moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
A Study to Evaluate the Ability of Speech- and Language-based Digital Biomarkers to Detect and Characterise Prodromal and Preclinical Alzheimer's Disease in a Clinical Setting - PAST Extension Study.
Trial ID: NCT04851496
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-11-19
Completion date: 2022-08-30
Sponsor: Novoic Limited
Conditions: Alzheimer Disease; Preclinical Alzheimer's Disease; Prodromal Alzheimer's Disease; Alzheimer's Disease (Incl Subtypes); Mild Cognitive Impairment
Locations: Re:Cognition Health; Re:Cognition Health; Re:Cognition Health; Re:Cognition Health
Countries: United Kingdom; United Kingdom; United Kingdom; United Kingdom
Enrollment: 80
Age range: 50 Years - 85 Years
Sex: ALL

Summary:
The primary objective of the study is to evaluate whether a set of algorithms analysing acoustic and linguistic patterns of speech can detect amyloid-specific cognitive impairment in early stage Alzheimer's disease, based on archival spoken or written language samples, as measured by the AUC of the receiver operating characteristic curve of the binary classifier distinguishing between amyloid positive and amyloid negative arms. Secondary objectives include (1) evaluating how many years before diagnosis of MCI such algorithms work, as measured on binary classifier performance of the classifiers trained to classify MCI vs cognitively normal (CN) arms using archival material from the following time bins before MCI diagnosis: 0-5 years, 5-10 years, 10-15 years, 15-20 years, 20-25 years; (2) evaluating at what age such algorithms can detect later amyloid positivity, as measured on binary classifier performance of the classifiers trained to classify amyloid positive vs amyloid negative arms using archival material from the following age bins: younger than 50, 50-55, 55-60, 65-70, 70-75 years old.

Eligibility criteria:
Inclusion Criteria:

* Subjects are fully eligible for and have completed the AMYPRED (Amyloid Prediction in early stage Alzheimer's disease from acoustic and linguistic patterns of speech) study.

(See https://clinicaltrials.gov/ct2/show/NCT04828122)

* Subject has access to audio or written recordings created by them that are available for collection.
* Subject consents to take part in PAST extension study.

Exclusion Criteria:

* Subject hasn't completed the full visit day in the AMYPRED study.

Outcomes:
The primary outcome measure is the area under the curve (AUC) of the receiver operating characteristic (ROC) curve of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) arms.
Glioma Adaptive Radiotherapy With Development of an Artificial Intelligence Workflow
Trial ID: NCT06492486
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: PHASE2
Start date: 2025-11-30
Completion date: 2028-07-30
Sponsor: Tata Memorial Centre
Conditions: Diffuse Glioma; Glioblastoma; Adaptive Radiotherapy; Artificial Intelligence
Interventions: Adaptive radiotherapy
Enrollment: 60
Age range: 18 Years - 70 Years
Sex: ALL

Summary:
Gliomas are common primary brain tumors in adults. Gliomas can be classified into different types based on tumor grade, histopathological features, and molecular characteristics. The common types of diffuse gliomas include glioblastoma, astrocytoma, and oligodendroglioma. The standard treatment for diffuse gliomas includes surgery followed by radiation and chemotherapy. As per standard institutional practice, a uniform dose of radiation is delivered to the disease area and MRI is done before and after the treatment. In this study, MRI and PET scan will be done before starting the treatment and standard dose of radiation will be delivered. The interval imaging will be done twice during the course of treatment with MRI and PET, followed by dose modifications. The CT, MRI, and PET will be combined. Based on PET imaging, specific dose will be altered and delivered to specific areas. Dose modification will be done with the help of artificial intelligence. Participant's assessment will be done at regular intervals.

Modifications in radiation plans are done based on the changes in disease seen in scans is likely to improve the accuracy of RT treatments. Dose modifications based on imaging to resistant areas will help achieve better tumor control, reduce treatment-related toxicities, precise delivery of the RT and adjusting doses to the organs at risk (OAR) and changes in disease leading to better treatment compliance. Creating an artificial intelligence framework in radiation oncology promises to improve quality of workflow, treatment planning and RT delivery.

The aim of the study is to develop an artificial intelligence workflow for treatment of glioma with adaptive radiotherapy. This study will be conducted in Tata Memorial Centre on a population of 60 patients for a duration of 2 years. The total study duration is 4 years.

Eligibility criteria:
Inclusion Criteria:

* Histological diagnosis of diffuse glioma. Patients with IDH-negative GBM (stratum A) and IDH-mutant glioma (astrocytoma or oligodendroglioma) need radiotherapy (stratum B).

Age: 18-70 years. Karnofsky Performance Scale (KPS) ≥60

Exclusion Criteria:

* Multifocal or multicentric disease Not eligible for radical intent radiation. IDH status is unknown or uninterpretable (IHC or gene sequencing). Use of prior radiotherapy to the head-neck region or brain or chemotherapy. Contraindication/unable to undergo MRI or PET scan during radiation.

Outcomes:
Proportion of patients undergoing volumetric and biological adaptation. Cumulative rate of symptomatic radionecrosis (RN) at 2 years.; Cumulative rate of symptomatic radionecrosis at 2 years
Chest X-ray Tomosynthesis for Detection of Lung Cancer and Lung Disease
Trial ID: NCT06577883
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-11-02
Completion date: 2027-06-30
Sponsor: University of California, San Diego
Collaborators: AIxSCAN, Inc.
Conditions: Lung Cancer
Interventions: Chest X-ray Tomosynthesis
Locations: University of California San Diego
Countries: United States
Enrollment: 1000
Age range: 30 Years - 85 Years
Sex: ALL

Summary:
The goal of this observational clinical trial is to learn if chest tomosynthesis is a potential alternative to computed tomography for the detection of lung cancer. It will also develop artificial intelligence tools to aid in the diagnosis of lung cancer on chest tomosynthesis images. The main questions it aims to answer are:

* What is the accuracy of chest X-ray tomosynthesis in diagnosing lung cancer in a population of individuals undergoing lung cancer screening or evaluation of a suspicious lung nodule?
* Can artificial intelligence help us detect lung cancer on chest tomosynthesis images?

Researchers will compare chest tomosynthesis images to computed tomography scans for each participant to see how they compare in diagnosing lung cancer.

Participants will a chest tomosynthesis scan in addition to their routine clinical computed tomography scan.

Eligibility criteria:
Inclusion Criteria:

* undergoing lung cancer screening
* undergoing evaluation of suspicious pulmonary nodule
* newly diagnosed lung cancer

Exclusion Criteria:

* prior history of lung cancer treatment

Outcomes:
Chest X-ray tomosynthesis detection of lung cancer
Investigation of 8-Week Artificial Intelligence Based Structured Exercise Program in Healthy Adults
Trial ID: NCT06508294
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-05-01
Completion date: 2025-08-30
Sponsor: Hacettepe University
Collaborators: Gulhane Training and Research Hospital
Conditions: Health Behavior
Interventions: 8 week artificial intelligence based posture exercises
Locations: University of Health Science
Countries: Turkey (Türkiye)
Enrollment: 36
Age range: 18 Years - 25 Years
Sex: ALL

Summary:
Posture is defined as the arrangement or orientation of body parts in relation to each other.In this study, it was aimed to examine the effect of an 8-week artificial intelligence-based structured exercise program in healthy adults. It performs measurements on frontal and lateral images of the individual in normal posture Photo of a participant showing how the Posture Screen Mobile app evaluates the horizontal tilt and inclination of the head, shoulders and hips from a frontal view.The craniovertebral angle (CVA) is a measurement of upper cervical posture obtained by calculating the angle between the ear tragus and a horizontal line passing through the spinous process of C7). KVA will be done with Posture Screen Mobile Phone application.Thoracic kyphosis is the anteroposterior curve of the thoracic spine in the sagittal plane and is defined using the Cobb angle.Thoracic kyphosis is the hunching of the spine in the upper back into an extreme external curve due to deformity.An artificial intelligence application called "Musclewiki" will be used for healthy adults to continue their exercise training at home. For 8 weeks, exercise will be practiced 3 times a week in the company of a physiotherapist, accompanied by a physiotherapist through artificial intelligence.

Eligibility criteria:
Inclusion Criteria:

* having a smartphone
* volunteering to participate in the study

Exclusion Criteria:

* having a neurological, orthopedic or rheumatologic problem in the spine that will affect its mechanics
* a history of cancer affecting the spine
* a history of any trauma to the spine in the last 6 months
* having vision or hearing problems that may affect the use of artificial intelligence

Outcomes:
craniovertebral angle measurement
Feasibility of an Artificial Intelligence Phone Call for Post-operative Care Following Cataract Surgery in a Diverse Population
Trial ID: NCT05766722
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-04-01
Completion date: 2023-12-01
Sponsor: Uptown Eye Specialists
Conditions: Cataract
Interventions: DORA AI
Enrollment: 250
Age range: 18 Years - None
Sex: ALL

Summary:
To analyze the evidence for clinical safety, usability, acceptability, satisfaction, appropriateness, and cost-effectiveness of Dora for recognizing patients who require additional in-person evaluation post cataract surgery. To assess Dora's sensitivity and specificity in cataract surgery management To determine what can be learnt to enhance AI technology in the field of ophthalmology especially when working with culturally diverse patients such as Punjabi and Chinese speaking patients.

Eligibility criteria:
Inclusion Criteria:

* Patients who are undergoing uncomplicated cataract surgery at a high volume surgical centre.

Exclusion Criteria:

* clinically significant complications during surgery that require more than the standard postop treatment or follow-up

Outcomes:
Dora is clinically safe; Dora is usable; Dora is clinically appropriate; Dora's clinical cost-effectiveness
AI-assisted Endoscopic Ultrasound Grading of Early Esophageal Cancer Invasion Depth: A Multicenter, Prospective, Randomized Cohort Study
Trial ID: NCT07251114
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-11-20
Completion date: 2028-12-31
Sponsor: Fujian Provincial Hospital
Conditions: Esophageal Cancer Stage; Early Esophageal Cancer
Interventions: Artificial Intelligence system
Locations: Fujian provincial hospital; Affiliated Hospital of Putian University; Putian First Hospital; Putian Hospital of Traditional Chinese Medicine; Xianyou County General Hospital
Countries: China; China; China; China; China
Enrollment: 200
Age range: 18 Years - None
Sex: ALL

Summary:
This study mainly uses an artificial intelligence system to assist in the classification of the depth of invasion of early esophageal squamous cell carcinoma under ultrasound endoscopy, providing a basis for preoperative T staging and diagnosis and treatment decisions.

Eligibility criteria:
Inclusion Criteria:

* Satisfy ①⑧⑨ and one of the following conditions simultaneously: ②③④⑤⑥⑦ ① Age over 18 years old, ② Esophageal ulcer, ③ low-grade intraepithelial neoplasia, ④ high-grade intraepithelial neoplasia, ⑤ patients with esophageal squamous cell carcinoma, ⑥ white patches of esophageal mucosa, ⑦ esophageal polyps, ⑧ with endoscopic examination records and detailed pathological records, ⑨ agree to participate in the study;

Exclusion Criteria:

* ① Patients who have undergone esophageal cancer surgery, ② those with a history of radiotherapy and chemotherapy for esophageal cancer, ③ patients with missing data.

Outcomes:
The accuracy of grading judgment of infiltration depth
Comparing Efficacy and Adherence of Smartphone-guided Exercises to Conventional Self-directed Exercises for Neck Pain in Office Workers: A Randomized Controlled Trial Protocol
Trial ID: NCT06485804
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-07-15
Completion date: 2024-10-31
Sponsor: University of Monastir
Collaborators: Sana Salah; Amr Chaabeni
Conditions: Neck Pain; Work-Related Condition
Interventions: Smartphone application guided exercises
Enrollment: 34
Age range: 30 Years - 63 Years
Sex: ALL

Summary:
This study aims to compare the impact of two different methods to administer self-exercises in administrative workers with neck pain: an Artificial Intelligence embedded mobile application and written sheet.

Eligibility criteria:
Inclusion Criteria:

* The presence of a chronic NP lasting for more than three months, and a pain level at baseline assessment ≥ 30 (VAS)

Exclusion Criteria:

* Acute neck pain, neck pain from specific causes (eg; Chronic inflammatory diseases), spine trauma or surgery, cervical radiculopathy or myelopathy, and physical therapy treatments in the last six months before baseline assessment.

Outcomes:
Pain intensity
Preliminary Assessment of iCare RETCAD as an Automated Tool for Diabetic Retinopathy Screening
Trial ID: NCT07404657
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-02
Completion date: 2026-04
Sponsor: Centervue SpA
Conditions: Diabetic Retinopathy (DR)
Interventions: automatic analysis of fundus images performed by the software tool based on artificial intelligence
Enrollment: 198
Age range: 22 Years - None
Sex: ALL

Summary:
This study is being done to evaluate the performance of a software that uses artificial intelligence to analyze photographs of the retina to help detect diabetic retinopathy. The study will also assess the safety of the software in combination with a fundus camera already available on the market. This software analyzes retinal photographs to detect more than mild diabetic retinopathy in adults with diabetes. The results will be compared to expert human evaluations.

Eligibility criteria:
Inclusion Criteria:

* 22 years old or older
* diabetes or diabetic retinopathy
* understand the study information and able to sign a consent form

Exclusion Criteria:

* cannot tolerate eye imaging tests
* laser treatment or injections
* eye surgery, except for simple cataract surgery
* currently involved in another study
* pregnant
* cannot or do not want to have your eyes dilated
* photodynamic therapy within the last 90 days

Outcomes:
Number of subjects whose results provided by the automatic AI-based tool match the reading center grading for the identification of referable diabetic eye disease (more than mild DR).
Voice Biomarkers for Type 2 Diabetes Detection: A Two-Stage Validation Study
Trial ID: NCT07421921
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2025-09-01
Completion date: 2026-03-16
Sponsor: Thymia Limited
Conditions: Type 2 Diabetes; Diabetes (DM); T2DM; T2DM (Type 2 Diabetes Mellitus)
Locations: Online
Countries: United Kingdom
Enrollment: 7319
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this observational study is to learn if computer analysis of voice recordings can detect Type 2 diabetes in adults.

The main questions it aims to answer are:

* Can advanced voice analysis accurately identify participants with Type 2 diabetes or pre-diabetes based on vocal biomarkers?
* How do voice-based predictions compare to HbA1c blood test results for diabetes screening?
* Can machine learning approaches effectively address the challenge of undiagnosed diabetes in population screening?

Participants will:

* Record themselves reading a short passage and answering brief questions out loud in a single online session.
* Complete health questionnaires about diabetes risk factors, medications, and general health status.
* A subset of participants (n=1,000) will provide a blood sample through an at-home HbA1c testing kit to validate voice-based predictions against laboratory results.
* Use their own devices (computer, tablet, or smartphone) to complete all study activities online from home.

Eligibility criteria:
Inclusion Criteria:

* 18+ years of age
* English as a first language
* No language difficulties
* Geographically based in the UK
* Normal or corrected to normal eye-sight, i.e., wearing glasses/contact lenses

Exclusion Criteria:

* No hearing impairments

Outcomes:
Accuracy of AI Model for Type 2 Diabetes Classification as Assessed by Voice Biomarker Analysis
EARLY IDENTIFICATION OF VENTILATOR ASSOCIATED PNEUMONIA USING MACHINE LEARNING TECHNIQUES: A PROSPECTIVE COHORT
Trial ID: NCT06917521
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-07-01
Completion date: 2025-06-15
Sponsor: Ente Ospedaliero Cantonale, Bellinzona
Conditions: Ventilator Associated Pneumonia ( VAP)
Locations: Ospedale civico Lugano
Countries: Switzerland
Enrollment: 76
Age range: 18 Years - None
Sex: ALL

Summary:
Ventilator-associated pneumonia (VAP) is the most frequent infection in the intensive care setting. For VAP there is currently no reliable diagnostic criteria. We aimed with the present study, using data from the mechanical ventilator to identify early this infection using artificial intelligence methods .

Eligibility criteria:
Inclusion Criteria:

* adult patients admitted to our ICU requiring invasive respiratory support for at least 48 hours

Exclusion Criteria:

* previuos pneumonia

Outcomes:
Early identification of VAP
Comparison of Dermatoscopy X400 Interpreted by 6 Non-dermatologists (3 Gynecologists, 3 General Practitioners) and an Artificial Intelligence Software Skin Artificial Intelligence Versus Dermatoscopy x 20 Interpreted by 3 Expert Dermatologists, for the Identification of 150 Genital Pigmented Tumors: Towards a New Medical Organization of the Dermatologist, Non-dermatologist Articulation?
Trial ID: NCT06079619
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-03-01
Completion date: 2022-04-20
Sponsor: Centre Hospitalier Universitaire de Saint Etienne
Conditions: Genital Tumor; Pigmented Lesions
Interventions: Analysis of photos of pigmented tumours by the three groups
Locations: CHU Saint Etienne
Countries: France
Enrollment: 157
Age range: 18 Years - None
Sex: ALL

Summary:
Pigmented lesions of the genital mucosa are common, affecting between 10% and 20% of the population. The clinical appearance is sometimes confusing. They are often extensive, irregular, and inhomogeneous in color, which can make them look suspicious. Moreover, dermatoscopic imaging is often complex to interpret, as shown by the many different patterns reported in the literature.

Thus, preliminary work in confocal microscopy has shown that it is possible to simplify diagnosis by using microscopic, in vivo, non-invasive imaging. However, CIM cameras are rare, very expensive and difficult to access.

Conversely, digital dermatoscopes with 400x magnification are much less expensive and can also allow, in a certain number of cases, an examination at the cellular level of tumors, with, in our opinion, the advantage of being easy to learn. In the context of the pooling of resources within medical centers in particular, their dissemination should accelerate. Furthermore, x400 dermatoscopy can provide similar information to IVCM for the most frequent black genital tumors.

Eligibility criteria:
Inclusion Criteria:

* Men and women with a pigmented lesion of the genital mucosa, having benefited from a mucosal consultation at the University Hospital of Saint-Etienne, with Professor Perrot.
* The patient's clinical file must contain clinical images, dermatoscopy images x 20 and at least 1 dermatoscopy image x 400.

Exclusion Criteria:

\- None

Outcomes:
Comparison results between expert dermatologists with x20 dermatoscopic photos (Group A) and non-dermatologists physicians with x400 dermatoscopic photos (Group B)
A Study to Evaluate Accuracy and Validity of Chang Gung Pleural Effusion Detection Software Compared With Reference by Qualified Physicians
Trial ID: NCT05903287
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-06-17
Completion date: 2024-08-12
Sponsor: Chang Gung Memorial Hospital
Conditions: Pleural Effusion
Interventions: Chang Gung Pleural Effusion Detection Software
Locations: Chang Gung memorial hospital
Countries: Taiwan
Enrollment: 282
Age range: 20 Years - 100 Years
Sex: ALL

Summary:
The Chang Gung Pleural Effusion Detection Software is a medical software that can automatically detect whether there is a pleural effusion in Chest X-Ray. The purpose of this study is to verify whether the Chang Gung Pleural Effusion Detection Software can correctly identify patients with pleural effusion in Chest X-Ray. The results of the software analysis will be used for the performance of the software on the primary and secondary outcomes.

Eligibility criteria:
Inclusion Criteria:

* The case were over 20 years old and under 100 years old.
* DICOM format.
* Brand model of Canon.
* DICOM resolution length range (700-3400 pixels), width range (490-3600 pixels).
* poster-anterior view(PA-view) of chest X-ray

Exclusion Criteria:

* The chest X-ray contains items that affect interpretation, such as chest tubes, endotracheal tubes, and heart rhythm regulators, but does not include patches and circuits used in electrocardiograms.
* The chest X-ray that are difficult to interpret due to poor image quality.

Outcomes:
Sensitivity