moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Feasibility Study of Adaptive Radiotherapy for the Treatment of Locally-Advanced Anal Squamous Cell Carcinoma
Trial ID: NCT05838391
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-05-18
Completion date: 2028-12-31
Sponsor: Columbia University
Collaborators: Varian Medical Systems
Conditions: Anal Squamous Cell Carcinoma
Interventions: Artificial Intelligence Guided Daily Radiotherapy Treatment Planning and Delivery; Mitomycin-C; 5-Fluorouracil; Capecitabine
Locations: Columbia University Irving Medical Center
Countries: United States
Enrollment: 20
Age range: 18 Years - None
Sex: ALL

Summary:
This is a 20 patient pilot study to examine the feasibility of dose-adapted radiation therapy for the treatment of locally advanced anal squamous cell cancer. The tumor and a patient's anatomy may change during radiation treatment and daily adaption of the radiation plan (i.e., a new daily plan based on the anatomy of the day) may help to maximize the dose to the tumor and minimize the radiation dose to the normal surrounding organs.

Eligibility criteria:
Inclusion Criteria:

* Histologically proven, invasive primary squamous, basaloid or cloacogenic carcinoma of the anal canal.
* American Joint Committee on Cancer (AJCC) 8th edition stage T2 \> 4 cm, T3-4 or N1.
* Age ≥18 years.
* Eastern Cooperative Oncology Group (ECOG) performance status ≤2 (Karnofsky ≥60%).
* Life expectancy of greater than 12 months.
* Patients must have normal organ and marrow function as defined below:
* leukocytes greater than or equal to 3,000/microliter
* absolute neutrophil count greater than or equal to 1,500/microliter
* platelets greater than or equal to 100,000/microliter
* total bilirubin within normal institutional limits
* Aspartate transaminase (AST)(SGOT)/Alanine transaminase (ALT)(SGPT) ≤ 2.5 × institutional upper limit of normal
* creatinine within normal institutional limits OR creatinine clearance ≥60 mL/min/1.73 m2 for patients with creatinine levels above institutional normal.
* Females of childbearing potential and males must agree to use adequate contraception (hormonal or barrier method of birth control; abstinence) prior to study entry, for the duration of study participation, and for 6 months after completion of study therapy. All pregnancies must be reported.
* Ability to understand and the willingness to sign a written informed consent document.

Exclusion Criteria:

* Prior or co-existing invasive malignancy (except non-melanomatous skin cancer) unless disease free ≥ 2 years.
* Prior chemotherapy or radiation for anal cancer.
* Patients who have undergone complete surgical resection.
* Presence of recurrent/metastatic disease.
* Prior allergic reaction to 5-Fluorouracil or mitomycin C.
* Artificial organ prosthetics, pacemakers or other implantable devices.
* Prior radiotherapy to the pelvis that would result in overlap of radiation therapy fields.
* Uncontrolled inter-current illness including but not limited to known history of HIV with cluster of differentiation 4 (CD4) count less than 200 or symptomatic cardiac disease.
* Women who are pregnant or lactating.

Outcomes:
Time to plan and deliver treatment fractions.
The Clinical Effects of Perioperative Use of the Hypotension Prediction Index
Trial ID: NCT05844774
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-05-22
Completion date: 2024-08-01
Sponsor: Radboud University Medical Center
Collaborators: Edwards Lifesciences
Conditions: Hypotension During Surgery
Interventions: HPI (using HemoSphere) - guided algorithm; Treatment of hypotension following standard of care
Enrollment: 50
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this clinical trial is to investigate if the use of the hypotension prediction index during the perioperative period leads to a decrease in hypotension both during and after surgery (24 hrs) in adults undergoing major abdominal surgery. The main question this trial aims to answer is:

• Does the use of the hypertension prediction index (HPI) decrease hypotension during surgery and in the post-operative period?

During and after their surgery, participants in the intervention group will be treated according to the HPI-algorithm.

Participants in the control group will be treated following the standard of care.

Eligibility criteria:
Inclusion Criteria:

* Patients \> 18 years old
* Patients planned to undergo laparotomy surgery
* Patients that will receive an invasive arterial catheter as part of their procedure
* Patients that are planned to go to the post anaesthetic care unit (PACU) post-operatively

Exclusion Criteria:

* Patients with known arrythmias
* Patients with known severe heart valve disease
* Patients with the need for dialysis
* Clamping of the aorta or Pringle's manoeuvre during surgery
* Emergency procedures

Outcomes:
Time Weighted Average of hypotension
Multicenter Study for the Validation of Willem AI: Aortic StenoSis Early Diagnosis With AI-electrocardiogram (Willem AoS-SEDAI) Study
Trial ID: NCT07479992
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-04-28
Completion date: 2026-07
Sponsor: Idoven 1903 S.L.
Conditions: Aortic Stenosis
Interventions: Willem AI ECG assessment
Locations: Universitätsklinikum Bonn; Technical University of Munich
Countries: Germany; Germany
Enrollment: 5000
Age range: 18 Years - None
Sex: ALL

Summary:
AoS-SEDAI study is an observational, multicenter, retrospective and prospective clinical study.

This study aims to assess Willem Artificial Intelligence (AI) ability to distinguish between aortic stenosis (AS) and non-AS patients from 12-lead electrocardiogram (ECG) data.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18 years;
* All available 12-lead ECG with a 10 seconds minimum length on raw data digital format will be included
* Available clinical data corresponding to each ECG to confirm patient demographics and Aortic Stenosis diagnosis
* Available transthoracic echocardiogram (TTE) within +/- 90 days of each ECG recording

No exclusion criteria are defined for this study.

Outcomes:
Willem performance to detect severe Aortic Stenosis
A Multi-center Study of Differential Diagnosis Thyroid Nodules by Ultrasound Artificial Intelligence and Ultrasound Elastography
Trial ID: NCT03887611
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2019-01-18
Completion date: 2020-03-18
Sponsor: Xin-Wu Cui
Collaborators: Xinhua Hospital, Shanghai Jiao Tong University School of Medicine; Wuhan Hospital of Traditional Chinese Medicine; Macheng People's Hospital; Huangshi Central Hospital; Affiliated Hospital of Jiangsu University; The First People's Hospital of Yichang; Yichang Second People's Hospital; Xiangyang Central Hospital; The Second Hospital of Anhui Medical University; Anqing Municipal Hospital; Huainan People's Hospital; Taizhou Hospital; Wenzhou Central Hospital; Xuzhou First People's Hospital; The Central Hospital of Lishui City; Huai'an First People's Hospital; WISCO General Hospital; Jiangxia District First People's Hospital; Enshi State Central Hospital; Lianyungang Third People's Hospital; First People's Hospital of Xianyang
Conditions: Thyroid Nodule
Interventions: Ultrasound diagnosis
Locations: Xin-Wu Cui
Countries: China
Enrollment: 2000
Age range: 18 Years - None
Sex: ALL

Summary:
The application of computer-aided diagnosis (CAD) technology "S-DetectTM" enables qualitative and quantitative automated analysis of ultrasound images to obtain objective, repeatable and more accurate diagnostic results. The Elastic Contrast Factor (ECI) technique, unlike conventional strain-elastic imaging techniques, can evaluate the elastic distribution in the region of interest. The purpose of the study was to evaluate the differential diagnosis value of ultrasound S-DetectTM technology for benign and malignant thyroid nodules and evaluate the consistency of ultrasound S-DetectTM technology and sonographer in the differential diagnosis of thyroid nodules and explore the differential diagnosis value of Samsung ultrasound ECI technology for benign and malignant thyroid nodules.

Eligibility criteria:
Inclusion Criteria:

1. Had thyroid lesions detected by ultrasound
2. Age 18 or older
3. Upcoming FNAB or surgery
4. Signing informed consent

Exclusion Criteria:

1. Patients who had received a biopsy of thyroid lesion before the ultrasound examination
2. Can not cooperate with the test operation
3. Patients who were pregnant or lactating
4. Patients who were undergoing neoadjuvant treatment.

Outcomes:
Benign or malignant lesions as determined by pathology; Elastic ratio
Health Outcomes of Nasopharyngeal Carcinoma Patients Three Years After Treatment by the AI-assisted Home Enteral Nutrition Management
Trial ID: NCT06603909
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-01-01
Completion date: 2024-06-30
Sponsor: Changsha Medical University
Conditions: Cancer, Carcinoma; Artificial Intelligence (AI); Nutrition
Interventions: group 1: Use AI to assist nutrition management; group 2：Traditional nutrition management（through the hospital dietitian）
Locations: First Affiliated Hospital of Zhengzhou University
Countries: China
Enrollment: 500
Age range: None - None
Sex: ALL

Summary:
You have completed the current stage of cancer treatment, after which you need to check regularly and pay attention to nutrition. So we started this study to see if AI could help with nutrition management. Main options: Option 1: Use AI to assist nutrition management; Option 2: Contact the nutritionist team of the hospital for nutrition management according to your own situation. Option 3: Do not adopt the first two management methods. Special Statement: Please choose; Cancer screening data is used for statistical analysis, but does not reveal any personal privacy.

Eligibility criteria:
Inclusion Criteria:

* Malignant tumor patient

Exclusion Criteria:

* no

Outcomes:
Survival Rate; Local Recurrence Rate
Artificial Intelligence in Image Recognition of Pouchoscopies in Patients With Restorative Proctocolectomy
Trial ID: NCT04864587
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-06-01
Completion date: 2023-06-01
Sponsor: Theresienkrankenhaus und St. Hedwig-Klinik GmbH
Collaborators: Universitätsmedizin Mannheim
Conditions: Pouches, Ileoanal
Interventions: Artificial intelligence used for image recognition in pouchoscopy
Locations: Theresienkrankenhaus und St. Hedwigkliniken GmbH
Countries: Germany
Enrollment: 500
Age range: 18 Years - None
Sex: ALL

Summary:
The application of artificial intelligence in pouchoscopy of patients with restorative proctocolectomy might improve the diagnosis of pouchitis and neoplasms. The aim of this pilot study is to develop a convolutional neural network algorithm for pouchoscopy

Eligibility criteria:
Inclusion Criteria:

• All patients aged ≥ 18 years with inflammatory bowel disease and status after restorative proctocolectomy with ileoanal pouch who had received a pouchoscopy

Exclusion Criteria:

• Very poor endoscopic image quality

Outcomes:
AI versus endoscopist; AI versus pathologist
Can Artificial Intelligence Take Place of Physical Medicine and Rehabilitation Physicians?: ChatGPT Versus Physical Medicine and Rehabilitation Residents
Trial ID: NCT06775938
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2025-01-01
Completion date: 2025-01-10
Sponsor: Sisli Hamidiye Etfal Training and Research Hospital
Conditions: Artificial Intelligence (AI); Resident Education
Interventions: resident exam
Locations: Şişli Hamidiye Etfal Training and Research Hospital
Countries: Turkey (Türkiye)
Enrollment: 314
Age range: None - None
Sex: ALL

Summary:
The aim of this study was to assess the performance of ChatGPT on examinations administered to physical medicine and rehabilitation (PMR) residents.

Eligibility criteria:
Inclusion Criteria:

* Physical medicine and rehabilitation residents exam results in May 2024

Exclusion Criteria:

\-

Outcomes:
The comparison of resident exam results and ChatGPT answers to the same questions
Artificial Intelligence for Preventing Heart Disease: Observational, Single Center, Prospective and Retrospective Study
Trial ID: NCT06029387
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-07-31
Completion date: 2024-11-14
Sponsor: IRCCS San Raffaele
Collaborators: DGS; EBIT; Dyrecta Lab; PORINI
Conditions: Coronary Artery Disease
Locations: IRCCS San Raffaele
Countries: Italy
Enrollment: 3000
Age range: 18 Years - None
Sex: ALL

Summary:
Coronary artery disease (CAD) is a leading cause of mortality in western countries. Coronary computed tomography angiography (cCTA) is the first-line imaging test in patients with suspected obstructive CAD. However, in most patients, cCTA shows non-obstructive CAD. The management of patients with non-obstructive CAD is unclear. This is due to the lack of cCTA-based methods capable to assess the risk of disease progression towards developing major adverse cardiovascular events (MACEs) based on the atherosclerosis characteristics of each patient. A solution for prognostication in these patients is particularly appealing since it could allow to identify patients who can benefit of a more aggressive medical treatment and management, thus improving outcome.

Proposed methods, which include qualitative evaluations such as the identification of adverse atherosclerotic plaque characteristics or quantitative evaluations such as the quantification of atherosclerotic plaque burden, may in some cases suffer of limited reproducibility between operators and software. Most importantly, each single biomarker is insufficient to accurately predict patient risk, hence potential synergic integration of cCTA and clinical biomarkers is the key to efficiently guide the personalization of patient's management. Furthermore, the few risk stratification methods that have been proposed are not designed to work on platforms capable of deploying the solution to other clinical settings, promoting prospective or external validation

Eligibility criteria:
Inclusion Criteria:

* Patients with cCTA performed for CAD assessment

Exclusion Criteria:

1. Refusal to participate in the study
2. Age \<18 years old
3. Presence of other cardiovascular comorbidities (e.g. severe valvulopathies; non-ischemic cardiomyopathies; etc.)

Outcomes:
composite outcome 1
A Cohort Study on feMale Patients' mammogRaphic texturE featureS: the COMPRESS Trial
Trial ID: NCT06469606
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-06-17
Completion date: 2038-12-15
Sponsor: Tampere University Hospital
Collaborators: Tampere University; Kuopio University Hospital; University of Eastern Finland; University of Turku; University of Oulu
Conditions: Breast Cancer; Artificial Intelligence; Mammography
Interventions: AI tool
Enrollment: 200
Age range: 18 Years - None
Sex: FEMALE

Summary:
Mammography is the most common method for breast imaging, and it provides information for model building and analysis. Radiomics applied to mammography has the potential to revolutionize clinical decision-making by providing valuable insights into risk assessment and disease detection. Despite this, the influence of imaging parameters and clinical and biological factors on radiological texture features remains poorly understood. There is a pressing need to overcome the obstacle of system-inherent effects on mammographic images to facilitate the translation of radiological texture features into routine clinical practice by enabling reliable and robust AI-based or AI-aided decision-making. Furthermore, understanding the relationship between imaging parameters, textural features, and clinical and biological information supports the clinical use of AI. The objective of this study is to evaluate AI methods for clinical practice and to study how it relates to clinical factors and biological features.

Eligibility criteria:
Inclusion Criteria:

* Candidate is a biological female aged 18 years or above;
* Candidate is willing and able to give informed consent and gives their written consent for the participation in the study;
* There is a clinical indication for a uni- or bilateral mastectomy

Exclusion Criteria:

* Candidate lacks the capacity to provide informed consent;
* Candidate has breast implants

Outcomes:
Mammographic texture features
Prediction of the Spontaneous Breathing Test Success Using Biosignal and Biomarker in Critical Care Unit by a Machine Learning Approach
Trial ID: NCT05886803
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-01-01
Completion date: 2025-12-12
Sponsor: Centre Hospitalier Universitaire de Nice
Conditions: Weaning From Mechanical Ventilation in Care Unit
Interventions: Spontaneous ventilation test
Locations: University Hospital of Nice
Countries: France
Enrollment: 500
Age range: None - None
Sex: ALL

Summary:
Context:

Several authors have been interested in applying Artificial Intelligence (AI) to medicine, using various Machine Learning (ML) techniques: managing septic shock, predicting renal failure... \[1, 2\] AI has an important place in decision support for clinicians \[3\]. The weaning period is a really important time in the management of a patient on mechanical ventilation and can take up to half of the time spent in intensive care unit. The first weaning attempt is unsuccessful in 20% of patients However, mortality can be as high as 38% in patients with the most difficult weaning \[4\]. Only a few studies have looked at the application of machine learning in this area, and only one has looked at the use of biosignals (cardiac rate, ECG, ventilatory parameters…) \[5-7\]. To improve morbidity, mortality and reduce length of stay, it is essential to be able to predict the success of the spontaneous breathing test and extubation.

Investigators propose to develop a predictive algorithm for the success of a ventilatory weaning test based on biosignal records and others features.

Methods:

It is a critical care, oligo-centric and retrospective study the investigators included biosignal variables extracted from the electronic medical record, such as respiratory (RR, minute volume...), cardiac (systolic pressure, heart rate...), ventilator parameters and other discrete variables (age, comorbidity...). Most biosignal variables are minute-by-minute records. Recording starts 48 hours before the test and stops at the start of the weaning test. The investigators extracted features from these records, combined them with other biomarkers, and applied several machine learning algorithms: Logistic Regression, Random Forest Classifier, Support Vector Classifier (SVC), XGBoost, and Light Gradient Boosting Method (LGBM)…

Eligibility criteria:
Inclusion Criteria:

* Computerized health report (CHR)
* Spontaneous breathing test should have been performed

Exclusion Criteria:

* Spontaneous breathing test has not been performed,
* Biosignal (cardiac, respiratory) are not registered in the CHR
* Patient died before the spontaneous breathing test
* Opposition to the study has been expressed.

Outcomes:
Prediction of the spontaneous breathing test outcome.
Treatment of Insomnia in Primary Care Study
Trial ID: NCT06149273
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-12-02
Completion date: 2029-12
Sponsor: University of Turku
Collaborators: University of Eastern Finland
Conditions: Insomnia Chronic
Interventions: Sleep School; Treatment as usual
Locations: University of Turku
Countries: Finland
Enrollment: 250
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this clinical trial is to learn about insomnia treatment among primary care patients with chronic insomnia.

The main question it aims to answer is:

• Does Sleep School (a therapy for insomnia) work well to decrease harm of insomnia? Participants will attend a group therapy intervention once a week for six weeks.

Researchers will compare Sleep School to treatment as usual (short counselling by an educated nurse) to see if the Sleep School works better than treatment as usual in decreasing the harm of insomnia.

Eligibility criteria:
Inclusion Criteria:

* Insomnia severity index (ISI) score at least 8
* insomnia symptoms present at least for 3 months

Exclusion Criteria:

* diagnosed dementia based on medical records
* acute suicidality
* acute psychotic symptoms

Outcomes:
Mean Change from Baseline in Insomnia Severity Index (ISI) score at 8 weeks
Intelligent Clinical Decision Support for Preoperative Blood Management: A Cluster-Randomized Clinical Trial
Trial ID: NCT07223853
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-10-28
Completion date: 2027-01-01
Sponsor: Washington University School of Medicine
Collaborators: National Heart, Lung, and Blood Institute (NHLBI)
Conditions: Surgery
Interventions: S-PATH clinical decision support system; Usual care
Locations: Washington University / Barnes Jewish Hospital
Countries: United States
Enrollment: 50
Age range: 18 Years - None
Sex: ALL

Summary:
20 million patients have surgery in the United States every year, with approximately 1 million of those patients requiring life-saving blood transfusion. Presurgical preparation for transfusion is important to allow for safe and timely transfusion during surgery; however, excessive preparation is unfortunately common, costly, and contributes to blood waste. This study aims to evaluate an intelligent clinical decision support system that helps clinicians prepare blood for patients who are likely to need it, while avoiding excessive preparation for patients who don't, potentially improving patient safety while reducing blood waste and healthcare costs.

Eligibility criteria:
Clinician Level Exclusion Criteria:

* Clinician (resident physician or advanced practice provider) who works at a preoperative assessment clinic

Clinician Level Exclusion Criteria:

* None

Patient Inclusion Criteria:

* Scheduled for surgery in one of the main operating room areas (non-remote) at Barnes Jewish Hospital
* Evaluated in-person at one of the preoperative assessment clinics affiliated with BJC Healthcare
* Have a valid S-PATH model prediction prior to their preoperative assessment clinic visit

Patient Exclusion Criteria:

* Pregnant
* Presence or history of red cell alloantibodies

Outcomes:
Frequency of patients with a type and screen order placed during the preoperative clinic assessment visit
The Development of Artificial Intelligence (AI) Based High Performance Structural-functional and Quantitative Ultrasound Imaging Software Platform
Trial ID: NCT05836246
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-09-01
Completion date: 2026-03-31
Sponsor: Seoul National University Bundang Hospital
Conditions: Chronic Liver Disease; Thyroid Disease; Benign Breast Disease; Malignant Breast Neoplasm; Acute Myocardial Infarction
Locations: Seoul National University Bundang Hospital
Countries: South Korea
Enrollment: 196
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this observational study is to compare the image differences between conventional ultrasound and artificial intelligence-based ultrasound software in conscious adults.

The main question it aims to answer is to evaluate the effectiveness by determining that the new image analysis method is considered valid if it helps to identify more than 30% of histological characteristics.

Participants will undergo the examination using the two methods mentioned earlier after signing the consent form.

Eligibility criteria:
Inclusion Criteria:

* People with heart disease, thyroid disease, breast disease, and liver disease.

Exclusion Criteria:

* Someone who has received surgery on the target organ in question.

Outcomes:
Quantitative ultrasound information