moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Spectroscopy and Artificial Intelligence to Disrupt the Status Quo in Cervical Cancer
Trial ID: NCT05236855
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-03-01
Completion date: 2022-09-30
Sponsor: IWK Health Centre
Collaborators: Canadian Institutes of Health Research (CIHR); Canadian Cancer Society (CCS); Lancaster University; Nova Scotia Health Authority
Conditions: Cervical Cancer; HPV
Interventions: Spectoscropy testing of urine specimen
Enrollment: 200
Age range: None - None
Sex: FEMALE

Summary:
Cervical cancer kills one woman every two minutes, 90% of these women are from low- and middle-income countries. Newly developed testing using biofluids has proven successful in identifying disease markers in, for example, brain cancers and endometrial cancers. Early studies have revealed that this technology is also showing potential for gynaecological cancers using validated human papillomavirus (HPV) test specimens. Urine samples, more easily collected, may make screening more accessible and acceptable to women. Spectroscopy is a portable and relatively simple technology; results are instant, reproducible and reliable. Once we confirm that spectroscopy has the ability to identify potential CIN 2+ by detecting HPV in urine, the test can be miniaturized and adapted to a point of care test. This will be more economical and logistically simpler than what is currently available; no consumables and pre-processing of samples are required. Women with abnormal cervical screening and women with normal screening as controls will be recruited, cervical and urine samples will be obtained. These will be tested for HPV DNA using standard methods and also by spectroscopy for HPV. These spectroscopy signals will be analyzed using artificial intelligence. The results will be compared to tissue samples obtained at colposcopy. This will allow evaluation of the new spectroscopy test. This preliminary study aims to prove the concept the spectroscopy as a simple, affordable screen can be used to radically change cervical cancer screening. Enabling a test that has point of care capabilities has huge implications for women in developed and more significantly in low-and middle-income countries, where cervical cytology and HPV testing have significant logistical problems. A non-invasive test will be preferred by many women. We believe spectroscopy will disrupt the status quo of 'no screening' in the low and middle income countries (LMICs), accelerate elimination of cervical cancer, and thus avert 15 million deaths in next 50 years.

Eligibility criteria:
Inclusion Criteria:

* Women undergoing standard HPV screening at the IWK Health Centre and Nova Scotia Health in Halifax, Nova Scotia, Canada

Exclusion Criteria:

\-

Outcomes:
Investigate the ability of spectroscopy to detect high risk HPV in urine
Correlation of Oxidative Stress and Oocyte Quality in Fertility Preservation
Trial ID: NCT05372549
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-06-01
Completion date: 2025-01-03
Sponsor: Clinique Ovo
Collaborators: Ferring Pharmaceuticals
Conditions: Fertility Preservation
Interventions: Oxidative stress; cf-DNA; MAGENTA AI
Locations: Olive Fertility Centre; Ottawa Fertility Centre; Clinique Ovo
Countries: Canada; Canada; Canada
Enrollment: 179
Age range: 18 Years - 37 Years
Sex: FEMALE

Summary:
Whether it is for oncology or personal reasons, fertility preservation allows participants to preserve their oocytes (eggs) at their current age and condition for a better chance of having a baby in the future.

In order to proceed with fertility preservation, participants will need to undergo in vitro fertilization (IVF), which is standard practice, and take stimulation medication to stimulate production of more follicles.

The combination of both Rekovelle and Menopur for fertility preservation may increase ovarian response to stimulation and increase the number of oocytes retrieved to give better chances of embryos once the oocytes will be fertilized.

The goal in this study is to understand the effect of oxydative stress on the quality of oocytes.

For the purpose of the study and to evaluate this connection, serum levels of oxidative stress and cf-DNA in your blood will be measured.

Oocyte quality will also be assessed with Magenta, a prediction tool utilizing artificial intelligence (AI). This method is non-invasive and has no risks on the oocytes.

Eligibility criteria:
Inclusion Criteria:

\- Women 18 to 37 years of age inclusively eligible for fertility preservation for either social or oncology reason

Exclusion Criteria:

* Pregnancy and lactation
* Uncontrolled thyroid or adrenal dysfunction
* Tumours of the hypothalamus or pituitary gland
* Ovarian enlargement not due to polycystic ovarian syndrome
* Gynaecological haemorrhages of unknown aetiology
* Endometriosis stage III/IV
* Use of hormonal preparations (except for thyroid medication) during the last menstrual cycle
* Use of Coenzyme Q10 during the stimulation cycle
* Renal and/or hepatic impairment
* Hypersensitivity to any active ingredient or excipients in follitropin delta and/or HP-hMG

Outcomes:
Evaluate the correlation between the oxidative stress and the quality of oocytes
Self-supervised Learning for Classifying Bowel Obstruction on Upright Abdominal Radiography
Trial ID: NCT06321614
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-12-31
Completion date: 2024-12-31
Sponsor: The First Affiliated Hospital of Soochow University
Conditions: Digestive System Disease; Polyp of Colon; Bowel Disease
Locations: TheFirst Affiliated Hospital of Soochow University
Countries: China
Enrollment: 4500
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
Background: Accurate labeling of obstruction site on upright abdominal radiograph is a challenging task. The lack of ground truth leads to poor performance on supervised learning models. To address this issue, self-supervised learning (SSL) is proposed to classify normal, small bowel obstruction (SBO), and large bowel obstruction (LBO) radiographs using a few confirmed samples.

Methods: A few number of confirmed and a large number of unlabeled radiographs were categorized based on the ground truth. The SSL model was firstly trained on the unlabeled radiographs, and then fine-tuned on the confirmed radiographs. ResNet50 and VGG16 were used for the embedded base encoders, whose weights and parameters were adjusted during training process. Furthermore, it was tested on an independent dataset, compared with supervised learning models and human interpreters. Finally, the t-SNE and Grad-CAM were used to visualize the model's interpretation.

Eligibility criteria:
Inclusion Criteria:

1. The hospital imaging system looked for plain abdominal standing films diagnosed as intestinal obstruction or normal between 2022 and 2024
2. Aged 18 to 80 years
3. The main complaint was gastrointestinal symptoms

Exclusion Criteria:

1. Image interference, fuzzy performance, difficult to distinguish
2. Non-gastrointestinal symptoms were the main complaint
3. Supine, prone, or lateral decubitus radiography
4. Paralytic obstruction, closed loop obstruction, et al

Outcomes:
Diagnostic and classification performance
Profiling of Radiological Factors in Treatment and Outcomes in Prostate Cancer
Trial ID: NCT03354416
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2018-02-26
Completion date: 2033-12-31
Sponsor: National Cancer Institute (NCI)
Conditions: Prostatic Cancer; Prostatic Neoplams; Prostatic Hyperplasia; Prostate Cancer; Cancer Of Prostate
Locations: National Institutes of Health Clinical Center
Countries: United States
Enrollment: 10000
Age range: 18 Years - None
Sex: MALE

Summary:
Background:

Prostate cancer is one of the most common cancers for men in the U.S. There are some new ways to take pictures of the cancer. There are also new ways to use image-guided biopsy and therapy. These could help manage prostate cancer. Researchers want to study how imaging can provide a profile of prostate cancer. They want to collect data to make diagnosis and treatments better.

Objectives:

To gather data about the radiological and clinical course of prostate cancer. To study imaging-based biomarkers of prostate cancer.

Eligibility:

Men ages 18 and older with diagnosed or suspected prostate cancer

Design:

Participants will give permission for researchers to use their medical history and records. Their data will be reviewed, collected, and analyzed. These include results of their tests and scans.

Sponsoring Institution: National Cancer Institute

Eligibility criteria:
* INCLUSION CRITERIA:
* Patients with an increased risk for prostate cancer (strong family history and/or germline mutation in DNA repair genes), or with a diagnosis of prostatic cancer or suspicious for prostatic cancer lesions.
* Age greater than or equal to 18 years
* Ability of subject to understand and the willingness to sign a written informed consent document.

EXCLUSION CRITERIA:

-none

Outcomes:
Associations between imaging features and clinicopathological factors
Capital's Funds for Health Improvement and Research
Trial ID: NCT05424614
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-05-13
Completion date: 2024-12-30
Sponsor: Beijing Neurosurgical Institute
Conditions: Cerebral Hemorrhage; Prognosis; Artificial Intelligence
Interventions: Functional outcome follow-up of patients
Locations: Beijing Tiantan Hospital, Capital Medical University
Countries: China
Enrollment: 150
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
Spontaneous intracerebral hemorrhage(SICH) is the most lethal and disabling stroke. Timely and accurate assessment of patient prognosis could facilitate clinical decision making and stratified management of patients and is important for improving patient clinical prognosis. However, current studies on the prediction of prognosis of patients with SICH are limited and only include a single variable, with less precise results and inconvenient clinical application, which may lead to delays in effective patient treatment. Our group's previous studies on SICH showed that hematoma heterogeneity and the degree of contrast extravasation within the hematoma are closely related to the clinical outcome of patients, but they are difficult to describe quantitatively based on imaging signs. Based on this, we propose to use radiomics to quantitatively extract hematoma features from NCCT and CTA images, combine them with patients' clinical information and laboratory tests, study their relationship with the prognosis of cerebral hemorrhage, and use artificial intelligence to establish a rapid and accurate prognostic prediction model for patients with SICH, which is of great significance to guide clinical individualized treatment.

Eligibility criteria:
Inclusion Criteria:

* 1\. aged 18-80 years; 2. patients diagnosed with acute cerebral hemorrhage by CT examination; 3. complete non-contrast CT and CTA images; 4. the time interval from onset to the first baseline CT and CTA examination is less than 6 hours; 5. follow-up data within 3 months; 6. agree and sign a written document.

Exclusion Criteria:

* 1\. Patients with secondary aneurysm hemorrhage; 2. Patients with secondary hemorrhage of cerebrovascular malformation; 3. Patients with dissecting aneurysm hemorrhage; 4. Patients with cerebral infarction hemorrhage transformation; 5. Patients lost to follow-up within 3 months; 6. CT or CTA images have a heavy artefact.

Outcomes:
patient outcome
A Study to Evaluate Accuracy and Validity of the Chang Gung Atrial Fibrillation Detecting Software
Trial ID: NCT05872516
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-07-11
Completion date: 2023-04-10
Sponsor: Chang Gung Memorial Hospital
Conditions: Atrial Fibrillation
Interventions: Chang Gung Atrial Fibrillation Detecting Software
Locations: Chang Gung memorial hospital
Countries: Taiwan
Enrollment: 788
Age range: 20 Years - 100 Years
Sex: ALL

Summary:
Chang Gung Atrial Fibrillation Detection Software is an artificial intelligence electrocardiogram signal analysis software that detects whether a patient has atrial fibrillation by static 12-lead ECG signals. This study is a non-inferiority test based on the control group. The main purpose is to verify whether Chang Gung atrial fibrillation detection software can correctly identify atrial fibrillation in patients with atrial fibrillation, and can be used to provide a reference for doctors to detect atrial fibrillation.

Eligibility criteria:
Inclusion Criteria:

* Equal or greater than twenty years old
* Static 12-lead electrocardiogram of General Electric MUSE XML format file.
* The data comes from the static 12-lead electrocardiogram device of General Electric (model MAC5500).
* The electrocardiogram signal is 500 Hz.
* The Alternating current (AC) filter of the electrocardiogram signal is 60 Hz.

Exclusion Criteria:

* Cases used in the model development process.
* Lacks any electrode.
* Contain any electrode lacks a segment.
* Misplaced leads

Outcomes:
Sensitivity
Research on Delirium Recognition in Neurocritical Patients Based on Facial Expression Behavior Patterns
Trial ID: NCT07136207
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-08-30
Completion date: 2026-01-30
Sponsor: Beijing Tiantan Hospital
Collaborators: Affiliated Jinyang Hospital of Guizhou Medical University; The Affiliated Hospital Of Guizhou Medical University
Conditions: Delirium; Artificial Intelligence (AI)
Locations: Beijing Tiantan Hospital
Countries: China
Enrollment: 1000
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
This research project employs machine learning algorithms integrated with computer vision, image processing, and pattern recognition technologies to perform digital analysis of facial expression behaviors in neurocritical care patients with delirium. By constructing multidimensional high-level features of delirium, the investigators have established a classification model based on behavioral. The primary objective of this study is to address the critical challenge of achieving precise and efficient delirium diagnosis in neurologically critically ill patients through automated facial expression behavior recognition.

Eligibility criteria:
Inclusion Criteria:

1. Neurocritical patients admitted to the ICU, including postoperative neurosurgical patients, stroke patients, and those receiving ICU care due to other neurological conditions.
2. Age over 18 years.
3. Signed informed consent.

Exclusion Criteria:

1. Age under 18 years.
2. Persistent coma (GCS ≤ 8) within 7 days pre- and post-surgery, making delirium assessment impossible.
3. Did not survive more than 24 hours in the ICU.
4. Patients with facial paralysis, post-traumatic facial disfigurement, or other conditions that could significantly affect facial recognition.
5. Exclusion of patients with severe dementia, Parkinson's disease, depression, or other conditions that might impact facial emotional expressions.

Outcomes:
Accuracy of the delirium prediction model; Sensitivity of the delirium prediction model; Specificity of the delirium prediction model
To Explore the Application Value of Magnetic Resonance Imaging in Noninvasive Quantitative Evaluation of Graft Function and Systemic Metabolism After Renal Transplantation
Trial ID: NCT07145944
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-09-01
Completion date: 2031-12-12
Sponsor: Tongji Hospital
Conditions: Kidney Transplant Failure and Rejection; Transplantation, Kidney; Kidney Transplant Dysfunction
Enrollment: 500
Age range: None - None
Sex: ALL

Summary:
At present, renal biopsy is the gold standard for evaluating the pathology of renal transplants, but it is invasive and has the risk of serious complications; and the sampled tissue is only a small part of the kidney, which is prone to sampling bias and lacks reliable and comprehensive detection results. Therefore, it is an urgent problem to develop a non-invasive dynamic detection method for renal insufficiency and transplanted kidney.

With the continuous development and updating of technology, imaging provides a new way for non-invasive evaluation of renal allograft pathology including rejection reaction, acute renal allograft injury, viral infection, etc. MRI technology has developed the diagnosis of renal allograft rejection, fibrosis and other renal allograft dysfunction from macroscopic simple biomorphological changes to microscopic complex pathophysiological changes due to its high resolution of soft tissue and its ability to perform multi-parameter analysis.

In recent years, under the background of precision medicine, artificial intelligence technologies such as radiomics and machine learning are rapidly becoming very promising auxiliary tools in the evaluation of transplanted kidney images. They can extract and learn features in images with high throughput, make greater use of information that cannot be recognized by human eyes in medical images, and realize disease diagnosis, prognosis evaluation, and curative effect prediction by establishing models. However, most of the current research is in the preliminary stage. There are few evaluation studies on kidney transplantation. It is believed that with the continuous improvement of algorithms and optimization of models, radiomics and machine learning will make great progress, which will promote the development of individualized and precise medicine for patients with renal insufficiency to a certain extent.

Eligibility criteria:
Inclusion Criteria:

* (1) Patients with MR examination after kidney transplantation;

  (2) Age/gender: unlimited;

  (3) Patients who voluntarily participate in clinical trials and sign written subject informed consent

Exclusion Criteria:

\- (1) Patients with pacemakers, unknown materials, metal implants, neurostimulators, claustrophobia, etc.

(2) Patients who cannot tolerate adequate breath-holding for adequate MR examination;

Outcomes:
ESKD
Refining mUltiple Artificial intelliGence strateGies for Automatic Pain Assessment Investigations: RUGGI Study
Trial ID: NCT07038434
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-05-06
Completion date: 2026-01
Sponsor: Valentina Cerrone
Collaborators: University of Salerno, Italy; Federico II University
Conditions: Chronic Pain; Cancer Pain; Neuropathic Pain; Pain Assessment
Interventions: Multimodal AI-Based Pain Assessment
Locations: Azienda Ospedaliera Universitaria San Giovanni di Dio e Ruggi d'Aragona
Countries: Italy
Enrollment: 200
Age range: 18 Years - None
Sex: ALL

Summary:
This single-center, non-profit, observational-interventional study aims to develop artificial intelligence (AI) models for the automatic assessment of chronic pain (APA - Automatic Pain Assessment). The study will enroll adult patients with chronic pain of various origins (oncologic and non-oncologic). Participants will undergo multidimensional evaluations that include clinical assessments, self-report questionnaires, bio-signal collection (e.g., EEG, EDA, HRV, GSR, PPG), and facial expression analysis via infrared thermography and video recordings.

The primary objective is to calibrate and test machine learning and deep learning models to recognize and predict the presence and severity of pain using multimodal data inputs. Secondary objectives include evaluating the effectiveness of pain treatments, assessing quality of life, and developing a standardized APA dataset for future research.

All data collection procedures are non-invasive and safe, and include tools like wearable sensors and standardized neurocognitive tests. The study is approved by the Italian Ethics Committee (Comitato Etico Territoriale Campania 2) and complies with GDPR and EU AI regulations.

Eligibility criteria:
Inclusion Criteria:

* Adults (≥18 years old) with chronic pain, defined according to IASP and ICD-11 as pain that persists or recurs for more than three months.
* Diagnosed with either:
* Chronic primary pain (e.g., fibromyalgia, irritable bowel syndrome, chronic headaches)
* Chronic secondary non-cancer pain (e.g., low back pain, osteoarthritis, post-surgical pain)
* Chronic cancer-related pain (due to cancer or its treatment)
* Ability to understand the study procedures and provide written informed consent.

Exclusion Criteria:

* Current treatment with psychotropic drugs or presence of active psychiatric disorders (e.g., psychosis, major depression).
* Known history of alcohol or substance abuse.
* Pregnancy or breastfeeding.
* Age under 18 years.
* Inability to provide informed consent (e.g., due to cognitive impairment).

Outcomes:
Accuracy of AI models in classifying chronic pain; Sensitivity of AI models in classifying chronic pain; Specificity of AI models in classifying chronic pain; Precision of AI models in classifying chronic pain; F1-score of AI models in classifying chronic pain; AUC-ROC of AI models in classifying chronic pain
Artificial Intelligence Echocardiogram Interpretation for Systematic Integration and Guided Healthcare Transformation (The AI ECHO INSIGHT Study)
Trial ID: NCT07229300
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-11-01
Completion date: 2026-06-01
Sponsor: Kaiser Permanente
Conditions: Echocardiography, Transthoracic; Echocardiographic Software
Interventions: AI-Assisted Echocardiogram Interpretation; Sonographer-Assisted Echocardiogram Interpretation; Cardiologist-Only Echocardiogram Interpretation
Locations: Kaiser Permanente Northern California Division of Research
Countries: United States
Enrollment: 1200
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this blinded randomized controlled trial is to assess whether AI-assisted TTE interpretation can improve cardiologist efficiency and reduce interpretation variability while preserving accuracy compared to current methods in adult patients whose historical echocardiogram images were performed.

The main questions it aims to answer is to:

1. Assess overall transthoracic echocardiogram (TTE) interpretation accuracy of an AI-assisted workflow compared to standard cardiologist-only and sonographer-assisted workflows to hypothesize if AI-assisted workflow for echocardiography interpretation is non-inferior in accuracy compared to standard workflows.
2. Evaluate cardiologist interpretation time using an AI-assisted workflow compared to standard cardiologist-only and sonographer-assisted workflows to determine if an AI-assisted workflow will result in a decrease in interpretation time compared to the cardiologist-only workflow.
3. To compare interpretation consistency and reliability compared to historical reports as well as consensus reports of all finalized cardiologist reports.

Eligibility criteria:
Inclusion Criteria:

* ≥ 18 years of age
* Comprehensive transthoracic echocardiogram study performed a KPNC Medical Center.

Exclusion Criteria:

* Known Congenital heart disease study
* Known Ventricular assist device
* Limited transthoracic echocardiogram study
* Transesophageal echocardiogram study
* Stress echocardiogram study

Outcomes:
Accuracy of AI-Assisted Echocardiogram Interpretation Compared to Standard Workflows.