moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Multimodal Imaging in Vitreo-retinal Surgery and Macular Dystrophies: Biomarkers of Morpho-Functional Recovery by Artificial Intelligence
Trial ID: NCT05747144
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-01-15
Completion date: 2025-01-16
Sponsor: Fondazione Policlinico Universitario Agostino Gemelli IRCCS
Conditions: Macular Holes; Epiretinal Membrane; Retinal Detachment; Macular Dystrophies
Interventions: Biometry; Retinography (Color) + Autofluorescence (AF); OCT B-scan and OCT angiography (OCTA); Microperimetry; Electrophysiological exams
Locations: Prof. Stanislao Rizzo
Countries: Italy
Enrollment: 100
Age range: 18 Months - None
Sex: ALL

Summary:
The aim of the study is to identify morphological and functional biomarkers of post-operative recovery after vitreoretinal surgery, using decisional support systems (DSS), based on multimodal big-data analysis by means of machine learning techniques in daily clinical practice

Eligibility criteria:
Inclusion Criteria:

* All patients to undergo vitreo-retinal surgery for:

  1. Macular hole
  2. Epiretinal membranes
  3. Retinal detachment
  4. Macular dystrophies (retinal pre-prosthesis)

Exclusion Criteria:

* Patients under 18 years of age will be excluded; patients in whom morphological examinations cannot be performed due to poor cooperation or opacity of the dioptric media (e.g. corneal pathology). Quality of morphological images inadequate for post acquisition processing (\<6/10).

Outcomes:
Predictivity of morphological-functional radiomic data
Prediction of Postoperative Visual Acuity in Cataract Patients Using a Macular Optical Coherence Tomography-based Deep Learning Method
Trial ID: NCT04887909
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2021-05-20
Completion date: 2026-12-31
Sponsor: Second Affiliated Hospital, School of Medicine, Zhejiang University
Conditions: Data Collecting
Locations: Second Affiliated Hospital, Zhejiang University School of Medicine
Countries: China
Enrollment: 1100
Age range: 50 Years - 90 Years
Sex: ALL

Summary:
The purpose of this study is to collect the macular OCT images and preoperative and postoperative visual acuity of cataract patients who had been operated in the eye center of the Second Affiliated Hospital of Zhejiang University Medical College, and to train a model that can relatively accurately predict the postoperative visual acuity of patients by deep learing.

Eligibility criteria:
Inclusion Criteria:

* Senile cataract patients, with or without macular disease, the impact of cataract on vision has affected the daily life of patients.

Exclusion Criteria:

* Glasses can obviously improve eyesight
* In addition to macular disease, combined with other eye diseases that seriously affect vision, resulting in no significant improvement in postoperative vision
* Complicated cataract surgery due to trauma and other reasons
* Combined with other eye diseases not suitable for intraocular lens implantation
* Patients with systemic diseases who cannot tolerate surgery

Outcomes:
Uncorrected distance visual acuity; Best Corrected Visual Acuity; Macular optical coherence tomography
The Role of Artificial Intelligence in Endoscopic Diagnosis of Esophagogastric Junctional Adenocarcinoma：A Single Center, Case-control, Diagnostic Study
Trial ID: NCT05819099
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-12
Completion date: 2026-04
Sponsor: Qilu Hospital of Shandong University
Conditions: Stomach Neoplasms
Interventions: An Intelligent Endoscopic Diagnosis System Developed and Verified Based on Deep Learning
Enrollment: 200
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
This is a single center, case-control, diagnostic study.The aim of this study is to use deep learning methods to retrospectively analyze the imaging data of gastrointestinal endoscopy in Qilu Hospital, and construct an artificial intelligence model based on endoscopic images for detecting and determining the depth of invasion of esophagogastric junctional adenocarcinoma.This study will also compare the established AI model with the diagnostic results of endoscopists to evaluate the clinical auxiliary value of the model for endoscopists.The research includes stages such as data collection and preprocessing, artificial intelligence model development, model testing and evaluation. The gastroscopy image dataset constructed by this research institute mainly includes three modes of endoscopic imaging: white light endoscopy, optical enhancement endoscopy (OE), and narrowband imaging endoscopy (NBI).

Eligibility criteria:
Inclusion Criteria:

* This study included endoscopic images of patients aged 18 and above who underwent endoscopic examination or treatment
* All patients in the case group need to be pathologically confirmed as esophageal gastric junction adenocarcinoma, and a pathologist has conducted a standardized pathological evaluation of the tumor classification of the lesion, including the overall appearance, size, differentiation type, depth of infiltration, presence or absence of lymphatic/vascular invasion, surgical margin status, etc.
* The endoscopic images of the control group patients need to be confirmed by biopsy pathology or at least two experienced endoscopists (with operating experience\>5000 cases) to jointly confirm that they have clear benign manifestations

Exclusion Criteria:

* The patient has a previous history of endoscopic treatment or surgery for the esophageal gastric junction.
* Necessary clinical information cannot be provided during the research process (patient age, gender, lesion characteristics, endoscopic manifestations, endoscopic images, etc.)
* Low quality endoscopic images, such as those severely affected by bleeding, aperture, blurring, defocusing, artifacts, or excessive mucus after biopsy.

Outcomes:
Sensitivity; Specificity; Negative predictive value; Positive predictive value; Accuracy
Pervasive Sensing and Artificial Intelligence in Intelligent ICU Subtitles: -Intelligent Intensive Care Unit (I2CU): Pervasive Sensing and Artificial Intelligence for Augmented Clinical Decision-making -ADAPT: Autonomous Delirium Monitoring and Adaptive Prevention
Trial ID: NCT05127265
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2021-05-24
Completion date: 2026-12
Sponsor: University of Florida
Collaborators: National Institute of Neurological Disorders and Stroke (NINDS); National Institute for Biomedical Imaging and Bioengineering (NIBIB)
Conditions: Critical Illness; Pain; Delirium; Confusion
Interventions: Video Monitoring; Accelerometer Monitoring; Noise Level Monitoring; Light Level Monitoring; Air Quality Monitoring; EKG Monitoring; Vitals Monitoring; Biosample Collection; Delirium Motor Subtyping Scale 4 (DMSS-4)
Locations: University of Florida Health Shands Hospital
Countries: United States
Enrollment: 400
Age range: 18 Years - None
Sex: ALL

Summary:
Important information related to the visual assessment of patients, such as facial expressions, head and extremity movements, posture, and mobility are captured sporadically by overburdened nurses, or are not captured at all. Consequently, these important visual cues, although associated with critical indices such as physical functioning, pain, delirious state, and impending clinical deterioration, often cannot be incorporated into clinical status. The overall objectives of this project are to sense, quantify, and communicate patients' clinical conditions in an autonomous and precise manner, and develop a pervasive intelligent sensing system that combines deep learning algorithms with continuous data from inertial, color, and depth image sensors for autonomous visual assessment of critically ill patients. The central hypothesis is that deep learning models will be superior to existing acuity clinical scores by predicting acuity in a dynamic, precise, and interpretable manner, using autonomous assessment of pain, emotional distress, and physical function, together with clinical and physiologic data.

Eligibility criteria:
Inclusion Criteria:

* aged 18 or older
* admitted to UF Health Shands Gainesville ICU ward
* expected to remain in ICU ward for at least 24 hours at time of screening

Exclusion Criteria:

* under the age of 18
* on any contact/isolation precautions
* expected to transfer or discharge from the ICU in 24 hours or less
* unable to provide self-consent or has no available proxy/LAR

Outcomes:
Algorithmic Activity Labeling; Algorithmic Pain Labeling; Decibel Levels; Lux Levels; Air Quality; Circadian Dyssynchrony Index; Algorithmic Delirium Recognition Profile; Delirium Motor Subtyping Scale 4 (DMSS-4)
The Use of the Hypotension Prediction Index (HPI) Combined With the Assisted Fluid Management (AFM) Software for Perioperative Hemodynamic Optimization in Patients Undergoing General Anesthesia.
Trial ID: NCT07301307
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-03-15
Completion date: 2027-03
Sponsor: National and Kapodistrian University of Athens
Collaborators: Attikon Hospital
Conditions: Intraoperative Hypotension
Interventions: Acumen IQ sensor with Hypotension Prediction Index (HPI) and Assisted Fluid Management (AFM) software; Conventional Intraoperative Hemodynamic management
Locations: 2nd University Department of Anesthesiology, Attikon University Hospital
Countries: Greece
Enrollment: 100
Age range: 18 Years - None
Sex: ALL

Summary:
This study investigates if the Hypotension Prediction Index (HPI) combined with the Assisted Fluid Management (AFM) software can improve perioperative hemodynamic management in adult patients undergoing general anesthesia. The main question is :

Does the HPI and AFM software reduce the incidence and duration of intraoperative hypotension? Does the HPI and AFM software optimize fluid and vasopressor administration? Does the HPI and AFM software improve perioperative outcomes? Participants will be randomly allocated to either an experimental group receiving goal directed hemodynamic therapy guided by HPI and AFM or a control group receiving conventional hemodynamic management.

Eligibility criteria:
Inclusion Criteria:

* Age \> 18 years
* Intraoperative monitoring \> 2 hours or general anesthesia \> 2 hours
* Invasive arterial pressure monitoring
* Target MAP ≥ 65 mm Hg intraoperatively
* Written informed consent preoperatively
* ASA Physical Status ≤ 4

Exclusion Criteria:

* Target MAP other than 65 mm Hg
* Severe preoperative hypotension (MAP \< 65 mm Hg)
* Severe heart failure (e.g. LVEF \< 20%)
* Emergency surgery

Outcomes:
Time-weighted average of intraoperative hypotension
Novel Sensors and Artificial Intelligence for Detection of Acute Pulmonary Exacerbations in Cystic Fibrosis and Non-Cystic Fibrosis Bronchiectasis
Trial ID: NCT07289100
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-09-30
Completion date: 2026-04-30
Sponsor: Papworth Hospital NHS Foundation Trust
Collaborators: LifeArc; University of Cambridge; Department of Health, United Kingdom; National Institute for Health Research, United Kingdom
Conditions: Cystic Fibrosis (CF); Bronchiectasis Adult
Interventions: Non-Interventional Study
Locations: Royal Papworth Hospital NHS Foundation Trust
Countries: United Kingdom
Enrollment: 50
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this observational study is to learn about new sensors that measure changes in the body that happen during a chest infection, such as breathing rate and heart rate. This study will be with adults who have either cystic fibrosis or bronchiectasis who start treatment with intravenous antibiotics (usually for around 2 weeks).

The main question it aims to answer is the change in sensor measure (such as heart rate) between the start and end of treatment for a chest infection.

No follow up visits are required for this study.

Eligibility criteria:
Inclusion Criteria:

* ≥ 18 years old
* Diagnosis of CF based on genetic testing and /or sweat chloride levels or CT confirmed diagnosis of non-CF bronchiectasis
* Under the care of Royal Papworth Hospital
* Starting IV antibiotics for treatment of APE as defined by the Hill Criteria ²

Exclusion Criteria:

* Patients unable to provide written informed consent
* Lung transplant recipients or on lung transplant waiting list
* Use of long-term oxygen therapy (LTOT) or non-invasive ventilation to manage respiratory failure
* Patients unwilling to consent to their link anonymised data from home monitoring being used for research
* Considered unsuitable for home monitoring in the opinion of the investigator
* Patients being admitted for elective antibiotic treatment (i.e. not being admitted for acute APE treatment)

Outcomes:
The change in physiological parameter measure such as heart rate (sensor dependant) between the start and end of intravenous antibiotics for acute pulmonary exacerbation.
Clinical Characterization of CoVid19 Infection: Prognostic Stratification and Complications
Trial ID: NCT04463706
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-06-01
Completion date: 2022-12-31
Sponsor: Hospital Galdakao-Usansolo
Collaborators: Hospital Costa del Sol; Hospital del Mar; University Hospital of the Nuestra Señora de Candelaria; Instituto de Salud Carlos III; Hospital Universitario Araba; Biobizkaia Health Research Institute; Hospital de Basurto; Hospital Donostia
Conditions: Covid19
Interventions: Predictors adverse evolution; Predictors of health care provide
Locations: Hospital Galdakao-Usansolo
Countries: Spain
Enrollment: 380000
Age range: None - None
Sex: ALL

Summary:
1\. Objectives: 1.-To create risk stratification scales of poor evolution in patients infected by SARS-CoV-2. 2.-Evaluate the accessibility and equity that these patients have had in the different care processes, diagnostic and therapeutic procedures, with special interest in patients who came from residences, by age, gender or geographic origin.3.-Evaluate the effectiveness of different therapeutic schemes that have been used in this pandemic. 4.-Evaluate the effectiveness of different diagnostic tests used to predict the poor evolution of these patients 5.- Evaluate the real costs associated with the treatment of hospitalized patients with COVID-19 ; 2. Methods: Information will be recorded from electronic medical record: epidemiological data, onset of symptoms, comorbidities and their treatments, symptoms, analytical data, vital signs, tests performed, treatments during admission and evolution up to 3 months after discharge. Statistical analysis: The investigators will use classic survival models, logistic regression, generalized linear models and also analysis using artificial intelligence techniques . Health care costs are assessed. Applications for decision making will be derived as a product.

Eligibility criteria:
Inclusion Criteria:

* Positive COVID19 people in the Basque country
* Patients admitted (confirmed cases) by CoVid-19

Exclusion Criteria:

* Pediatric population (for objective #1 only)

Outcomes:
Clinical deterioration
A Non-Invasive Artificial Intelligence Model for the Prediction of Human Blastocyst Ploidy: a Multi-Center, Prospective, Non-Randomized Validation Study
Trial ID: NCT06762704
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-02
Completion date: 2027-12
Sponsor: The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School
Collaborators: RenJi Hospital; Jiangxi Maternal and Child Health Hospital; The Second Affiliated Hospital of Air Force Military Medical University; The First Affiliated Hospital of USTC (Anhui Provincial Hospital)
Conditions: The Focus is to Validate the Accuracy of a Non-invasive Artificial Intelligence Model for the Prediction of the Human Blastocyst Ploidy; Preimplantation Genetic Testing (PGT)
Interventions: Video recording
Locations: The First Affiliated Hospital of USTC (Anhui Provincial Hospital); The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School; Jiangxi Maternal and Child Health Hospital; Tangdu Hospital of Air Force Military Medical University; Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine
Countries: China; China; China; China; China
Enrollment: 1408
Age range: 20 Years - 55 Years
Sex: ALL

Summary:
The goal of this clinical trial is to evaluate an artificial intelligence model for the prediction of human blastocyst ploidy without invasive procedures in couples that receive preimplantation genetic testing. The main questions it aims to answer are:

* Is an artificial intelligence model able to predict the ploidy status of a human blastocyst based on its 3D morphology?
* Do quantitative 3D morphological parameters of trophectoderm cells and inner cell mass have strong correlations with human blastocyst ploidy status?

Videos that include multi-view images of each blastocyst from participants will be collected on Day 5/6 of culture, and preimplantation genetic testing results of these blastocysts will be collected 4-8 weeks after trophectoderm biopsy.

Eligibility criteria:
Inclusion Criteria:

* Maternal age between 20 and 40; paternal age between 20 and 55.
* Preimplantation genetic testing (PGT) cycles, including PGT for aneuploidy, PGT for monogenic disorders (PGT-M) or PGT for structural chromosome defect (PGT-SR).
* Having at least one Day 5/6 blastocyst developed from two-pronuclear (2PN) embryo which is suitable for trophectoderm biopsy (i.e., degree of expansion: IV, and at least a grade better than C for trophectoderm and inner cell mass grading).
* Couples with written informed consent.

Exclusion Criteria:

* Couples with contraindications for IVF or PGT.
* Women with all oocytes frozen after retrieval.
* Couples who fail to follow the study protocol.
* Couples deemed ineligible for enrollment by the investigator in consideration of study protocol and treatment safety.

Outcomes:
Sensitivity; Specificity
Identifying Electrocardiographic Markers on a Standard 12-lead ECG to Determine Which Patients Are Eligible for the Implantation of a Subcutaneous Defibrillator.
Trial ID: NCT07157449
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-10-01
Completion date: 2027-06-01
Sponsor: Nantes University Hospital
Conditions: Defibrillator; ECG
Locations: CHU de Nantes
Countries: France
Enrollment: 1000
Age range: 18 Years - None
Sex: ALL

Summary:
The S-ICD defibrillator developed by Boston Scientific Inc. is a fully subcutaneous automatic defibrillation system that prevents and treats sudden cardiac arrest. The subcutaneous system offers the advantage of avoiding the risks associated with transvenous access, limiting serious infections and lead failures. The detection system is based on the identification of rapid ventricular arrhythmias using one of three bipolar vectors defined by the subcutaneous lead (distal and median electrode) and the generator. However, before implanting the device in a patient, it is essential to carry out a screening procedure to ensure that the patient's QRS signal amplitude is sufficient to be detected by the S-ICD. This screening procedure is carried out by recording a specific three-lead electrocardiogram (ECG), which represents the three bipolar vectors of the S-ICD. The aim of this research is to show that the recording of a standard 12-lead ECG can be sufficient to predict the eligibility of patients for S-ICD implantation. More specifically, it aims to identify electrocardiographic markers on a standard 12D-ECG that can be used to determine which patients may benefit from implantation of a subcutaneous defibrillator, thereby eliminating the need for a 3-lead screening ECG. The developments that will be carried out will be based on the development of signal analysis algorithms and decision support using artificial intelligence.

Eligibility criteria:
Inclusion Criteria :

* Patient undergoing cardiology consultation, whatever the pathology
* Patient over 18 years of age
* Patient having been informed and having given their oral non-opposition
* Patient and relatives affiliated to a social security scheme

Exclusion Criteria :

* Patients receiving cardiac stimulation
* Patient taking part in a therapeutic trial which may interfere with the results of the research
* Patients under guardianship.

Outcomes:
to assess the ratio between the size of the R wave corresponding to the depolarization of the ventricles; to assess the amplitude of the T wave (ventricular repolarization)
Validation of Insulin Dose Prediction Model Based on Long Short- Term Memory Artificial Intelligence Algorithm
Trial ID: NCT07066891
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-07-15
Completion date: 2026-06
Sponsor: Sun Yat-sen University
Conditions: Diabetes Mellitus; Type 2 Diabetes
Interventions: CSII in the prediction model group; CSII in the empirical group
Locations: Department of Endocrinology and Diabetes Center, The First Affiliated Hospital of Sun Yat-sen University
Countries: China
Enrollment: 400
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
The present study aims to conduct a prospective controlled trial comparing an LSTM-based artificial intelligence (AI) prediction model and clinicians' experience in the efficacy and safety of blood glucose control in hospitalized patients with type 2 diabetes mellitus (T2DM) receiving continuous subcutaneous insulin infusion (CSII) treatment in the Department of Endocrinology. The main question it aims to answer is:

Is the prediction model superior to or (at least) non-inferior to clinicians' experience?

Eligible patients who receive CSII treatment are randomly allocated into the prediction model group and the empirical group. Patients will:

1. Receive CSII treatment as standard of care during hospitalization for 1-2 weeks, where the daily insulin dose regimen is determined by a prediction model or a clinician's experience.
2. Use continuous glucose monitoring (CGM) for glucose tracking.
3. Receive diabetes self-management education covering nutrition and physical activity.

Eligibility criteria:
Inclusion Criteria:

1. Meets the diagnostic criteria of type 2 diabetes mellitus in the Chinese Guidelines for the Prevention and Treatment of Type 2 Diabetes (2020 edition).
2. Insulin pump is used to control blood glucose during hospitalization, and the duration of CSII treatment period ≥6 days and \<30 days.

Exclusion Criteria:

1. Diabetes other than type 2.
2. Age ≥75 years who is not suitable for intensive insulin therapy.
3. Hypoglycemic regimen other than CSII treatment, such as oral hypoglycemic drugs or multiple daily insulin injections during hospitalization.
4. With severe infection or uncontrolled acute complications (including ketoacidosis coma, hyperosmolar hyperglycemia, etc.) , or any condition that the researcher believes not suitable for the study.
5. Severe hepatic and renal insufficiency (ALT≥5 times the upper limit of normal, eGFR\<30ml/min/1.73m2) ), or patients at the acute stage of cardiovascular and cerebrovascular diseases considered unsuitable for study.
6. Pregnancy.

Outcomes:
Time in range; Time below range