moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Efficacy of CPAP in Reducing Cognitive Fog in Post-COVID Patients With Sleep Apnea
Trial ID: NCT06052514
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-01-04
Completion date: 2024-12-31
Sponsor: Venkatesh Krishnamurthy
Collaborators: Breathe Pennsylvania
Conditions: COVID-19; Sleep Apnea
Interventions: Continuous positive airway pressure
Locations: University of Pittsburgh
Countries: United States
Enrollment: 22
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
This is a prospective, observational study evaluating the relationship between severity of sleep apnea with severity of cognitive fog and if treatment of sleep apnea with CPAP improves cognitive fog in a cohort of post COVID patients with sleep apnea.

Eligibility criteria:
Inclusion Criteria:

* Patients more than 18 years of age.
* Diagnosed with COVID and continues to have persistent symptoms beyond 4 weeks.
* Diagnosed with sleep apnea based on a home sleep study

Exclusion Criteria:

* Patients with Narcolepsy or other acute Primary sleep problems.
* Cognitive impairment secondary to a neurodegenerative disorders and dementia.
* Acute exacerbation of psychiatric illness including severe depression, schizophrenia or bipolar disorder in the past 30 days.
* Serious medical or neurological illness or infection requiring hospitalization or unable to function in the past 30 days.
* Any medications that were started or change in dosage of the following medication in the past 30 days including: corticosteroids, stimulants, benzodiazepines/non benzodiazepine receptor agonists, antidepressants, antipsychotics or other medications which alter cognition.
* Patients who are on any active treatment for sleep apnea including CPAP, dental device or implant for two weeks before enrolling into the study.

Outcomes:
Change from Baseline in Montreal Cognitive Assessment at 4-6 Weeks
Development and Validation of a CT-based Diagnostic Models Using Artificial Intelligence for Detection of Small Bowel Obstruction
Trial ID: NCT05566158
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-08-09
Completion date: 2023-12-31
Sponsor: Fondation Hôpital Saint-Joseph
Conditions: Small Bowel Obstruction
Locations: Central for Visual Computing - OPIS Inria group; Groupe Hospitalier Paris Saint-Joseph
Countries: France; France
Enrollment: 8000
Age range: 18 Years - None
Sex: ALL

Summary:
Small bowel obstruction (SBO) is a common non-traumatic surgical emergency. All guidelines recommend computed tomography (CT) as the first-line imaging test for patients with suspected SBO. The objectives of CT are multiple: (i) to confirm or refute the diagnosis of GI obstruction, defined as distension of the digestive tracts greater than 25 mm, and, when SBO is present, (ii) to confirm the mechanism (mechanical vs. functional), (iii) to localize the site of obstruction, i.e., the transition zone (TZ), (iv) to identify the cause, and (v) to look for complications such as strangulation or perforation, influencing management.

Given the exponential increase in the number of scans being performed, especially in the setting of emergency management, methods to assist the radiologist would be useful to:

1. Sort the scans performed, allowing prioritization of the analysis of scans with a higher probability of pathology (occlusion in our case)
2. Help the radiologist to diagnose occlusion and its type (functional or mechanical), and to identify signs of severity.
3. To help the emergency physician and the digestive surgeon to make a decision on the management of the disease (surgical or medical).

Machine learning has developed rapidly over the last decades, first thanks to the increase in data storage capacities, then thanks to the arrival of parallel processing hardware based on graphic processing units, in the context of radiological diagnostic assistance. Consequently, the number of studies on deep neural networks in medical imaging is increasing rapidly. However, few teams focus on SBO. The only published classification models have been produced for standard abdominal radiographs. No studies have used CT or 3D models, apart from our preliminary study on ZTs, despite the recognized advantages of CT for the diagnosis of SBO and the likely contribution of 3D models, which may be comparable to that of multiplanar reconstruction for the analysis of images in multiple planes of space.

Eligibility criteria:
Inclusion Criteria:

* Patient whose age ≥ 18 years
* Patient who has had a CT scan with at least one abdominal-pelvic acquisition performed within the Saint Joseph Hospital Group
* Report containing the terms "occlusion" or "occlusive", "vomiting" or "ileus"
* French-speaking patient

Exclusion Criteria:

* Imaging not usable
* Absence of abdomino-pelvic volume on CT acquisitions
* Patient under guardianship or curatorship
* Patient deprived of liberty
* Patient under court protection
* Patient objecting to the use of his data for this research

Outcomes:
Automated detection of digestive occlusions
Evaluation of Artificial Intelligence Models for Diagnosis of Anterior Open Bite Malocclusion
Trial ID: NCT06140849
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-11
Completion date: 2024-09
Sponsor: Cairo University
Collaborators: Future University in Egypt
Conditions: Artificial Intelligence; Open Bite
Interventions: artificial intelligence model
Locations: Orthodontic Department, Faculty of Dentistry, Cairo University
Countries: Egypt
Enrollment: 200
Age range: 7 Years - 60 Years
Sex: ALL

Summary:
Building artificial intelligence models for diagnosis of anterior open bite malocclusion

Eligibility criteria:
Inclusion Criteria:

* anterior open bite

Exclusion Criteria:

* craniofacial syndromes, previous orthodontic treatment, poor quality records

Outcomes:
the efficiency of artificial intelligence (AI) model in diagnosis of anterior open bite
Questionnaire Study Concerning Artificial Intelligence and Its Application in (Gastrointestinal) Healthcare - Patients' and Physicians' Perspectives
Trial ID: NCT05214625
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-04-08
Completion date: 2021-08-12
Sponsor: Maastricht University Medical Center
Collaborators: Catharina Ziekenhuis Eindhoven
Conditions: Gastro-Intestinal Disorders
Interventions: Questionnaire
Locations: Maastricht University Medical Center; Catharina Ziekenhuis Eindhoven
Countries: Netherlands; Netherlands
Enrollment: 457
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to investigate the knowledge, experience, and opinion on AI among gastroenterology (GI) patients, gastroenterologists, and GI-fellows, particularly concerning implementation and application of AI (in assisting clinicians) in healthcare. The secondary aims are to investigate (dis)advantages of AI use in healthcare and the availability of technical facilities and infrastructures within endoscopy to implement and apply AI in Dutch hospitals.

Eligibility criteria:
Inclusion Criteria:

* Gastrointestinal (GI) patients with an outpatient appointment in the gastroenterology department for an endoscopic procedure in the context of the Dutch national screening program, because of gastrointestinal symptoms, or because of follow-up of previously diagnosed bowel diseases (in one of the two participating hospitals);
* Gastroenterologists working in Dutch hospitals;
* Gastroenterology fellows working in Dutch hospitals;
* Minimal age of 18 years old.

Exclusion Criteria:

* No appropriate understanding of the Dutch language;
* Not willing to sign informed consent;
* Objection to participate in medical scientific research, reported in the medical file.

Outcomes:
The knowledge on AI investigated by means of a questionnaire; The experience with AI investigated by means of a questionnaire; The opinion on AI investigated by means of a questionnaire
Localizing Functional Brain Cortices and Epileptogenic Zones With High Frequency Brain Signals
Trial ID: NCT00600717
Status: WITHDRAWN
Study type: OBSERVATIONAL
Start date: 2000-11-01
Completion date: 2027-08-01
Sponsor: Children's Hospital Medical Center, Cincinnati
Collaborators: University of Cincinnati
Conditions: Epilepsy; Migraine; Headache; Pain; Autism; Cerebral Palsy; Brain Development; Traumatic Brain Injury; ADHD - Attention Deficit Disorder With Hyperactivity; Mental Health
Locations: MEG
Countries: United States
Enrollment: 0
Age range: 1 Day - 69 Years
Sex: ALL

Summary:
The objective of this study is to utilize high-frequency brain signals (HFBS) to localize functional brain areas and characterize HFBS in epilepsy, migraine, and other brain disorders. Our goal is to create the world's first high-frequency MEG/EEG/ECoG/SEEG database for the developing brain. HFBS include high-gamma activation/oscillations, high-frequency oscillations (HFOs), ripples, fast ripples, spikelets, fast spikelets, and very high-frequency oscillations (VHFOs). While terminologies and frequency bands may vary among reports, both HFOs and high-gamma waves are crucial for understanding brain function and developing potential treatments for neurological disorders.

We have been developing an intelligent software platform to analyze signals from low to very high-frequency ranges across multiple frequency bands. To achieve these goals, we have developed several innovative techniques and software packages:

* Accumulated spectrogram
* Accumulated source imaging
* Frequency-encoded source imaging
* Multi-frequency analysis at source levels
* Artificial intelligence detection of HFOs
* Neural network analysis (Graph Theory)
* Other techniques (e.g., Independent Component Analysis, virtual sensors) These methods enable researchers to better understand the characteristics and significance of HFOs and high-gamma brain waves, contributing to advancements in the diagnosis and treatment of neurological disorders.

Eligibility criteria:
Inclusion Criteria:

* Healthy and cooperative.
* Ages: from 1 day to 69 years (male or female).
* Normal hearing and vision.
* Normal hand movement.
* No history of neurological or psychiatric diseases.
* No family history of genetic neurological or psychiatric diseases.
* No metal implants such as pacemakers, neuro-stimulators, cochlear implants, etc.

Exclusion Criteria:

* Taking any medications for depression, neurologic, or psychiatric conditions.
* Not feeling well, having epilepsy, or other brain disorders.
* Recent concussion or head injury.
* Presence of metal in the body, such as dental braces, which could cause "magnetic noise". A simple, quick "magnetic noise screening" can be conducted at the MEG Center to determine eligibility.
* Presence of electrical or metal implants such as pacemakers, neuro-stimulators, or orthopedic pins or plates. The research nurse will discuss all exclusions in further detail before the magnetic resonance imaging (MRI) scan.
* Inability to pass the pre-experimental screening.

Outcomes:
epileptic foci
Clinical Validation of Radiomics Artificial Intelligence: Application to Breast Cancer
Trial ID: NCT05070884
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-06-01
Completion date: 2025-12
Sponsor: Instituto de Investigacion Sanitaria La Fe
Collaborators: Karolinska Institutet; Medical University of Gdansk; Medical School University of Zagreb; Medical University of Vienna; Hacettepe University; Alexander Fleming Institute; Ain Shams University
Conditions: Breast Cancer
Interventions: The development of new clinical AI solutions to predict treatment response to neoadjuvant chemotherapy (NAC) in breast cancer
Locations: Alexander Fleming; Medical University of Vienna; University of Zagreb School of Medicine; Ain Shams University; Medical University of Gdansk; Hospital Universitario y Politécnico La Fe de Valencia; Karolinska Institute; Hacettepe University
Countries: Argentina; Austria; Croatia; Egypt; Poland; Spain; Sweden; Turkey (Türkiye)
Enrollment: 5000
Age range: 18 Years - 85 Years
Sex: FEMALE

Summary:
RadioVal will develop and implement interoperable solutions for clinical deployment of the radiomics tools, including information, training, and communication packages for clinicians and patients, as well as standard operating procedures for the integration of radiomics in clinical oncology. With this study, we will clinically validate these solutions, by looking at their reliability for precise breast cancer diagnosis, treatment recommendation and prognosis estimate, treatment response, evaluation of residual disease and outcome prediction.

Eligibility criteria:
Inclusion Criteria:

* Females ≥ 18 years up to 85 years old
* Individuals referred to hospitals for diagnosis of breast cancer
* Availability of radiological images: 2D mammography or 2D synthetic digital tomosynthesis, ultrasound, or magnetic resonance
* Availability of pathological report (surgical specimen)
* Availability of (Neoadjuvant) treatment allocation (scheme, duration, benefit)
* Availability of treatment response

Exclusion Criteria:

* Patient with incomplete or low-quality data (radiological, pathological or clinical)

Outcomes:
Percentage of patients non-respondents vs respondents in neoadjuvant breast cancer treatment (Estimate tumor aggressiveness)
Künstliche Intelligenz (KI) in Der Funktionellen Bildgebung Zur Individualisierten Behandlung Von Kopf-Hals-Plattenepithelkarzinom-Patienten Artificial Intelligence in Functional Imaging for Individualized Treatment of Head and Neck Squamous Cell Carcinoma Patients
Trial ID: NCT05192655
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2021-12-01
Completion date: 2026-12-01
Sponsor: Charite University, Berlin, Germany
Collaborators: Helmholtz-Zentrum Dresden-Rossendorf; Max-Delbrück-Centrum Berlin
Conditions: HNSCC
Interventions: Observation
Locations: Department of Radiooncology and Radiotherapy, Charité - Universitätsmedizin Berlin, Campus Virchow Klinikum
Countries: Germany
Enrollment: 250
Age range: 18 Years - None
Sex: ALL

Summary:
The two curative treatment modalities for patients with HNSCC - primary chemoradiation (CRT) or primary surgery (often combined with postoperative (C)RT) - are both associated with serious side effects for which reason further stratification, optimization and personalization of treatment is urgently needed. As novel quantitative image analyses are a promising tool for further risk stratification, the investigators training a three-dimensional Convolutional Neural Network on 18F-Fluorodesoxyglucose (FDG) positron emission tomography (PET) imaging and clinical / histopathological data of a multicentric, retrospective cohort of 1200 patients treated with primary CRT and 800 patients treated with primary surgery at Charité and cooperation institutes in order to predict individual treatment-specific outcomes and identify patients with excellent outcome after primary CRT or primary surgery or unfavorable outcome for both. The trained algorithm of the artificial intelligence will be validated in a prospective trial to see if predicted loco-regional control and recommended treatment strategies are reliable. In total 250 curative HNSCC patients, treated with CRT or primary surgery, will be enrolled on this prospective validation trial with observational character, while biomarker, clinical and FDG-PET data are collected from these patients and follow-up visits will be concluded.

Eligibility criteria:
Inclusion Criteria:

* All patients older than 18 years with diagnosed HNSCC, attending the Department of Radiooncology and Radiotherapy or the Department of Oral and Maxillofacial Surgery at Charité for a treatment with curative intension ( R(C)T or primary surgery / combined).
* All patients received as pretherapeutic diagnostic method a 18F-Fluorodesoxyglucose (FDG) positron emission tomography (PET) imaging

Exclusion Criteria:

* Patients with HNSCC who cannot be treated in a curative approach
* Patients with HNSCC treated in a curative approach but unable to receive FDG PET imaging before start of treatment
* Data from patients who have withdrawn their study consent

Outcomes:
Locoregional Control
Multidisciplinary Expert System for the Assessment & Management of Complex Brain Disorders
Trial ID: NCT05171725
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-04-01
Completion date: 2024-09-30
Sponsor: Neurological Institute of Athens
Collaborators: Rabin Medical Center; Fundació Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau; King's College London; Uppsala University; Holistic IKE; National Technical University of Athens; Evolution Projects; Engineering Ingegneria Informatica S.p.A.; SOFTWARE IMAGINATION & VISION SRL; Vrije Universiteit Brussel; STICHTING LIBER; University of Edinburgh; CyberEthics Lab. srls
Conditions: Dementia; Alzheimer Disease; Epilepsy; Sleep Disorder; Circadian Rhythm Disorders; Healthy
Interventions: EEG,PSG; biosample collection (blood and CSF); neuropsychological testing; actigraphy; clinician assessment
Locations: Neurological Institute of Athens; Rabin Medical Center; Sant Pau Memory Clinic; King's College London
Countries: Greece; Israel; Spain; United Kingdom
Enrollment: 635
Age range: None - None
Sex: ALL

Summary:
The Multidisciplinary Expert System for the Assessment \& Management of Complex Brain Disorders (MES-CoBraD) is an interdisciplinary project combining Real-World Data (RWD) from multiple clinical and consumer sources through comprehensive, cost-efficient, and fast protocols towards improving diagnostic accuracy and therapeutic outcomes in people with Complex Brain Disorders (CoBraD), as reflected in Neurocognitive (Dementia), Sleep, and Seizure (Epilepsy) disorders and their interdependence. It brings together internationally recognized experts in medicine, engineering, computer science, social health science, law, and marketing and communication from across Europe, and combines clinical information and scientific research in CoBraD with technical innovation in secure data-sharing platforms, artificial intelligence algorithms, and expert systems of precision and personalized care, with a primary focus on improving the quality of life of patients, their caregivers, and the society at large. It leverages RWD from diverse CoBraD populations across cultural, socioeconomic, educational, and health system backgrounds, with special attention on including vulnerable populations and minorities in an equitable manner and engaging key stakeholders to maximize project impact.

Eligibility criteria:
Inclusion Criteria:

* All participants willing to undergo comprehensive assessment who visit one of three clinics (Epilepsy, Neurocognitive disorders, and Sleep Disorders), and healthy volunteers

Exclusion Criteria:

* participant disinterest

Outcomes:
Expert System Accuracy for the Assessment of Complex Brain Disorders; Expert System Accuracy for the Management of Complex Brain Disorders
Engaging Patients in Prenatal Genetic Testing Decisions as a Pathway to Improve Obstetric Outcomes
Trial ID: NCT06382636
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-05-06
Completion date: 2027-02-28
Sponsor: The Cleveland Clinic
Conditions: Prenatal Disorder
Interventions: Standard Care; Obstetric Prenatal Genetic Testing Engagement Solution (OPUS) AI Chatbot
Locations: Neighborhood Family Practice; MetroHealth Medical Center; Cleveland Clinic
Countries: United States; United States; United States
Enrollment: 600
Age range: 18 Years - 50 Years
Sex: FEMALE

Summary:
The goal of this study is to ensure that pregnant patients have the resources and support needed to access Prenatal Screening \& Diagnostic Testing (PS\&D) in an informed and evidence-based fashion by developing an innovative digital tool to support patients' decision-making and contributing fundamental knowledge to advance science in a way that promotes patients' access to new prenatal applications of genomic science and technology. Our central hypothesis is that, by focusing on patient engagement as a key driver to improve patient outcomes, the use of an evidence-based artificial-intelligence (AI) powered patient engagement tool will increase patients' ability to seek information and structure a decision-making process that, in turn, increases informed decisions about PS\&D and decreases decisional conflict associated with those decisions.

Using data from NEST (Ensuring Patients Informed Access to NIPT \[non-invasive prenatal testing\]), the investigators designed the next iteration of NEST, a point-of care shared decision-making tool powered by artificial intelligence (AI) to provide a personalized and dynamic decision support tool: Obstetric Prenatal Genetic Testing Engagement Solution (OPUS). OPUS is an AI-enabled healthcare chatbot (a computer program capable of processing and simulating human conversation) that provides patients with personalized information and decision-making support at different stages of the PS\&D pathway. It functions using a series of questions contained in the NEST with a branching logic sequence of questions and answers based on the responses to and from the patient, using a conversational and adaptable interaction. It also contains nested tiers of information, ranging from introductory to detailed information about patient engagement, health literacy, the different PS\&D options, and resources to learn about insurance coverage for PS\&D. OPUS was designed to be accessed by patients with different technological resources and preferences, using a cell phone, a mobile device, or a computer.

Eligibility criteria:
Inclusion Criteria:

* Able to read and speak English
* Able to provide consent to participate in the study
* Offered routine aneuploidy screening and diagnostic testing

Exclusion Criteria:

* Less than 18 years of age
* Not currently pregnant or an intrauterine pregnancy has not yet been established
* Unable to provide informed consent for research participation
* Unable to read and speak English

Outcomes:
Patient activation
LIFECHAMPS: A Collective Intelligence Platform to Support Cancer Champions - Stockholm Pilot Use Case 2
Trial ID: NCT05990634
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-03-01
Completion date: 2023-11
Sponsor: Region Stockholm
Collaborators: Aristotle University Of Thessaloniki; Hospital Universitario La Fe; University of Glasgow
Conditions: Melanoma (Skin)
Interventions: LifeChamps Platform
Locations: Academic Primary Health Care Centre
Countries: Sweden
Enrollment: 20
Age range: 65 Years - None
Sex: ALL

Summary:
The purpose of this study is to collect data from various sources (PROM / PREM, sensors, journal data) to train AI based models in the LifeChamps digital platform in a pre-pilot, as well as partly implement a pilot/feasibility study to examine the applicability of the digital technology developed in LifeChamps, as well as the usability for patients (cancer survivors) and health care professionals

Eligibility criteria:
Inclusion Criteria:

* Diagnosed with malignant melanoma (stage I-III) and undergone primary treatment within the last 36 months.
* Has completed primary and secondary cancer treatment and is now in remission (survivorship stage).
* Assessed as physically and mentally able to participate in the study
* Can read, write and understand the Swedish language
* Can bring and use their own smart phone (Android version 10 or newer) below the study
* Have 24-hour access to internet (broadband) via WiFi router in your own household and/or free 4G mobile data (sim card and 4G router will be provided if this criterion is not met)

Exclusion Criteria:

* Terminal cancer stage or prognosis \<18 months from time of recruitment
* Diagnosed with severe mental illness or cognitive illness that affects the ability to participate
* Screening with Mini-Cog where the result is between 0-2
* Inability or unwillingness to provide written informed consent

Outcomes:
Analytical models; Usability assessment