moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Data Clustering Study With Artificial Intelligence and Phenotyping of Patients Who Presented With Acute Pulmonary Embolism
Trial ID: NCT06183944
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-12-11
Completion date: 2026-07-01
Sponsor: Centre Hospitalier Intercommunal de Toulon La Seyne sur Mer
Conditions: Pulmonary Embolism
Interventions: Hierarchical clustering methods
Locations: centre hospitalier intercommunal Toulon La Seyne sur Mer - Internal and vascular medicine
Countries: France
Enrollment: 2500
Age range: 18 Years - None
Sex: ALL

Summary:
The aim will be to identify clinically relevant phenotypes in patients with acute pulmonary embolism. Hierarchical clustering methods combined with unsupervised learning (machine learning) will be used to obtain groups of patients who are homogeneous at diagnosis. Evaluating their prognosis at 6 months (recurrence or chronic thromboembolic pulmonary hypertension), account the first 3 months of anticoagulant treatment, would provide an aid to medical decision-making.

This research will include a retrospective and a prospective parts. The retrospective part will include patients who have been admitted to CHITS for acute pulmonary embolism since 2019. For the prospective part, it is planned to include patients with same characteristics over the years 2024 and 2025. More than 2,500 patients are expected to be included.

This research will have no impact on current patient care. Data from consultations and various examinations carried out as part of care will be collected for six months post-diagnosis in order to meet the research objectives.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18 years;
* Patient with acute pulmonary embolism in CHITS (hospitalised or not).

Exclusion Criteria:

* Sub-segmental pulmonary embolisms ;
* Patient opposition.

Outcomes:
Primary: Identify homogeneous groups of patients based on their medical characteristics at diagnosis, and then compare their evolution at 6 months.
Technical Development of Automated Low Dose Risk Assessment Mammography (ALDRAM) in Women Attending for Annual Mammography Through a Family History Clinic
Trial ID: NCT05796219
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2019-02-22
Completion date: 2023-02-16
Sponsor: Manchester University NHS Foundation Trust
Conditions: Breast Cancer
Interventions: Low dose mammogram
Locations: The Nightingale Centre
Countries: United Kingdom
Enrollment: 154
Age range: 30 Years - 45 Years
Sex: FEMALE

Summary:
Breast cancer (BC) is the commonest cause of death in young women. Breast screening in women aged 35-45, at increased risk due to their family history, has been shown to improve survival. However, 80% of women who develop BC do not have a family history. Numerous studies have shown that high mammographic density (MD) is one of the strongest risk factors for BC development. Full field digital mammography (FFDM) can be used to assess MD, however it is not recommended for population BC screening in those \<40 years of age due to the concerns about the use of ionising radiation. Safe and accurate high throughput methods to quantify MD in young women are thus required to improve risk prediction and reduce BC mortality. This study aims to develop a low dose mammogram, with quantification of density using artificial intelligence, to facilitate high throughput risk assessment in young women. 600 women aged 30-45, previously identified as being at increased risk of BC and attending for annual mammography at The Nightingale Centre will be recruited. Participants will undergo FFDM of the right breast as usual, however, following acquisition of the craniocaudal (CC) view, the breast will remain compressed and the mammogram dose reduced by 90% to deliver a LD mammogram. This process will be repeated for the right medio-lateral oblique (MLO) view. The left breast FFDM will proceed as normal. It is estimated that each extra exposure will take 1-2 minutes only. Deep machine learning methods will be used to define the relationship between standard FFDM views and their low dose counterparts and determine which view (CC vs MLO) provides the best correlation to be taken forward to the next stage of the research.

Eligibility criteria:
Inclusion Criteria:

* Females with moderate to high risk of breast cancer, identified through and attending for annual review and mammography at the family history clinic, Nightingale Centre, Manchester
* Current age 30-45 years
* Capable of providing informed consent to a patient information sheet written in English

Exclusion Criteria:

* Prior breast cancer
* Prior breast augmentation or reduction
* Participation in the TARA-Prev study which included an additional mammogram and thus radiation dose

Outcomes:
Correlation between full dose and low dose percent mammographic density estimates
Clinical Validation of an Artificial Intelligence Algorithm to Help Interpret Mammograms
Trial ID: NCT05640011
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-04-20
Completion date: 2023-05-20
Sponsor: University Hospital, Strasbourg, France
Conditions: Breast Cancer
Locations: Service de Radiologie 1 - CHU de Strasbourg - France
Countries: France
Enrollment: 1000
Age range: 40 Years - 75 Years
Sex: FEMALE

Summary:
This aims to clinically validate, on a large population, a tumor detection aid software which has already been trained on a representative French population (from several hospital centers and liberals from several departments in the west and east of France).

This population consists of 1000 patients who have been treated for breast cancer (histologically proven by breast biopsy) and whose investigators have mammograms performed at the time of diagnosis. The control population consists of the unaffected breast of each patient (with the exception of the rare cases of bilateral cancers).

This innovative software has the main feature of recognizing healthy breast tissue, allowing the radiologist to focus on breast tissue at risk, improving the management of medical time and the management of "difficult" files.

Eligibility criteria:
Inclusion Criteria:

* Adult woman (40 to 75 years old)
* Treatment at the University Hospitals of Strasbourg between 2010 and 2020 for breast cancer
* including mammography and histological evidence available
* Patient who has already given her consent for the reuse of her anonymous data for research purposes

Exclusion Criteria:

* Woman who expressed her opposition to participating in the study

Outcomes:
Evaluate the diagnostic performance (sensitivity, specificity) for operator tumor detection assisted by mammographic diagnostic aid software, and compare it to the diagnostic performance of an unassisted operator.
Real-Time Artificial Intelligence Aided Diagnosis of Colorectal Polyps During Colonoscopy: A Clinical Trial With the EndoBRAIN Technology
Trial ID: NCT04510545
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-06-01
Completion date: 2021-05-05
Sponsor: King's College Hospital NHS Trust
Conditions: Colonic Polyp; Colorectal Cancer
Interventions: Endobrain, Computer Aided Diagnosis (CAD)
Locations: King's College Hospital
Countries: United Kingdom
Enrollment: 89
Age range: 18 Years - 90 Years
Sex: ALL

Summary:
The purpose of this study is to assess whether computer aided technology (CAD) can help in the diagnosis of polyps found the bowel compared with visual inspection alone and therefore whether it is beneficial in helping clinicians to decide whether to remove a polyp or not. Presently, most endoscopists remove all polyps found and send them to the laboratory for testing. The number of colonoscopies is increasing, meaning that more polyps are detected and removed. This comes at a significant cost to the health service and increases the time taken to complete a colonoscopy.

Eligibility criteria:
Inclusion Criteria:

* Individuals 18 years or older who are scheduled for screening, surveillance, diagnostic, or therapeutic colonoscopy at at King's College Hospital with diminutive rectosigmoid polyps.

Exclusion Criteria:

* Diminutive polyps with known histology
* Inflammatory bowel disease
* Polyposis syndrome (e.g., familial adenomatous polyposis, serrated polyposis)
* History of chemotherapy or radiation therapy for colorectal lesions
* Inability to undergo polypectomy (e.g. intake of anticoagulants, comorbidities, or patient refusal)
* Pregnancy

Outcomes:
True positive adenoma detection rate
Development of Artificial Intelligence Model for Automized Dental Caries Detection Vs Expert Decision: A Diagnostic Accuracy Study
Trial ID: NCT06984029
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-06
Completion date: 2026-07
Sponsor: Noura Nageh Ramadan Ahmed
Conditions: Dental Caries
Locations: Pediatric Dentistry Department, Faculty of Dentistry, Cairo University
Countries: Egypt
Enrollment: 100
Age range: 6 Years - 9 Years
Sex: ALL

Summary:
This study aims to evaluate the effects of an artificial intelligence (AI)-based caries detection system on the diagnosis and categorization of dental caries in pediatric patients. The purpose of this research is to better understand how AI may help improve the accuracy and reliability of early dental caries detection compared to traditional clinical examination methods. Participants in this study will be pediatric patients aged 6-9 years, and they will undergo clinical evaluations for dental caries. The study will compare the AI system's performance to conventional clinical examination in terms of sensitivity, specificity, and overall diagnostic accuracy. The progress of participants will be monitored over a period of six months, with regular assessments of their caries detection results. The study will assess the effectiveness, reproducibility, and diagnostic accuracy of the AI model. Throughout the study, participants will be closely monitored by dental healthcare providers to ensure their safety and well-being. Participants and their guardians are encouraged to communicate any concerns or questions with the study team.

Eligibility criteria:
Inclusion Criteria:

* Pediatric patients aged 6-9 years
* Presence of at least one decayed tooth identified during initial screening
* Cooperative behavior, allowing for intraoral imaging and clinical examination

Exclusion Criteria:

* Patients with systemic diseases or conditions affecting oral health
* Uncooperative patients who cannot complete the examination
* Patients with severe dental anomalies or extensive restorations that interfere with caries detection

Outcomes:
Diagnostic accuracy of the AI model
Usefulness of GI-GENIUS in FIT-based Colorectal Cancer Screening Program. Randomized Controlled Trial.
Trial ID: NCT04673136
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-04-01
Completion date: 2022-03-31
Sponsor: Asociación Española de Gastroenterología
Collaborators: Medtronic
Conditions: Colorectal Neoplasms; Intestinal Neoplasms; Gastrointestinal Neoplasms; Colonic Polyp; Colorectal Cancer; Adenoma Colon; Serrated Adenoma; Serrated Polyp
Interventions: GI-GENIUS Medtronic; Colonoscopy
Locations: Hospital General Universitario de Alicante; Hospital Clinic Barcelona; Complexo Hospitalario de Ourense; Hospital Universitario Central de Asturias; Hospital Universitario Río Hortega; Hospital Universitario Álvaro Cunqueiro
Countries: Spain; Spain; Spain; Spain; Spain; Spain
Enrollment: 3400
Age range: 18 Years - None
Sex: ALL

Summary:
Deep learning technology has an increasing role in medical image applications and, recently, an artificial intelligence device has been developed and commercialized by Medtronic for identification of polyps during colonoscopy (GI-GENIUS). This kind of computer-aided detection (CADe) devices have demonstrated its ability for improving polyp detection rate (PDR) and the adenoma detection rate (ADR). However, this increase in PDR and ADR is mainly made at the expense of small polyps and non advanced adenomas.

Colonoscopies after a positive fecal immunochemical test (FIT) could be the scenario with a higher prevalence of advanced lesions which could be the ideal situation for demonstrating if these CADe systems are able also to increase the detection of advanced lesions and which kind of advanced lesions are these systems able to detect.

The CADILLAC study will randomize individuals within the population-based Spanish colorectal cancer screening program to receive a colonoscopy where the endoscopist is assisted by the GI-GENIUS device or to receive a standard colonoscopy.

If our results are positive, that could suppose a big step forward for CADe devices, in terms of definitive demonstration of being of help for efectively identify also advanced lesions.

Eligibility criteria:
Inclusion Criteria:

* Individuals with a positive result in fecal immunochemical test within the population-based colorectal cancer screening program.
* Complete colonoscopy with cecal intubation.
* Inform consent signed.

Exclusion Criteria:

* Personal history of colorectal cancer.
* Family history of colorectal cancer: ≥2 FDR or ≥1 FDR diagnosed before 50 years of age.
* Family history of hereditary colorectal cancer syndromes: Lynch syndrome, FAP, etc.
* Personal history of inflammatory bowel disease.
* Terminal illness.
* Personal history of total proctocolectomy.

Outcomes:
Detection of advanced lesions
Dialysis Access Monitoring Using a Digital Stethoscope-Based Deep Learning System
Trial ID: NCT05105503
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-05-23
Completion date: 2024-12-31
Sponsor: Eko Devices, Inc.
Collaborators: University of North Carolina, Chapel Hill
Conditions: Dialysis Access Malfunction; Dialysis; Complications
Interventions: Eko CORE Digital Stethoscope
Locations: Carolina Dialysis - Carrboro
Countries: United States
Enrollment: 22
Age range: 18 Years - None
Sex: ALL

Summary:
This study will enroll end-stage renal disease (ESRD) patients on hemodialysis with a maturing arteriovenous fistula (AVF) for hemodialysis access. A study staff member will mark with indelible ink on each participant's skin the three sites on the upper extremity where the Eko CORE digital stethoscope will be used to take sound recordings. 9 recordings will be taken (3 at each site) once per week during weekly dialysis treatments.

Eligibility criteria:
Inclusion Criteria:

* Adults aged 18 years or older
* Potential subject is able and willing to give informed consent
* End-stage renal disease, stage V, on active HD treatment
* Participant has a maturing AVF that has not yet been cannulated
* Participant is willing to have repeat Eko recordings taken weekly from the time of enrollment until the access is declared "mature and suitable for HD access" or "failed and unsuitable for HD access"

Exclusion Criteria:

* Younger than 18 years
* Pregnant
* Incarcerated
* Unable and/or unwilling to give informed consent
* Presence of an active infection or open wounds along potential access and data collection site
* Unable to complete study activities according to the above schedule

Outcomes:
Development of deep learning-scale database of maturing AVF sounds.
Prediction of Microvascular Invasion in HCC Using Spatiotemporal Radiomics of Contrast-enhanced Ultrasound: a Deep Learning Model With Transcriptomics Correlation
Trial ID: NCT06760494
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-11-01
Completion date: 2024-05-30
Sponsor: Chinese PLA General Hospital
Collaborators: Chinese Academy of Sciences
Conditions: HCC - Hepatocellular Carcinoma; Microvascular Invasion (MVI)
Interventions: the MAPUSE model
Locations: the First Affiliated Hospital of Sun Yat-sen University; Chinese PLA General Hospital
Countries: China; China
Enrollment: 250
Age range: 18 Years - None
Sex: ALL

Summary:
An artificial intelligence (AI) model to predict MVI of HCC using contrast-enhanced ultrasound was constructed. This model also has biological explainability. The investigators named it as MAPUSE (MVI AI prediction via contrast-enhanced ultrasound with explainability).

The goal of MAPUSE study is to prospectively test the performance of MAPUSE model on MVI prediction and its biological correlation in different geographical areas of China.

Eligibility criteria:
Inclusion Criteria:

1. Age \>18 years old.
2. The HCC diagnosis and the presence of MVI were confirmed by surgical pathology.
3. Complete and clear CEUS videos obtained within two weeks preoperatively.

Exclusion Criteria:

1. Unqualified CEUS images.
2. Missing surgical pathological diagnosis.
3. Lesions underwent local treatments.
4. Non-HCC diagnosis

Outcomes:
area under operating characteristic curves (AUC)
Near Focus NBI-Driven Artificial Intelligence for the Diagnosis of Gastro-Oesophageal Reflux Disease
Trial ID: NCT04268719
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2017-11-23
Completion date: 2018-11-30
Sponsor: King's College Hospital NHS Trust
Conditions: Gastro Esophageal Reflux
Interventions: wireless pH capsule recording
Locations: Shraddha Gulati
Countries: United Kingdom
Enrollment: 76
Age range: 18 Years - 90 Years
Sex: ALL

Summary:
Gastro-oesophageal reflux disease (GORD) is a chronic condition with symptoms arising secondary to the reflux of stomach contents (Vakil et al., 2006). It is divided into four phenotypes: Erosive Oesophagitis (EO), Non-Erosive Reflux Disease (NERD), Reflux Hypersensitivity (RH), Functional Heartburn (FH) (Nikaki, Woodland and Sifrim, 2016). The definition of these phenotype have evolved with the addition of diagnostic tests and methods of their interpretation, the most recent being the Lyon Consensus Statement (Gyawali et al., 2018). The majority of patients presenting with symptoms suggestive of GORD have no mucosal lesion seen at endoscopy (Nikaki, Woodland and Sifrim, 2016). Studies have shown a relation of increased IPCL numbers with GORD. This study aims to build a fully autmoated AI model using Near-Focus NBI images on patients with symptoms suggestive of GORD phenotyped in accordance with the Lyon Consensus.

Eligibility criteria:
Inclusion Criteria:

* Retaining capacity and medically fit for gastroscopy
* Requiring gastroscopy by current BSG guidelines for the investigation of acid reflux/ dyspepsia

Exclusion Criteria:

* Unable to give informed consent
* History of oesophageal or gastric surgery
* Allergy to proton-pump inhibitor
* Known Barrett's Oesophagus/ oesophageal carcinoma/ oesophageal stricture/ known oesophageal dysmotility
* Portal Hypertension
* Pacemaker (BRAVO)

Outcomes:
To evaluate Intra-Papillary Capillary Loop (IPCL) changes secondary to oesophageal acid exposure.; To develop an accurate and reliable artificial intelligence model for the diagnosis of Gastro-Oesophageal Reflux Disease (GORD)
Artificial Intelligence in the Diagnosis of Orthopaedic Conditions, Particularly Bone Tumours and Infection
Trial ID: NCT04746001
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-06-28
Completion date: 2024-07-01
Sponsor: Robert Jones and Agnes Hunt Orthopaedic and District NHS Trust
Collaborators: Keele University
Conditions: Bone Tumours; Bone Infection; Prosthetic Joint Infection; Prosthetic Infection
Interventions: Genomic sequencing; Histological examination
Locations: Robert Jones and Agnes Hunt Orthopaedic Hospital NHS Trust
Countries: United Kingdom
Enrollment: 100
Age range: 18 Years - None
Sex: ALL

Summary:
Translational research aimed at improving diagnostic accuracy of musculoskeletal infection and musculoskeletal tumours using machine learning applied to clinical data, histopathological sections and genomic sequencing.

Eligibility criteria:
Inclusion Criteria:

Suspected infection of bone tumour and biopsy taken for analysis

Exclusion Criteria:

Patient doesn't consent

Outcomes:
Sequencing data and identification; Histological slides and identification
Show Me How You Move and I Will Tell You Who You Are. Body Movement Analysis and Automatic Recognition of Associated Vestibular Impairment Patterns
Trial ID: NCT06739577
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-08-10
Completion date: 2023-07-01
Sponsor: Université Catholique de Louvain
Conditions: Vestibular Disease; Vestibular Function Disorder
Interventions: Evaluation of walking abilities (manual Timed-Up & Go - TUG and automatic -OpenTUG)); Neuropsychological assesment is provide to measure objective and subjective cognition/emotional aspects.
Locations: Cliniques universitaires Saint-Luc
Countries: Belgium
Enrollment: 100
Age range: 18 Years - 90 Years
Sex: ALL

Summary:
The aim of this experiment is to gain a better understanding of the body movement patterns of patients with balance disorders (following damage to the vestibular system), as well as their cognitive performance profile. To achieve this, we will need to assess the performance of both patients and healthy subjects with no balance disorders. We will be using a newly-developed artificial intelligence tool to analyze body movement patterns on the basis of participants' videos, and we will also be evaluating participants' performance in a few simple tests of reaction times to image presentations on computer and/or tablet/smartphone.

If you agree to take part in this experiment, you will be asked to perform a simple walking exercise in a corridor (filmed and supervised by a physiotherapist) as well as a few objective cognitive measurement tests (reaction time to attentional and vigilance tasks on computer and/or tablet/smartphone) and subjective tests (self-reported questionnaires) requiring around 40 minutes of investigation in total.

In the long term, the results of this research will make it easier to assess balance disorders in patients with vestibular pathology, and better quantify any cognitive difficulties they may have.

Eligibility criteria:
For patients :

Inclusion Criteria:

* Age between 18 and 90 years old.
* Vestibular loss diagnosed by senior ENT doctors with the gold standard ENT measures (caloric testing, head impulse test and/or vestibular evoked myogenic potential, ocular and cervical).

Exclusion Criteria:

* Previous neurological disorders (stroke, dementia, …)
* Current medication interfering with the testing (reducing reaction time).
* Visual difficulties interfering with the testing (Visual field reduction,…)
* Age below 18 years old.

For healthy controls :

Inclusion criteria :

\- Age between 18 and 90 years old.

Exclusion criteria:

* Previous neurological disorders (stroke, dementia, …)
* Current medication interfering with the testing (reducing reaction time)
* Visual difficulties interfering with the testing (Visual field reduction,…)
* Vertigo or balance complains or an history of previous vestibular disorder.
* Age below 18 years old.
* Not being able to understand the consigns of the task.

Outcomes:
Neuropsychological measures; Neuropsychological measures; Neuropsychological measures; Neuropsychological measures; Walking activity