moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Analysis of ADVerse evENTs in Anesthesia Using ARtificial IntelligencE
Trial ID: NCT05185479
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-11-12
Completion date: 2021-11-12
Sponsor: University Hospital, Strasbourg, France
Conditions: Allergic Reaction
Locations: Service d'Anesthésie et Réanimation chirurgicale - CHU de Strasbourg - France
Countries: France
Enrollment: 9559
Age range: 1 Year - None
Sex: ALL

Summary:
The interest of health databases in anesthesia is no longer to be demonstrated. The aim of this research was to develop a natural language processing approach to establish a classification of adverse events observed during the perioperative period and to facilitate their analysis:

The main objective of the study was to identify what a "naïve" unsupervised model would discover based on Adverse Event (AE) descriptions. Our second goal was to identify apparently unrelated events whose combination could favor the occurrence of an AE

Eligibility criteria:
Inclusion Criteria:

* Minors and adults having had an allergic reaction associated with care
* Having had an adverse event reported by an anesthetist between January 01, 2009 and June 30, 2020

Exclusion Criteria:

\- Patient not meeting the inclusion criteria

Outcomes:
Development of a natural language processing approach to establish a classification of adverse events observed during the perioperative period and to facilitate their analysis.
Accuracy Evaluation of Artificial Intelligence Assisted Liver Tumor Ablation Path Planning
Trial ID: NCT05161624
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-12-15
Completion date: 2022-04-30
Sponsor: Sun Yat-sen University
Conditions: Liver Tumor
Interventions: Accuracy evaluation
Locations: Sun Yat-sen University Cancer Center
Countries: China
Enrollment: 160
Age range: 18 Years - 70 Years
Sex: ALL

Summary:
CT-guided microwave ablation is one of the important treatment methods for primary or metastatic liver tumors. It has the advantages of minimally invasive, safe, and economical. For liver tumors with a diameter of less than 3 cm, it can be comparable to surgical resection. However, different doctors have different levels of experience, leading to a high risk of residues after ablation and even complications. The key is to fail to plan the needle placement reasonably. The past research on path planning was mostly conducted by the engineering team, so that the actual clinical needs were not considered comprehensively, and the sample size of verified cases was small, which limited the application of existing models in the clinical application. In previous study, we used artificial intelligence to segment the upper abdominal organs and construct the constraint function to establish the ablation path planning model, and initially verified the effectiveness of the path planning model in a small number of cases. Now it is planned to expand the number of patients enrolled to evaluate the accuracy of the ablation path planning model that we have established.

Eligibility criteria:
Inclusion Criteria:

1. 18-70 years old patients with liver tumors, not limited to primary liver cancer or secondary liver metastases;
2. The number of tumors is less than 5;
3. The diameter of the tumor is less than 3 cm;
4. No local treatment of intrahepatic tumors has been done in the past.

Exclusion Criteria:

1. Diffuse liver tumor;
2. Multiple lesions in the liver\> 3cm, which cannot be completely ablated;
3. Patients with lymph node metastasis and distant metastasis outside the liver;
4. Other situations where the researcher believes that it is necessary to withdraw from the study

Outcomes:
Receiver operating characteristic curve, ROC
"Artificial Intelligence-Based Data Analysis Results and Mortality Prediction in Covid-19 Patients in Intensive Care"
Trial ID: NCT06795880
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2020-04-01
Completion date: 2025-04-01
Sponsor: Kocaeli City Hospital
Conditions: COVID-19
Locations: Kocaeli City Hospital
Countries: Turkey (Türkiye)
Enrollment: 400
Age range: 18 Years - None
Sex: ALL

Summary:
An artificial intelligence-based analysis will be performed using retrospective data of patients treated in adult intensive care units due to COVID-19. The dataset will include various parameters such as demographic information, laboratory results, vital signs, and clinical history. Among the machine learning models, logistic regression, support vector machines (SVM), decision trees, and deep learning techniques (e.g., artificial neural networks) will be utilized. The performance of these models will be compared with traditional scoring systems.

As a result of the analysis, it is anticipated that AI-based models will provide higher accuracy and reliability in mortality prediction. In particular, it is expected that deep learning-based models will better capture complex relationships and predict the outcomes of critically ill patients with greater precision. AI-supported data analysis results have the potential to guide diagnosis and treatment strategies in high-risk intensive care patients and can contribute to mortality prediction. AI-based approaches in intensive care are likely to offer significant advantages in the management of critical diseases such as COVID-19. These methods have the potential to improve clinical decision-making processes by providing healthcare professionals with more precise and timely information.

Eligibility criteria:
Inclusion Criteria:

* All patients diagnosed with Covid-19 in the anesthesia and reanimation adult intensive care unit

Exclusion Criteria:

* Participants who do not meet the inclusion criteria stated above will be excluded from the study.

Outcomes:
Mortality prediction in covid-19 patients in intensive care using artificial intelligence models
User-driven Retrospectively Supervised Classification Updating (RESCU) System for Robust Upper Limb Prosthesis Control
Trial ID: NCT04043234
Status: COMPLETED
Study type: INTERVENTIONAL
Start date: 2023-11-07
Completion date: 2023-12-19
Sponsor: Infinite Biomedical Technologies
Conditions: Amputation; Upper Limb
Interventions: RESCU; Pattern Recognition
Locations: Medical Center O&P
Countries: United States
Enrollment: 4
Age range: 18 Years - None
Sex: ALL

Summary:
This study will compare the use of RESCU \[Experimental\] Prosthesis with a \[Standard\] pattern recognition prosthesis in a clinical setting and in unsupervised daily activity. The protocol will follow a single case experimental design (SCED) to compensate for the limited size of the patient population. Each of the participants will use the Standard and Experimental and systems over a 35-day period. The Standard system will include at least two controllable DoFs (hand, wrist, multi-articulated hand, etc) and a commercially-available pattern recognition controller. The RESCU system will use the same components as the Standard system but will differ with respect to incorporating eight IBT Element Electrodes (as required for pattern recognition control) and the RESCU control software. The hypothesis is that pattern recognition will outperform the commercially-available control strategy for most participants on in-clinic, at-home usage, and subjective measures.

Eligibility criteria:
Inclusion Criteria:

* Trans-radial limb difference.
* Candidate for a 2+ degree-of-freedom (DoF) myoelectric pattern recognition prosthesis as determined by the study prosthetist
* Active pattern recognition myoelectric prosthesis user
* Fluent in English
* Age of 18 years or greater

Exclusion Criteria:

* Patients with a residual limb that is unhealed from the amputation surgery
* Patients with easily damaged or sensitive skin who would not tolerate EMG electrodes
* Unhealed wounds
* Significant cognitive deficits as determined upon clinical evaluation
* Significant neurological deficits as determined upon clinical evaluation
* Significant physical deficits of the residual limb impacting full participation in the study as determined upon clinical evaluation
* Uncontrolled pain or phantom pain impacting full participation in the study as determined upon clinical evaluation
* Serious uncontrolled medical problems as judged by the project therapist

Outcomes:
Mean Daily Prosthesis Use Duration
Artificial Intelligence to Assess the Association Between Facial Characteristics and Coronary Artery Diseases
Trial ID: NCT03731936
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2017-07-10
Completion date: 2019-08-02
Sponsor: China National Center for Cardiovascular Diseases
Conditions: Coronary Artery Disease
Interventions: No intervention
Locations: Beijing Anzhen hospital; Fuwai hospital; Guanganmen Hospital, Chinese Academy of Traditional Chinese Medicine; Wuhan union hospital; Xuzhou third people's hospital; The first affiliated hospital of Dalian medical university; Shanghai East hospital; Shanghai Renji hospital; The fisrt affiliated hospital of Wenzhou medical university
Countries: China; China; China; China; China; China; China; China; China
Enrollment: 6864
Age range: 18 Years - None
Sex: ALL

Summary:
The purposes of this study are 1) to explore the association between facial characteristics and the increased risk of coronary artery diseases; 2) to evaluate the diagnostic efficacy of appearance factors for coronary artery diseases.

Eligibility criteria:
Inclusion Criteria:

* Undergoing coronary angiography or coronary computer tomography angiography
* Written informed consent

Exclusion Criteria:

* Prior percutaneous coronary intervention (PCI)
* Prior coronary artery bypass graft (CABG)
* Screening coronary artery disease before treating other heart diseases
* Without blood biochemistry outcome
* With artificially facial alteration (i.e. cosmetic surgery, facial trauma or make-up)
* Other situations which make patients fail to be photographed

Outcomes:
Sensitivity of diagnostic model
Real-world Evaluation of a Recommender System for Mental Health Self-management
Trial ID: NCT06451549
Status: WITHDRAWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-08-01
Completion date: 2024-11
Sponsor: Newcastle University
Collaborators: Cornwall Partnership NHS Foundation Trust; University of Plymouth
Conditions: Mental Health Issue; Depression; Anxiety
Interventions: Syndi recommender system for mental health apps
Locations: Pennine Care NHS Foundation Trust; Newcastle University; Cornwall Partnership NHS Foundation Trust; Devon Partnership NHS Trust
Countries: United Kingdom; United Kingdom; United Kingdom; United Kingdom
Enrollment: 0
Age range: 18 Years - None
Sex: ALL

Summary:
Mental health concerns are a growing global concern, but support can still be difficult to access. Digital health technologies can help people access support, but with over 10,000 mental health apps available, it can be difficult to choose one that is safe, effective, and personalised. Syndi is a digital health platform that uses a machine learning system to provide personalised recommendations of mental health apps. This study aims to examine the impact of Syndi and its recommendations on the mental health and well-being of people with mental health concerns.

The investigators will use the standard National Health Service (NHS) Talking Therapies measures of anxiety and depression, as well as additional measures of wellbeing and functional impairment to look at the impact of Syndi on patients' mental well-being. The investigators will also look at user experience with Syndi using a survey. Users' engagement with Syndi and its recommended apps will be assessed using a short survey and some participants will be randomly selected for an interview so the investigators can get a better understanding of what people liked and disliked about using the platform and why.

The investigators expect that the project will show that using Syndi helps improve mental well-being in people with mental health concerns. If the study shows this positive impact, this will provide evidence to support the use of Syndi to improve the accessibility of mental health support in clinical pathways. The investigators will publish the results of the study in academic journals as well as in more accessible platforms.

Eligibility criteria:
Inclusion Criteria:

* Based in the UK
* Referred or self-referred to proceed through the standard NHS Talking Therapies care pathway OR people with mental health concerns looking for app recommendations online;
* Willing and able to provide informed consent;
* Aged 18 years or older;
* Able to speak English;
* Have access to the internet and own a smartphone.

Exclusion Criteria:

* Patients with a PHQ-9 score \>=20
* Patients with suicidal ideation measured as an answer of 'YES' to any question in the Columbia Suicide Severity Rating Scale (C-SSRS);
* Incapable of self-consent;
* In a dependent/unequal relationship with the research or care teams or any PPI representatives.

Outcomes:
Depression Severity
Development and Prospective Validation of an Artificial Intelligence Model to Predict Postoperative Acute Kidney Injury
Trial ID: NCT04705064
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-03-01
Completion date: 2022-02-01
Sponsor: Seoul National University Hospital
Conditions: Non-cardiac Surgery
Interventions: Prediction of postoperative acute kidney injury using an artificial intelligence
Locations: Hyung-Chul Lee
Countries: South Korea
Enrollment: 2000
Age range: 18 Years - None
Sex: ALL

Summary:
The main objective of this study is to develop and validate an artificial intelligence model that predicts postoperative acute kidney injury.

Eligibility criteria:
Inclusion Criteria:

* Adults patients undergoing non-cardiac surgery

Exclusion Criteria:

* Age under 18 years
* Surgery duration \< 1 hour
* Transplantation surgery
* Nephrectomy
* Cardiac surgery
* Patients who had severe kidney dysfunction preoperatively as follows:
* Serum creatinine ≥ 4 mg/dl
* Estimated glomerular filtration rate \<15 ml/min/1.73m2
* History of renal replacement therapy
* Patients who had no results of preoperative or postoperative serum creatinine

Outcomes:
the incidence of postoperative acute kidney injury
A Big Data-based Cohort Study for Cataract Patients
Trial ID: NCT05491798
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-07-01
Completion date: 2025-06
Sponsor: Sun Yat-sen University
Conditions: Cataract; Retina Disorder
Locations: Zhognshan Ophthalmic Center, Sun Yat-sen University
Countries: China
Enrollment: 1000
Age range: 18 Years - None
Sex: ALL

Summary:
Cataract is an important cause of blindness and visual impairment worldwide. At present, the only effective treatment method is surgery. The visual function of most patients can be significantly improved after surgery, but there are still 5-20% of patients whose visual function cannot be improved after surgery. Previous studies have found that the surgical complications and postoperative visual function of cataract patients are closely related to the condition of the fundus, but the current fundus camera cannot perform clear fundus imaging of cataract patients, and the existing potential visual inspections, such as retinal visual inspection, are also inaccurate. Predict postoperative visual acuity. Therefore, there is an urgent need for a reliable postoperative effect prediction system for cataract patients to provide reference for both ophthalmologists and patients.

This study intends to collect patient medical record information and traditional/ultra-wide fundus photos and other multi-modal data. Firstly, this study will use artificial intelligence technology to enhance fundus photos of cataract patients to obtain clearer fundus photos. Then this study will use both medical record information and traditional/ultra-wide fundus photographs to predict postoperative vision and visual function of cataract patients.

Eligibility criteria:
Inclusion Criteria:

* Candidates for cataract surgery (phacoemulsification and intraocular lens implantation) within a week.

Exclusion Criteria:

* Unwilling or unable to receive fundus photography

Outcomes:
Change of best corrected visual acuity; Accuracy for detection of retinal disorders
HOPPLearning - Active Learning in Elementary Schools in Horten Municipality
Trial ID: NCT02495714
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2015-01
Completion date: 2023-12
Sponsor: Kristiania University College
Collaborators: Horten municipality
Conditions: Health Behaviour
Interventions: Active learning; Dietary councelling
Locations: Kristiania University College
Countries: Norway
Enrollment: 2300
Age range: 6 Years - 12 Years
Sex: ALL

Summary:
HOPP Learning will be implemented in elementary schools in the Horten municipality and will assess the effect of a combined pedagogical approach, active learning, on a large student population. Students are to increase their amount of physical activity with learning, during a school day by one hour.

Eligibility criteria:
Inclusion Criteria:

* Students from Grade 1-6 (2015) from all elementary schools in Horten municipality and two schools from Loerenskog and Baerum municipality.

Exclusion Criteria:

* Students with psychological and physical disabilities preventing them participating in the tests
* Students afflicted with sickness/disease during the testing period
* Students who do not wish to participate in the test

Outcomes:
Academic performance
A Hierarchical Multi-modal AI Framework for Pathological and Genetic Subtyping of Lung Cancer Based on PET/CT Imaging
Trial ID: NCT07463300
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-08-01
Completion date: 2027-08-01
Sponsor: Second Affiliated Hospital, School of Medicine, Zhejiang University
Collaborators: First Hospital of China Medical University; West China Hospital; Zhongnan Hospital; Zhejiang Cancer Hospital; Guangdong Second Provincial General Hospital; The First Affiliated Hospital of Zhejiang Chinese Medical University; Wuhan TongJi Hospital; Northern Jiangsu People's Hospital
Conditions: Lung Cancer
Interventions: PET imaging analysis, data mining, and AI model developing
Locations: Guangdong Second Provincial General Hospital; Wuhan Tongji Hospital; Zhongnan Hospital; Northern Jiangsu People's Hospital; First Hospital of China Medical University; West China Hospital; The First Affiliated Hospital of Zhejiang Chinese Medical University; Department of Nuclear Medicine and PET/CT Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University; Zhejiang Cancer Hospital
Countries: China; China; China; China; China; China; China; China; China
Enrollment: 5500
Age range: 18 Years - None
Sex: ALL

Summary:
PET/CT imaging and clinical information (age, gender, smoking history, family history of cancer, history of present illness, and several tumor biomarkers, etc.) were used to establish a hierarchical multi-modal AI framework for pathological and genetic subtyping of lung cancer

Eligibility criteria:
Inclusion Criteria:

* Newly diagnosed NSCLC confirmed pathologically
* Age ≥18 y
* Underwent pre-treatment 18F-FDG PET/CT scan
* No prior anti-tumor treatments
* No history of other malignancies

Exclusion Criteria:

▪ Pure ground-glass nodules with no FDG uptake

Outcomes:
Accurate differentiation between small cell lung cancer and non-small cell lung cancer
Langue and Imaging-integrated Foundation Model for Gastric Cancer Detection and Staging Via Contrast-Enhanced CT: a Multicenter Study
Trial ID: NCT07250347
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-08-01
Completion date: 2028-12-30
Sponsor: The First Affiliated Hospital with Nanjing Medical University
Collaborators: Jiangsu Cancer Institute & Hospital; Zhengzhou University; Peking University First Hospital
Conditions: Gastric Cancer Stage; Gastric Cancer Patients Undergoing Gastrectomy
Interventions: CT scan
Locations: The First Affiliated Hospital of Nanjing Medical University
Countries: China
Enrollment: 8000
Age range: 18 Years - 85 Years
Sex: ALL

Summary:
Accurate preoperative assessment of gastric cancer stage guides eligibility for endoscopic resection, extent of gastrectomy and lymphadenectomy, selection for neoadjuvant therapy, and use of staging laparoscopy. Contrast-enhanced CT (CECT) is guideline-endorsed for initial staging, yet performance varies across institutions and readers. This study will evaluate an artificial-intelligence (AI) system that analyzes routine CECT to detect gastric cancer and assign four-class T stage (T1-T4) and N stage (N0-N3) .

Eligibility criteria:
Inclusion Criteria:

1. pathologically confirmed gastric cancer;
2. preoperative contrast-enhanced CT performed;
3. no evidence of distant metastasis on baseline staging;
4. curative-intent management with complete postoperative histopathology.

Exclusion Criteria:

1. prior treatment before surgery;
2. non-diagnostic or poor-quality CT precluding evaluation.

Outcomes:
Diagnostic performance of the AI model for staging
Outcome Reflection for Immune Mediated Rejection by Utilizing Artificial Intelligence in Cardiac Transplant Patients Through Early and Late Echocardiographic Analysis (ORACLE)
Trial ID: NCT06178744
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-04-04
Completion date: 2027-05
Sponsor: Mayo Clinic
Conditions: Cardiac Transplant Rejection
Locations: Mayo Clinic in Rochester
Countries: United States
Enrollment: 500
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of this research is to detect episodes of rejection versus non-rejection after cardiac transplant and the diagnostic accuracy of an Artificial Intelligence (AI) algorithm using the data from an ECG (electrocardiogram) and Cardiac Ultrasound (Echocardiogram and/or point of care ultrasound).

Eligibility criteria:
Inclusion Criteria:

* Patients undergoing post-cardiac transplant endomyocardial biopsy, and the ability to provide informed consent and a consent for the study.

Exclusion Criteria:

* Refusal of study participation

Outcomes:
Number of episodes identified by the Artificial Intelligence (AI) algorithm as 1R or greater acute cellular rejection, grade 1 or greater antibody mediated rejection, reduction in LVEF to <50%, or a drop in LVEF >5%