two groups: an intervention group watching the AI-generated video and a control group receiving standard clinic care

Eligibility criteria:
Inclusion Criteria:

* Children aged between 4 and 10 years (from 4th birthday to 11th birthday).
* Children undergoing a Skin Prick Test (SPT) for the first time.
* Presence of suspected Allergic Rhinitis and/or Asthma.
* Cognitive ability to understand Turkish, follow the video story, and interpret visual assessment scales.
* Parents with internet access to view the digital content.
* Obtained parental informed consent and child assent (for ages 7 and above)

Exclusion Criteria:

* Previous experience with Skin Prick Testing or a history of frequent medical procedures (e.g., frequent blood draws, surgeries) due to chronic illness.
* Diagnosed with Autism Spectrum Disorder, severe ADHD(attention deficit hyperactivity disorder), intellectual disability, or severe psychiatric conditions preventing cooperation.
* Significant visual or hearing impairments that prevent engagement with the video content.
* Use of anxiolytics, sedatives, or medications affecting cognitive functions within the last 24 hours.
* Chronic health problems (e.g., malignancy, immunodeficiency) that may elevate baseline anxiety levels.
* Patients undergoing SPT for suspected Food Allergy, Drug Allergy, or Bee Venom Allergy.
* For the intervention group: Failure to watch the video or answering the three "validation questions" incorrectly on the procedure day.

Outcomes:
Baseline Anxiety Level; Acute Procedural Fear Level
"Evaluating the Effectiveness of an AI-Based Software in Personalizing High-Quality End-of-Life Care for the Elderly: A Randomized Controlled Clinical Trial"
Trial ID: NCT07027618
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-07
Completion date: 2026-09
Sponsor: Baqiyatallah Medical Sciences University
Conditions: Artificial Intelligence (AI); Dementia; Palliative Care
Interventions: AI-based Software for Personalized End-of-Life Care
Enrollment: 140
Age range: 60 Years - None
Sex: ALL

Summary:
This study aims to evaluate the effectiveness of an artificial intelligence (AI)-based software in personalizing high-quality end-of-life care for elderly patients. As the elderly population grows, providing tailored and quality care during the final stages of life becomes increasingly important. This AI software continuously monitors vital signs and behaviors through wearable sensors, offers smart medication reminders, alerts the care team to potential risks, and provides personalized care plans along with psychological and social support.

The study is designed as a randomized controlled trial comparing two groups: one receiving standard end-of-life care and the other using the AI software. Key outcomes include improving quality of life, reducing adverse events like falls and emergency hospitalizations, increasing patient and family satisfaction, improving medication management, and reducing caregiver burden. Data will be collected over six months to assess these effects. The results will help determine whether AI technology can enhance end-of-life care for seniors and support families and healthcare providers.

Eligibility criteria:
Inclusion Criteria:

Age ≥ 60 years

Clinical diagnosis of being in the end-of-life stage, based on criteria such as the Karnofsky Performance Scale or Palliative Performance Scale

Informed consent obtained from the participant or legal representative

Ability to use technology independently or with support provided by the research team

Access to necessary equipment for the intervention (e.g., wearable sensors, smartphone/tablet)

Exclusion Criteria:

Presence of severe cognitive impairment preventing software use

Voluntary withdrawal from the study at any stage

Critical medical deterioration or death during the study

Poor adherence or insufficient engagement with the intervention software in the intervention group

Outcomes:
Change in Quality of Life Score
Sedation Level Estimation Based on Machine Learning of Quantitative Occipital Electroencephalogram Features in Gastroenteroscopy Patients
Trial ID: NCT05545397
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-09-15
Completion date: 2022-12-31
Sponsor: General Hospital of Ningxia Medical University
Conditions: Gastritis
Interventions: Propofol; EEG montior
Locations: Ningxia Medical University Cardiovascular and Cerebrovascular Disease Hospital
Countries: China
Enrollment: 50
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
Painless endoscopy technology can make patients comfortable under anesthesia, but because of the painless inside.The diagnosis and treatment time of endoscopic examination is short, and the transport is fast. Anesthesia related wind such as deep breathing depression and hypoxemia will occurRisks.Eeg depth monitoring can assist anesthesiologists to evaluate the depth of anesthesia and reduce the risk. Artificial intelligence is adopted.There are few reports on the evaluation of anesthesia depth and drug dosage by electroencephalogram (EEG) monitoring in outpatient patients with painless gastroenteroscopy.

Eligibility criteria:
Inclusion Criteria:

1. Painless gastroenteroscopy patients
2. ASA classification is Ⅰ-Ⅱ
3. Patients who are willing to cooperate
4. No schizophrenia,epilepsy and other neurological diseases

Exclusion Criteria:

1. allergic to propofol
2. Patients who are unwilling to cooperate
3. Patients with psychiatric disorders
4. Patients using sedative drugs for a long time
5. Patients with neuropsychiatric disorders
6. Patients with severe heart, liver, kidney and respiratory diseases

Outcomes:
Electroencephalogram features; Propofol dosage
Role of Artificial Intelligence in Predicting Muscle Fatigue Using Virtual Reality Training In Healthy And Post COVID19 Subjects
Trial ID: NCT05813613
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-04-15
Completion date: 2023-06-07
Sponsor: Beirut Arab University
Conditions: Fatigue
Interventions: Squatting with the aid of Kynapsis Virtual Training apparatus.
Locations: Ahmad ElMelhat
Countries: Lebanon
Enrollment: 90
Age range: 18 Years - 49 Years
Sex: ALL

Summary:
The goal of this observational predicted study is to predict muscle fatigue using a specific AI algorithm in healthy vs post Covid-19 infected individuals. The main question it aims to answer is:

Can Artificial Intelligence be used as a reliable source of predicting localized muscle fatigue in healthy vs post Covid-19 infected individuals?

Participants will be divided into two groups: A healthy group and a post Covid-19 group.

* Each group will undergo a familiarization process before the start of the exercises.
* Then, each group will perform squatting exercises guided by the kynpasis virtual reality apparatus.
* sEMG for the vastus lateralis and rectus femories, chest expansion, and goniometric measurements of the knee will be taken during different reported fatigue levels using the Biopac system.
* Groups will continue squatting while recording their subjective fatigue levels using the Borg scale.
* Data will then be run through machine learning processes to produce an AI algorithm capable of predicting isolated muscle fatigue.

Eligibility criteria:
Inclusion Criteria:

* Non-athletic healthy individuals.
* Avoided intense activities in the past 3 days.
* Confirmed positive PCR test done within an interval of 1 year for Covid-19 group subjects.

Exclusion Criteria:

* Being old age geriatrics (more than 50 years old).
* Having any respiratory, cardiac, renal, neuromuscular, orthopedic, and musculoskeletal disorders.
* Smokers and some medicinal drug users must be taken into consideration because it affects the performance and increases the fatigue levels.
* Subjects not meeting any of the inclusion criteria.

Outcomes:
Surface electromyography; The Borg Rating of Perceived Exertion (RPE) scale
Assessing the Performance of Artificial Intelligence (AI)-Augmented Electronic Health Record (EHR) Data Abstraction for Clinical Trial Patient Screening
Trial ID: NCT06561217
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-08-18
Completion date: 2024-07-12
Sponsor: University of Pennsylvania
Collaborators: Mendel AI
Conditions: Cancer
Interventions: Chart review
Locations: Emory University; University of Pennsylvania
Countries: United States; United States
Enrollment: 355
Age range: None - None
Sex: ALL

Summary:
Identifying eligible patients is a key process in the clinical trial enterprise. Currently, this process relies on time-intensive manual chart review, creating a rate-limiting step for trial participation. The integration of AI technology into the trial screening process has potential to improve participation rates. This study aims to assess the performance (accuracy, efficiency) of AI-augmented patient identification and inform optimal integration into clinical research screening processes.

Eligibility criteria:
Inclusion Criteria:

* Diagnosis of colorectal or non-small cell lung cancer.
* A minimum of 5 patient documents in the Mendel database.
* Most recent document was within 5 years from the time of data extraction.

Exclusion Criteria:

* None.

Outcomes:
Abstracted Chart-level Accuracy
Classification of Retinal Diseases by Artificial Intelligence
Trial ID: NCT04678375
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2018-06-01
Completion date: 2020-10-01
Sponsor: Beijing Tongren Hospital
Collaborators: Beijing Tulip Partner Technology Co., Ltd, China
Conditions: Artificial Intelligence; Retinal Diseases
Interventions: Retinal diseases diagnosed by artificial intelligence algorithm
Locations: Wen-Bin Wei
Countries: China
Enrollment: 1000000
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
The objective of this study is to apply an artificial intelligence algorithm to diagnose multi retinal diseases from fundus photography. The effectiveness and accuracy of this algorithm was evaluated by sensitivity, specificity, positive predictive value, negative predictive value, and area under curve.

Eligibility criteria:
Inclusion Criteria:

* fundus photography around 45° field which covers optic disc and macula
* complete identification information

Exclusion Criteria:

* insufficient information for diagnosis.

Outcomes:
Area under curve; Sensitivity and specificity; Positive predictive value, negative predictive value; F1 score