moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Harnessing the Power of Technology to Transform Delirium Severity Measurement in the ICU
Trial ID: NCT06172491
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-01-15
Completion date: 2028-05-31
Sponsor: Mayo Clinic
Conditions: Intensive Care Unit Delirium; Encephalopathy; Delirium
Locations: Mayo Clinic
Countries: United States
Enrollment: 400
Age range: 50 Years - None
Sex: ALL

Summary:
The goal of this observational study is to develop a passive digital marker (PDM) for delirium severity and examine its performance in comparison to validated delirium severity tools in ICU patients \>50 years of age.

The main questions it aims to answer are:

* Is the trained convolutional neural network able to reliably measure delirium severity.
* Is the Passive Digital Marker able to accurately measure delirium severity
* Is the Passive Digital Marker acceptable and usable by frontline ICU nurse clinicians, patients, and their identified proxies (i.e., caregivers).

Participants will:

* Study participation involves a video camera recording you 24 hours per day while you are a patient in the Intensive Care Unit (ICU).
* Study staff will visit you 4 times each day you are in the ICU. You will be asked questions each time they visit to train the digital marker and see differences between assessments and camera data.

Eligibility criteria:
Inclusion Criteria: • Age \>50;

* Estimated length of stay \>24 hours in ICU; and
* Are not admitted for acute alcohol intoxication, drug (prescribed or illicit) overdose or withdrawal.

Exclusion Criteria:

* Admitted for acute alcohol intoxication, drug (prescribed or illicit) overdose or withdrawal.
* Admitted for acute neuronal injury
* Unable to communicate with research team due to sensory deficits (aphasic, blind, deaf) or language (does not speak English)

Outcomes:
Delirium Severity; Acceptability of passive digital marker for delirium severity; Usability of passive digital marker for delirium severity
MAchine Learning to Boost the Early Diagnosis of Acute Cardiovascular Conditions
Trial ID: NCT06927791
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-04-01
Completion date: 2027-03
Sponsor: University Hospital, Basel, Switzerland
Collaborators: University of Basel
Conditions: Acute Cardiovascular Disease; ST-segment Elevation Myocardial Infarction (STEMI); NSTEMI - Non-ST Segment Elevation MI
Interventions: Machine learning based development of a diagnostic tool for acute cardiovascular disease
Locations: University Hospital Basel
Countries: Switzerland
Enrollment: 200000
Age range: 18 Years - None
Sex: ALL

Summary:
The research project aims to develop clinical decision support tools integrating established diagnostic variables and machine learning (ML) models for rapid diagnosis of acute life-threatening cardiovascular conditions in emergency department (ED) patients with chest pain or dyspnea with the ultimate goal of Improved diagnostic accuracy, faster patient management, and reduced medical errors.

Eligibility criteria:
Inclusion Criteria:

• Acute cardiovascular disease (ACVD)

Exclusion Criteria

* age \< 18 years old
* patients presenting in cardiogenic shock
* chronic terminal kidney failure requiring dialysis

Outcomes:
Developing a clinical decision support tool; Validate machine learning (ML) models
A Study on Artificial Intelligence Algorithms for Breast Cancer Classification From Histopathology Images
Trial ID: NCT06717984
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-01-11
Completion date: 2025-02-11
Sponsor: Taufiq Hasan, PhD
Collaborators: National Institute of Cancer Research & Hospital, Bangladesh
Conditions: Breast Cancer
Interventions: Biopsy, Mastectomy, Histopathology
Locations: National Institute of Cancer Research & Hospital (NICRH)
Countries: Bangladesh
Enrollment: 500
Age range: None - None
Sex: FEMALE

Summary:
Breast cancer, a prevalent and potentially fatal disease, underscores the need for early and accurate detection to improve patient outcomes. Traditional histopathological examination, the current gold standard for diagnosis, faces limitations like subjectivity and low efficiency. In response, this research seeks to revolutionize breast cancer diagnostics by using deep learning techniques to classify invasive and noninvasive breast cancer types from histopathological images. Non-invasive cancers, like DCIS and LCIS, are confined to milk ducts or lobules, while invasive cancers spread to surrounding tissue and make up 70% of cases, often leading to poorer outcomes.

The proposed AI model aims to enhance diagnostic accuracy and efficiency, surpassing manual methods, and providing a scalable solution for diverse healthcare settings. By automating image analysis, the model seeks to democratize cancer screening, making it accessible in underserved populations and adaptable to different resources and equipment. Ultimately, this research aims to advance breast cancer detection, improve patient care, and contribute to better treatment outcomes globally.

Eligibility criteria:
* Inclusion criteria:

  * Female patients of any age can be selected as subjects.
  * Individuals willing to participate in breast cancer screening.
  * Availability for biopsy examination.
  * Women with no current or prior diagnosis of breast cancer.
  * Availability of relevant medical records for confirmation and comparison purposes.
* Exclusion criteria:

  * Pregnant women are excluded due to potential impacts on screening results and the necessity for special considerations during pregnancy.
  * Individuals with severe medical conditions or circumstances that may render histopathologic examination inappropriate or unsafe are excluded.
  * Patients with conditions that could interfere with the accuracy of screening results are excluded.
  * Follow-up screenings are not included in this study.

Outcomes:
Accuracy of Deep Learning Model in Classifying Breast Tissue as Normal Benign, In Situ, or Invasive.
Impact and Safety of AI in Decision Making in the ICU: a Simulation Experiment
Trial ID: NCT05495438
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-07-22
Completion date: 2022-10-31
Sponsor: Imperial College London
Collaborators: University of York
Conditions: Sepsis
Interventions: Hypothetical AI
Locations: Imperial College Hospitals NHS Trust
Countries: United Kingdom
Enrollment: 38
Age range: 18 Years - None
Sex: ALL

Summary:
The impact of deploying artificial intelligence (AI) in healthcare settings in unclear, in particular with regards to how it will influence human decision makers. Previous research demonstrated that AI alerts were frequently ignored (Kamal et al., 2020 ) or could lead to unexpected behaviour with worsening of patient outcomes (Wilson et al., 2021 ). On the other hand, excessive confidence and trust placed in the AI could have several adverse consequences including ability to detect harmful AI decisions, leading to patient harm as well as human deskilling. Some of these aspects relate to automation bias.

In this simulation study, the investigators intend to measure whether medical decisions in areas of high clinical uncertainty are modified by the use of an AI-based clinical decision support tool. How the dose of intravenous fluids (IVF) and vasopressors administered by doctors in adult patients with sepsis (severe infection with organ failure) in the ICU), changes as a result of disclosing the doses suggested by a hypothetical AI will be measured. The area of sepsis resuscitation is poorly codified, with high uncertainty leading to high variability in practice. This study will not specifically mention the AI Clinician (Komorowski et al., 2018). Instead, the investigators will describe a hypothetical AI for which there is some evidence of effectiveness on retrospective data in another clinical setting (e.g. a model that was retrospectively validated using data from a different country than the source data used for model training) but no prospective evidence of effectiveness or safety. As such, it is possible for this hypothetical AI to provide unsafe suggestions. The investigators will intentionally introduce unsafe AI suggestions (in random order), to measure the sensitivity of our participants at detecting these.

Eligibility criteria:
Inclusion Criteria:

* Junior (senior house officer) or senior (registrar/fellow/consultant) ICU doctor

Exclusion Criteria:

* Participants not meeting the inclusion criteria.

Outcomes:
Influence of AI on ICU Clinicians
SLAP Injury of the Shoulder Joint: Application Value of Deep Learning in Diagnosis
Trial ID: NCT04953026
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-10-01
Completion date: 2022-07-01
Sponsor: Peking University Third Hospital
Conditions: Superior Labrum From Anterior to Posterior Injuries
Interventions: Diagnositic test
Locations: Peking University Third Hospital
Countries: China
Enrollment: 800
Age range: None - None
Sex: ALL

Summary:
This study intends to study the shoulder SLAP injury through deep learning technology and establish a deep learning model through the combination of axial and oblique coronal images to establish a deep learning method that can accurately identify and grade shoulder SLAP injury.

Eligibility criteria:
Inclusion Criteria:

1. Without any treatment before imaging examination;
2. MR of the shoulder joint was performed within 3 months before the operation and the image quality was good;
3. Arthroscopic operation was performed in our hospital, and the operation records were complete.

Exclusion Criteria:

1. History of shoulder surgery, tumor, or previous fracture;
2. Unclear image, serious artifact, or incomplete clinical data.

Outcomes:
SLAP Injury of the Shoulder Joint: Application Value of Deep Learning in Diagnosis
Vocal Biomarkers for the Detection and Prevention of Hypoglycemia (HypoVoice)
Trial ID: NCT05569876
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-11-11
Completion date: 2023-01-31
Sponsor: Insel Gruppe AG, University Hospital Bern
Collaborators: Idiap Research Institute; CSEM Centre Suisse d'Electronique et de Microtechnique SA; Ludwig-Maximilians - University of Munich
Conditions: Diabetes Mellitus; Hypoglycemia
Interventions: Controlled hypoglycemic state
Locations: Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism
Countries: Switzerland
Enrollment: 7
Age range: 18 Years - 60 Years
Sex: ALL

Summary:
The HypoVoice study aims at identifying potential vocal biomarkers associated with hypoglycemia to pave the way towards a voice-based hypoglycemia detection approach.

Eligibility criteria:
Inclusion Criteria:

* Written informed consent
* Type 1 Diabetes mellitus as defined by WHO for at least 6 months
* Aged 18 - 60 years
* HbA1c ≤9.0 %
* Functional insulin treatment with good knowledge of insulin self-management
* Native language German or Swiss German
* Use of continuous glucose monitoring (CGM) or flash glucose monitoring (FGM)

Exclusion Criteria:

* Incapacity to give informed consent
* Contraindications to insulin aspart (NovoRapid®)
* Total daily insulin dose \>2 IU/kg/day
* Pregnancy, breast-feeding or lack of safe contraception
* Active heart, lung, liver, gastrointestinal, renal or psychiatric disease
* Pacemaker or implantable cardioverter defibrillator (ICD)
* Epilepsy or history of seizure
* Chronic neurological or ear-nose-and-throat (ENT) disease influencing voice or history of voice disorder
* Illiteracy or dyslexia
* Active smoking
* Active drug or alcohol abuse
* Medication known to interfere with voice or to induce listlessness (e.g. opioids, benzodiazepines, etc.)

Outcomes:
Diagnostic accuracy of the HypoVoice approach to detect hypoglycemia based on voice data quantified as area under the receiver operating characteristic curve (AUROC)
JARVISDHL Screening Tools
Trial ID: NCT05626517
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-01-30
Completion date: 2027-06-30
Sponsor: National Heart Centre Singapore
Collaborators: Singapore Eye Research Institute
Conditions: Coronary Artery Disease
Interventions: Ocular Imaging; Questionnaires
Locations: National Heart Centre Singapore
Countries: Singapore
Enrollment: 2000
Age range: 21 Years - 99 Years
Sex: ALL

Summary:
The main purpose of this study is to pioneer an easy risk stratification tools, which is developed using novel artificial intelligence (AI) algorithms, that will be able to detect common and fatal heart diseases easily simply through a picture of the back of the eye, the retina. The retinal images will be analysed using a computer application with the risk stratification tool to predict health outcome of individual. The study also aims to correlate between clinical characteristics, lifestyle (eg. exercise, sleep, erectile dysfunction) and diet to retinal and coronary vasculature and clinical outcomes.

Eligibility criteria:
Inclusion Criteria for NHCS:

1. 21 years of age or older
2. Sufficient language skills in English or Chinese
3. Provided informed consent
4. Completed CTCA or CT CAC in the past 6 months.

Inclusion Criteria for SERI:

1. 21 years of age or older
2. Sufficient language skills in English or Chinese
3. Provided informed consent
4. Patient diagnosed with Diabetes for more than or equal to 10 years.

Exclusion Criteria for NHCS:

1. Patients whose informed consent was not obtained.
2. Age \<21 years old
3. Patients with cardiac event (acute myocardial infarction, Stroke, heart failure, angina requiring hospitalization) and/or coronary revascularization (percutaneous coronary intervention (PCI) and/or coronary artery bypass grafting (CABG) prior to CTCA

Exclusion Criteria in SERI:-

1. Patient whose informed consent was not obtained.
2. Age \<21 years old
3. Patients with cardiac event (acute myocardial infarction, Stroke, heart failure, angina requiring hospitalization) and/or coronary revascularization (percutaneous coronary intervention (PCI) and/or coronary artery bypass grafting (CABG) prior to CTCA

Outcomes:
AI model and retinal imaging
Assessment of an App Based on Artificial Intelligence for Purulent Acute Otitis Media (AOM) Diagnosis in a a Pediatric Emergency Department
Trial ID: NCT04752891
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-06-10
Completion date: 2023-03-16
Sponsor: Fondation Lenval
Conditions: Otitis Media, Suppurative
Interventions: AOM diagnosis with app; AOM diagnosis without app
Locations: Hôpitaux Pédiatrique de Nice CHU Lenval
Countries: France
Enrollment: 203
Age range: None - 17 Years
Sex: ALL

Summary:
Otitis media (OM) is one of the most common childhood infections and is a major cause of morbidity in children and results as being the first cause of antibiotic prescription among children in developed countries . An artificial intelligence-based tool could help physicians refine their diagnosis

Eligibility criteria:
Inclusion Criteria:

Child under 18 years Presenting signs that may suggest acute otitis media (fever, earache, purulent otorrhea) For which the informed written consent of, at least, one of the two parents or the holder of parental authority has been obtained

Exclusion Criteria:

A sign (s) of vital distress Trans-tympanic drains

\-

Outcomes:
diagnosis of purulent Acute Otitis Media (AOM) made by junior physicians using the i-Nside application associated with Smartscope®; diagnosis of purulent Acute Otitis Media (AOM) made by senior physicians using a standard otoscope
Diagnosis and Characterization of Non-Alcoholic Fatty Liver Disease Based on Artificial Intelligence.
Trial ID: NCT04099147
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2019-09-30
Completion date: 2020-12-31
Sponsor: Instituto de Investigación Marqués de Valdecilla
Collaborators: Servicio Cántabro de Salud
Conditions: Non-alcoholic Fatty Liver Disease (NAFLD)
Interventions: This is an observational study.
Enrollment: 14046
Age range: 19 Years - 74 Years
Sex: ALL

Summary:
A key element in the diagnosis of non-alcoholic fatty liver disease (NAFLD) is the differentiation of non-alcoholic steatohepatitis (NASH) from non-alcoholic fatty liver (NAFL) and the staging of the liver fibrosis, given that patients with NASH and advanced fibrosis are those at greatest risk of developing hepatic complications and cardiovascular disease. There are still no available non-invasive methods that allow for correct diagnosis and staging of NAFLD. The implementation of Artificial Intelligence (AI) techniques based on artificial neural networks and deep learning systems (Deep Learning System) as a tool for medical diagnoses represents a bona fide technological revolution that introduces an innovative approach to improving health processes.

Eligibility criteria:
Inclusion Criteria:

* Subjects aged 19-74 belonging to the ETHON cohort or registered in the Hepamet Spanish registry of NAFLD or the European NAFLD registry

Exclusion Criteria:

* Subjects that not fulfill the inclusion criteria and those who did not sign informed consent to participate in the ETHON cohort or to be registered in the mentioned registers.

Outcomes:
Number of subjects diagnosed with NAFLD and NASH in the ETHON cohort after applying Artificial Intelligence algorithms; Percentage of subjects diagnosed with NAFLD and NASH in the ETHON cohort after applying Artificial Intelligence algorithms; Sensitivity in terms of NASH diagnosis of AI algorithms with respect to histologic diagnosis compared with the Hepamet non-invasive score; Specificity in terms of NASH diagnosis of AI algorithms with respect to histologic diagnosis compared with the Hepamet non-invasive score; Positive predictive value in terms of NASH diagnosis of AI algorithms with respect to histologic diagnosis compared with the Hepamet non-invasive score.; Negative predictive Value in terms of NASH diagnosis of AI algorithms with respect to histologic diagnosis compared with the Hepamet non-invasive score.; Kappa coefficient of concordance about NASH diagnosis between AI algorithms and histologic diagnosis.; Kappa coefficient of concordance about NASH diagnosis between AI algorithms and the Hepamet non-invasive score.; ROC curve at various threshold settings obtained through the algorithms for NASH diagnosis and staging
A Relational Artificial Intelligence (AI) Chatbot for App-Based Physical Activity Promotion Intervention Among Adults With a Sedentary Lifestyle
Trial ID: NCT05794308
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-04-05
Completion date: 2024-12-01
Sponsor: University of California, Davis
Conditions: Behavior, Health
Interventions: Non-Relational Chatbot Physical Activity Intervention; Relational Chatbot Physical Activity Intervention
Locations: University of California, Davis
Countries: United States
Enrollment: 140
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to empirically test the theoretical mechanisms of relational perceptions in the context of building and testing a relational artificial intelligence (AI) chatbot for improving physical activity (PA) behaviors among a sedentary adult population in the U.S.

The aim of the study is to build and experimentally test relational capacities of AI chatbot in inducing positive human-AI relationship and leading to higher PA behavior change intention. During the 7-day intervention, the relational chatbot will educate participants on physical activity using 5 types of relational messages during a PA intervention including 1) social dialogue, 2) empathy, 3) self-disclosure, 4) meta-relational communication, and 5) humor. On the other hand, the non-relational chatbot will only deliver PA intervention messages, without relational cues. Relational chatbot condition will be compared to the non-relational chatbot condition to assess its effectiveness.

The objective of this study is to test the efficacy of the mobile app intervention leveraging chatbots in increasing participants' relationship perception and physical activity behavior change.

Eligibility criteria:
Inclusion Criteria:

* Living in the United States
* Adults (18 years or older)
* Able to speak and read English
* Physically inactive at work and/or during leisure time
* Having internet access to use the chatbot
* Smartphone users

Exclusion Criteria:

* Adults unable to consent
* Individuals who are not yet adults (infants, children, teenagers)
* Prisoners
* Pregnant women
* Physically active individuals who are meeting physical activity guidelines
* Individuals who have participated in a physical activity intervention in the past 12 months

Outcomes:
Relationship perception; Changes in physical activity behavior; Physical activity behavior (step counts)
Comparison of Artificial Intelligent Clinic and Normal Clinic for Diagnosing Congenital Cataracts
Trial ID: NCT03240848
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2017-08-09
Completion date: 2018-05-25
Sponsor: Sun Yat-sen University
Conditions: Cataract
Interventions: artificial intelligent clinic; normal clinic; experts diagnose
Locations: Zhognshan Ophthalmic Center, Sun Yat-sen University
Countries: China
Enrollment: 350
Age range: 1 Month - 14 Years
Sex: ALL

Summary:
In this study, the investigators provide participants≤14 years old the artificial intelligent clinic or normal clinic in the purpose of seeking out a better way to make a definite diagnosis with high efficiency and accuracy, and report a prospective, randomized controlled study aiming at comparison of artificial intelligent clinic and normal clinic for diagnosing congenital cataract.

Eligibility criteria:
Inclusion Criteria:

* Paediatric patients from collaborating eye clinics Written informed consents provided

Exclusion Criteria:

* Definitive diagnosis of cataract or other ocular abnormalities Previous eye surgery Can not cooperate with the slip lamp examination Unwilling to participate in this trail

Outcomes:
The accuracy of artificial intelligence diagnosis for congenital cataract