moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
MR Based Survival Prediction of Patients With Primary Glioma Ssing Deep Learning or Machine Learning
Trial ID: NCT04215211
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2017-01-01
Completion date: 2027-06-01
Sponsor: The First Affiliated Hospital of Zhengzhou University
Collaborators: Sun Yat-sen University
Conditions: Glioma
Interventions: Survival prediction for glioma patients
Locations: Department of Neurosurgery, First Affiliated Hospital of Zhengzhou University
Countries: China
Enrollment: 2500
Age range: 1 Year - 90 Years
Sex: ALL

Summary:
This registry aims to collect clinical, molecular and radiologic data including detailed survival data, clinical parameters, molecular pathology (1p/19q codeletion, MGMT methylation, IDH and TERTp mutations, etc) and conventional/advanced/new MR sequences (T1, T1c, T2, FLAIR, ADC, DTI, PWI, etc) of patients with primary gliomas. By leveraging artificial intelligence, this registry will seek to construct and refine algorithms that able to predict patients' survivals in the frame of molecular pathology or subgroups of gliomas.

Eligibility criteria:
Inclusion Criteria:

* Patients must have radiologically and histologically confirmed diagnosis of primary glioma
* Life expectancy of greater than 3 months
* Must receive tumor resection
* Signed informed consent

Exclusion Criteria:

* No gliomas
* No sufficient amount of tumor tissues for detection of molecular pathology
* Patients who have any type of bioimplant activated by mechanical, electronic, or magnetic devices
* Patients who are pregnant or breast feeding
* Patients who are suffered from severe systematic malfuctions

Outcomes:
AUC of survival prediction performance
Observational and Prospective Study of Hepatic Steatosis and Related Risk Factors Using Ultrasound and Artificial Intelligence
Trial ID: NCT06103175
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-01-15
Completion date: 2024-11-01
Sponsor: University of Bari
Collaborators: Eurisko Technology srl; Centro Radiologico Lucano
Conditions: Liver Steatoses
Locations: Department of Department of Precision and Regenerative Medicine and Ionian Area (DiMePre-J - Clinica medica "A. Murri"
Countries: Italy
Enrollment: 150
Age range: 18 Years - 70 Years
Sex: ALL

Summary:
Fatty liver is the most frequent chronic liver disease worldwide and ultrasonography is widely employed for diagnosis. The accuracy of this technique, however, is strongly operator-dependent. Few information is available, so far, on the possible use of algorithms based on Artificial Intelligence (AI) to ameliorate the diagnostic accuracy of ultrasonography in diagnosing fatty liver. This study showed that the use of AI is able to improve the diagnostic accuracy of ultrasonography in the diagnosis of fatty liver

Eligibility criteria:
Inclusion Criteria:

* Age between 18-70 years
* MRI regardless of clinical indications,
* written informed consent

Exclusion Criteria:

* cirrhosis
* hepatocellular carcinoma or any liver tumours,
* absence of the right kidney
* previous liver transplantation
* large liver cysts or kidney cysts

Outcomes:
Hepato-renal index calculation; Magnetic Resonance scanning and fat percentage evaluation
Artificial Intelligence - Assisted Colonoscopy in Diagnosis of Colorectal Neoplasms: Russian Multicenter Randomised Open - Label Trial
Trial ID: NCT06469671
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-07
Completion date: 2024-12
Sponsor: State Scientific Centre of Coloproctology, Russian Federation
Conditions: Colonic Neoplasms; Colonic Polyp; Diagnosis
Interventions: The system based on real - time artificial intelligence - assisted for colonoscopy
Locations: Airat Mingazov
Countries: Russia
Enrollment: 1000
Age range: 18 Years - 90 Years
Sex: ALL

Summary:
The primary goal of this study is to estimate the effectiveness of a medical decision support system based on artificial intelligence in the endoscopic diagnosis of benign tumors.

Researchers will compare Adenoma detection rate between "artificial intelligence - assisted colonoscopy" and "conventional colonoscopy" groups to evaluate the clinical effectiveness of artificial intelligence model.

Eligibility criteria:
Inclusion Criteria:

* Screening coloscopy is needed

Exclusion Criteria:

* Indications for colonoscopy
* Previously detected colorectal neoplasms
* Previously performed colorectal surgery
* Colorectal cancer
* Inflammatory bowel disease

Outcomes:
Adenoma detection rate
The Effects of Air Pollution on Pregnancy and Adverse Birth Outcomes
Trial ID: NCT06340971
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-11-01
Completion date: 2029-11-30
Sponsor: Queen Mary University of London
Collaborators: University College London Hospitals
Conditions: Premature Birth
Interventions: Policy
Locations: Tina Chowdhury; Anna David
Countries: United Kingdom; United Kingdom
Enrollment: 200000
Age range: 18 Years - None
Sex: FEMALE

Summary:
We are an inter-disciplinary team of UK scientists with expertise in obstetrics, women's and child health, epidemiology, climate science, inflammation, computational modelling, machine learning and artificial intelligence. Together we have a long history with existing strengths underlying preterm birth research that crosses multiple disciplines and an excellent track record of publications and awards leading research in preterm birth.

We aim to develop and validate a deep learning model to predict the risk of preterm birth and other adverse pregnancy outcomes using data from EPIC electronic health records at University College London Hospital Trust (UCLH) for a cohort of 18000 patients. We will obtain corresponding data on exposure to ambient pollution using non-identifiers for postcode (area) and date of delivery (month). The model will review the temporal sequence of events within a patient's medical history and current pregnancy, identifying significant interactions and will predict the risk of preterm birth. It will also determine the threshold and gestation at which pollution exposure has the greatest impact.

Eligibility criteria:
Inclusion Criteria:

* We aim to include data from pregnant women who delivered at University College London Hospitals from 2019 onwards after the start of the EPIC electronic patient record. The is no specified age range for this study, so as to improve inclusivity. We also aim to represent minority ethnic groups and patients with social deprivation within our dataset.

Exclusion Criteria:

* We will exclude data from patients with an incomplete duration of follow-up due to transfer of antenatal care for delivery at another trust. Patients with incomplete past obstetric history data, inaccurate estimations of gestational age (e.g. due to late booking of the pregnancy) and missing data for 'postcode of usual address' will also be excluded. Patients who are less than 18 years of age will be excluded.

Outcomes:
Machine learning model to predict the risk of preterm birth and adverse birth outcomes
Evaluation of Autonomic Activity in REM Sleep by a New Marker of Cardiac Variability in Subjects With Severe Obstructive Sleep Apnea Hypopnea Syndrome
Trial ID: NCT06808464
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-05-14
Completion date: 2027-02
Sponsor: University Hospital, Lille
Conditions: Sleep Apnea
Locations: Service de Neurophysiologie - unité de médecine du Sommeil - CHU Lille
Countries: France
Enrollment: 97
Age range: 18 Years - 64 Years
Sex: ALL

Summary:
The main objective of this study is to compare the different variations of the cardiac variability index measured thanks to the technique developed by the CIC-IT between subjects with severe SAS and healthy subjects with sleep pathology.

In a first step, these two groups will be compared in REM sleep phase because it is the period during which the risk of sleep apnea syndrome is the highest. In a second step, the same methodology will be applied for the other sleep phases and correlations will be established with the other markers of cardiac variability (HRV) already known. This will allow to validate a reliable method of screening for SAS that is more accessible in ambulatory settings by means of a machine learning system based on artificial intelligence

Eligibility criteria:
Inclusion Criteria:

* Group healthy subjects - 1): absence of sleep disorder referenced in the ICSD-3.

  * Epworth Sleep Scale \< 11
  * Pittsburgh Quality Sleep Index \< 6
  * Berlin Questionnaire: \< 2 positive categories
  * STOP BANG \<3
  * HADS normal
  * No RLS
* Group 2 (subjects with severe SAS - 2): AHI \> 30/h obstructive

Exclusion Criteria:

Non-inclusion criteria:

* Cardiac rhythm disorder
* Neurological or psychiatric pathology impacting on ANS
* Control group (healthy subjects - 1)

  * Complaint of sleep disorder
  * Known sleep disorder
* Experimental group (subjects with severe SAS - 2):

  o Associated sleep pathology at diagnosis of severe SAS
* Administrative reasons:

  * Unable to receive informed information,
  * Unable to participate in the entire study
  * Lack of social security coverage
  * Refusal to sign consent Exclusion criteria
* Control group (healthy subjects - 1):

  * Sleep pathology detected by polysomnography
  * Total sleep time less than 4 hours
  * Recording technically not usable

Outcomes:
Ability of the high frequency variability index (HFVI) measured in REM sleep phase to differentiate subjects with severe SAS and healthy subjects with sleep pathology
HEMOdynamic Proactive Management During Cytoreductive Surgery and Intraperitoneal hyPErthermic Chemotherapy for Advanced Ovarian Cancer
Trial ID: NCT07070973
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-09-01
Completion date: 2027-02-01
Sponsor: Fondazione Policlinico Universitario Agostino Gemelli IRCCS
Conditions: Ovarian Cancer; Hemodynamic Instability; Fluid Overload
Interventions: Assisted Fluid Management; Goal Directed Fluid Therapy (GDT)
Locations: IRCCS Fondazione Policlinico A. Gemelli
Countries: Italy
Enrollment: 52
Age range: 18 Years - None
Sex: FEMALE

Summary:
Cytoreductive surgery (CRS) with hyperthermic intraperitoneal chemotherapy (HIPEC) is a complex surgical procedure carried out through laparotomic approach. After CRS-HIPEC morbidity and mortality rates go up to 20%-40% and 3% respectively, and acute kidney injury and pulmonary effusion/oedema are the most frequent postoperative complications.

Intraoperative hypotension and risk of fluid overload are common. Efficient and accurate control of arterial pressure and cardiac output is a major concern during CRS-HIPEC.

The aim of this study is to perform a pilot study describing a hemodynamic management protocol based on artificial intelligence - derived parameters, that allows to implement the standard goal directed therapy (GDT) protocol in term of amount of IOH and stroke volume (SV) optimization.

Specifically, this study aims to test the hypotheses that a hemodynamic protocol based on HPI-AFM monitoring compared to standard GDT helps clinicians reduce IOH during surgery and improve the time "in-target" range of SV index.

The study cohort will be compared to an historical cohort of 50 patients underwent to CRS-HIPEC between 2022 and 2024, managed with an institutional goal directed therapy protocol.

Eligibility criteria:
Inclusion Criteria:

* age \> 18 years
* patient scheduled for CRS for ovarian cancer

Exclusion Criteria:

* patients' refusal
* cardiac arrythmia
* pregnancy.

Outcomes:
TWA-MAP<65mmHg; TIT-SVI
Middle East Coronary Artery Ectasia Registry (MECAER): Incidence, Management, and One-Year Prognosis: A Multi-Centre Prospective Cohort Study.
Trial ID: NCT05213429
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-09-01
Completion date: 2027-12-31
Sponsor: Jordanian Research and Artificial Intelligence Group
Conditions: Coronary Artery Ectasia
Interventions: non interventional study
Locations: Specialty Hospital
Countries: Jordan
Enrollment: 11000
Age range: 18 Years - None
Sex: ALL

Summary:
This research project is the first in Jordan and Middle East in providing detailed registration of coronary ectasia cases provided by their primary healthcare providers. The investigators responsible for conducting this research are physicians and their teams who deal with patients with coronary ectasia in their clinics including the public and private medical sectors.

Eligibility criteria:
Inclusion Criteria:

* Subjects with suspected CAD who are referred for elective coronary angiography and fit the Coronary ectasia definition.

Exclusion Criteria:

* Patient younger than 18 years old.
* Patients who refuse to sign a consent form . Patients who are enrolled in another Ectasia study

Outcomes:
Cardiovascular events
A Randomized Controlled Trial on the Impact of Using Generative Artificial Intelligence on Analytical Writing Performance and Cognitive Abilities
Trial ID: NCT06511102
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-07-18
Completion date: 2025-02-20
Sponsor: University Hospital Heidelberg
Conditions: Cognitive Change; Well-Being, Psychological
Interventions: GPT Support
Locations: Core Facility for Neuroscience of Self-Regulation, Heidelberg University
Countries: Germany
Enrollment: 160
Age range: 18 Years - 35 Years
Sex: ALL

Summary:
The goal of this randomized controlled lab experiment is to examine if using generated artificial intelligence (AI) technology will affect people's academic performance and cognitive abilities in the context of analytical writing among college students. The main questions it aims to answer are:

1. Does using the technology affect students' writing performance?
2. Does using the technology affect students' cognitive effort during the writing process?

Participants will be randomly assigned to either a control group, which is writing without AI assistance, or an experimental group, which is writing with the assistance of ChatGPT. Researchers will compare the two groups to see if ChatGPT affects students' writing performance and cognitive effort.

For each participant, the lab experiment will last for no more than 1.5 hours. An eye-tracker will monitor the participant's gaze activities and pupil size. A functional near-infrared spectroscopy (fNIRS) will monitor the participant's brain activities in the frontal lobe. During the experiment, participants will be asked to:

1. Read learning materials on analytical writing techniques.
2. Based on the previously provided materials, complete an analytical writing assignment that will take approximately 30 minutes either with or without the aid of ChatGPT.
3. Answer survey questions about their experience with the writing assignment, attitudes on using ChatGPT, and demographic backgrounds.

Eligibility criteria:
Inclusion Criteria:

* Full-time university student.
* Able to read and write in English.
* Use the computer most days of the week.
* Have not taken, nor currently preparing for, the Graduate Record Examinations (GRE).
* Do not wear glasses (contact lenses are allowed).
* Have no eye impairment.
* Not currently taking any opioids, epinephrine, or anti-hypertensive drugs.
* During the experiment, not wearing any makeup around the eyes.

Outcomes:
Writing Performance; Cognitive Effort Measured by Pupil Size
Comparing Clinical Decision-making of AI Technology to a Multi-professional Care Team in an Electronic Cognitive Behavioural Therapy Program for Depression
Trial ID: NCT05648175
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-12-01
Completion date: 2025-12-01
Sponsor: Dr. Nazanin Alavi
Conditions: Depression
Interventions: e-CBT; e-CBT + Phone Call; e-CBT + Phone Call + Pharmacotherapy
Locations: Hotel Dieu Hospital
Countries: Canada
Enrollment: 186
Age range: 18 Years - None
Sex: ALL

Summary:
Depression is a leading cause of disability worldwide, affecting up to 300 million people globally. Despite its high prevalence and debilitating effects, only one-third of patients newly diagnosed with depression initiate treatment. Electronic cognitive behavioural therapy (e-CBT) is an effective treatment for depression and is a feasible solution to make mental health care more accessible. Due to its online format, e-CBT can be combined with variable therapist engagement to address different care needs. Typically, a multi-professional care team determines which combination therapy is the most beneficial to the patient. However, this process can add to the costs of these programs. Artificial intelligence (AI) technology has been proposed to offset these costs. Therefore, this study aims to determine a cost-effective method to decrease depressive symptoms and increase treatment adherence to e-CBT. This will be done by comparing AI technology to a multi-professional care team when allocating the correct intensity of care for individuals diagnosed with depression. This study is a double-blinded randomized controlled trial recruiting individuals (n = 186) experiencing depression according to the Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5). The degree of care intensity a participant will receive will be randomly decided by either: (1) a machine learning algorithm (n = 93), or (2) an assessment made by a group of healthcare professionals (n = 93). Subsequently, participants will receive depression-specific e-CBT treatment through the secure online platform, OPTT. There will be three available intensities of therapist interaction: (1) e-CBT; (2) e-CBT with a 15-20-minute phone/video call; and (3) e-CBT with pharmacotherapy. This approach aims to accurately allocate care tailored to each patient's needs, allowing for more efficient use of resources.

Eligibility criteria:
Inclusion Criteria:

* Diagnosed with MDD by a trained research assistant according to the criteria outlined in the DSM-5
* Ability to provide informed consent
* Ability to speak and read English
* Having consistent and reliable access to the internet

Exclusion Criteria:

* Active psychosis
* Acute mania
* Severe alcohol, or substance use disorder
* Active suicidal or homicidal ideation
* Currently receiving psychotherapy

Outcomes:
Change in Patient Health Questionnaire (PHQ-9) Score; Change in Quick Inventory of Depressive Symptoms (QIDS) Score; Change in Assessment of Quality of Life (AQoL-8D) Score
Be Right! Back: An Artificial Intelligence Enabled Mobile Application for Patients With Low Back Pain
Trial ID: NCT06973915
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-06-01
Completion date: 2027-03-31
Sponsor: Singapore General Hospital
Collaborators: National Medical Research Council (NMRC), Singapore; KK Women's and Children's Hospital
Conditions: Low Back Pain
Interventions: AI model for movement and pain assessment in low back pain
Locations: Singapore General Hospital
Countries: Singapore
Enrollment: 120
Age range: 21 Years - 75 Years
Sex: ALL

Summary:
Low back pain (LBP) is a common problem with complex causes, of which some are modifiable. Physical factors like strength, movement, and pain play a big role, but measuring all these factors accurately is tricky. This is where Artificial Intelligence (AI) comes in.

This projects aims to develop an AI solution (in the form of a mobile application) that can measure four key components of the physical factor of LBP, such as how quickly you can stand up five times, your spine's flexibility, how you walk, and your pain levels while moving. The measurements taken by the mobile application will be compared against those of trained physiotherapists to ensure its accuracy.

If successful, this AI solution will be a game-changer. Physiotherapists will be able to remotely track the progress of their LBP patients. The data gained from the remote tracking will allow physiotherapists to have a better understanding of the individual profile of each LBP patient and adjust their treatment accordingly, hence allowing for better care and more effective LBP management.

In short, this project aims to harness the power of AI to make managing LBP easier for both patients and physiotherapists.

Eligibility criteria:
Inclusion Criteria:

1. Aged 21 to 75 years
2. Referred to physiotherapy for low back pain
3. All genders and races
4. Allow video recording of their facial and body movement
5. Good comprehension of English language
6. Ability to provide informed consent

Exclusion Criteria:

1. Psychiatric disorders (e.g. anxiety, depression)
2. Any cognitive impairment
3. Neurological disorders (e.g. CVA, Parkinson's Disease)
4. Musculoskeletal limitations that result in gait abnormalities/limitations
5. Previous thoracic and/or lumbar spine surgery with instrumentation
6. Inability to provide informed consent

Outcomes:
5 times sit-to-stand
LMU Project on the Impact of Reviewing AI Annotated SD-OCT Therapy Assistance Reports on Ophthalmologists' Treatment Decision-making for Anti-VEGF Therapy in NAMD Patients
Trial ID: NCT06817915
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-01-30
Completion date: 2026-01-30
Sponsor: Johannes Schiefelbein
Collaborators: Deepeye Medical GmbH; Technomics Research; M3 Macula Monitor Muenster
Conditions: Neovascular Age-Related Macular Degeneration (nAMD)
Interventions: AI assisted assessment of SD-OCT scans
Locations: LMU Klinikum
Countries: Germany
Enrollment: 100
Age range: 18 Years - None
Sex: ALL

Summary:
This is a research plan from the University of Munich (LMU) that aims to study how the use of AI reports can impact ophthalmologists' decisions regarding treatment for patients with neovascular age-related macular degeneration (nAMD). This disease is a leading cause of vision loss, and while anti-VEGF treatments are effective, they require careful monitoring and retreatment decisions to maximize benefits.

The study will involve up to 1000 ophthalmologists with varying levels of expertise. These ophthalmologists will review SD-OCT scans and make treatment decisions before and after reviewing AI-generated reports. The primary objective is to compare these decisions and see how the AI reports influence them. Secondary objectives include assessing the accuracy and safety of the AI reports.

Eligibility criteria:
Inclusion Criteria:

* Electronically consented to the informed consent form (eICF)
* Criteria to be included in one of the following six Ophthalmology user groups:

Group 1 Non-retina specialist Group: Ophthalmology, completed ophthalmology residence with no or another subspecialty other than retina (e.g., Glaucoma, refractive, etc) Group 2 Resident Group: \<5 years in residency in ophthalmology Group 3 Fellow Group: Retina specialist in training Group: in fellowship in vitreoretinal medicine, medical retina Group 4 Retina specialist Group: completed retina training, regular requalification Group 5 Junior reader Group: have already gained experience in the reporting clinical routine with the diagnostics in question and completed the initial certification process at an Image and Reading Center (acc. to centre's SOP) Group 6 Senior reader Group: specialist with several years of experience in the relevant field or have completed at least 3 years of residency training. Completed the certification process at the Image and Reading Center (acc. to centre's SOP)

Exclusion Criteria:

* Not an Ophthalmologist.
* Does not have time to participate in the estimated project duration of 30 minutes.

Outcomes:
Ophthalmologist's Treatment Decision