moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
A RadioPathomics Integrated Artificial Intelligence System to Predict Neoadjuvant Chemoradiotherapy Response for Locally Advanced Rectal Cancer: A Multicenter, Prospective and Observational Clinical Study
Trial ID: NCT04271657
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-01-10
Completion date: 2020-12-30
Sponsor: Sixth Affiliated Hospital, Sun Yat-sen University
Collaborators: The Third Affiliated Hospital of Kunming Medical College.; Sir Run Run Shaw Hospital
Conditions: Rectal Cancer
Locations: the Sixth Affiliated Hospital of Sun Yat-sen University; The Third Affiliated Hospital of Kunming Medical College; Sir Run Run Shaw Hospital
Countries: China; China; China
Enrollment: 100
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
In this study, investigators utilize a radiopathomics integrated Artificial Intelligence (AI) supportive system to predict tumor response to neoadjuvant chemoradiotherapy (nCRT) before its administration for patients with locally advanced rectal cancer (LARC). By the system, whether the participants achieve the pathologic complete response (pCR) will be identified based on the radiopathomics features extracted from the pre-nCRT Magnetic Resonance Imaging (MRI) and biopsy images. The predictive power to discriminate the pCR individuals from non-pCR patients, will be validated in this multicenter, prospective clinical study.

Eligibility criteria:
Inclusion Criteria:

* pathologically diagnosed as rectal adenocarcinoma
* defined as clinical II-III staging (≥T3, and/or positive nodal status) without distant metastasis by enhanced Magnetic Resonance Imaging (MRI)
* intending to receive or undergoing neoadjuvant concurrent chemoradiotherapy (5-fluorouracil based chemotherapy, given orally or intravenously; Intensity-Modulated Radiotherapy or Volume-Modulated Radiotherapy delivered at 50 gray (Gy) in gross tumor volume (GTV) and 45 Gy in clinical target volume (CTV) by 25 fractions)
* intending to receive total mesorectum excision (TME) surgery after neoadjuvant therapy (not completed at the enrollment), and adjuvant chemotherapy
* MRI (high-solution T2-weighted imaging, contrast-enhanced T1-weighted imaging, and diffusion-weighted imaging are required) examination is completed before the neoadjuvant chemoradiotherapy
* biopsy H\&E stained slides are available and scanned with high resolution before the neoadjuvant chemoradiotherapy

Exclusion Criteria:

* with history of other cancer
* insufficient imaging quality of MRI to delineate tumor volume or obtain measurements (e.g., lack of sequence, motion artifacts)
* insufficient imaging quality of biopsy slides imaging to delineate tumor volume or obtain measurements (e.g., tissue dissection, color anomaly)
* incomplete neoadjuvant chemoradiotherapy
* no surgery after neoadjuvant chemoradiotherapy resulting in lack of pathologic assessment of tumor response
* tumor recurrence or distant metastasis during neoadjuvant chemoradiotherapy

Outcomes:
The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of the radiopathomics artificial intelligence model
Multimodal Imaging Diagnosis and Decision Aid System for Hepatic Echinococcosis Based on Image Omics and Vision Macromodel
Trial ID: NCT06540742
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-09-30
Completion date: 2024-09-12
Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Conditions: Hepatic Echinococcosis
Interventions: Artificial intelligence identifies liver hydatids
Locations: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Countries: China
Enrollment: 1000
Age range: 1 Year - 80 Years
Sex: ALL

Summary:
Hepatic echinococcosis (hepatic echinococcosis) is an important zoonotic disease widely existing in the agricultural and pastoral areas of northwest China. The disease can be parasitic in any part of the human body and may affect multiple organs. In severe cases, patients will lose the ability to work. At present, the disease faces challenges in diagnostic accuracy, specific type identification, preoperative activity assessment, postoperative recurrence prediction, and decision evaluation of T-tube indentation. This problem is particularly significant in high incidence areas with uneven distribution of medical resources and shortage of excellent imaging physicians and clinicians. Our previous studies have demonstrated that the use of visual large models and imaging omics algorithms can effectively segment liver echinococcus lesions, extract key features, and provide clinicians with accurate and reliable diagnosis and treatment recommendations. We believe that on the basis of the transformation of different medical image modes (such as MRI, CT and ultrasound) based on a broader multicentre large data set, the goal of effective identification, diagnosis, surgical decision support, and postoperative accurate prediction of hepatic echinococcosis can be achieved. We will use artificial intelligence technology solutions such as adversarial generation network, vision large model, image omics and decision level fusion, taking into account diagnosis and treatment efficiency, diagnosis and treatment automation and interpretability of diagnosis results, to build a comprehensive accurate diagnosis and prognosis system for hepatic echinococcosis

Eligibility criteria:
Inclusion Criteria:

1. Patients with complete original images in CT, ultrasound, and MRI dcim formats
2. Patients with liver hydatid confirmed by pathology after operation
3. Patients with complete clinical data preservation

Exclusion Criteria:

1. Patients with poor quality imaging data
2. Patients with incomplete clinical data
3. CE4 and CE5 liver hydatid patients diagnosed by imaging alone without surgical treatment

Outcomes:
roc curve; AUC; PPV; NPV
Using Gout Buddy to Enhance Dietary Literacy and Gout Management-A Mixed Method Clinical Trial
Trial ID: NCT07004231
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-07-01
Completion date: 2026-08-15
Sponsor: SingHealth Polyclinics
Conditions: Gout
Interventions: GOUT Buddy, an AI chatbot
Locations: SingHealth Polyclinics
Countries: Singapore
Enrollment: 110
Age range: 21 Years - None
Sex: ALL

Summary:
Background: Dietary management is crucial for gout, but patients often lack adequate dietary literacy. However, patients often lack adequate dietary literacy and struggle to navigate complex dietary recommendations. Gout Buddy is an autonomous AI agent to offer personalized gout education and awareness tailored to individual needs. This study aims to evaluate the effectiveness and user experience of Gout Buddy, in improving dietary literacy and gout management.

Materials and methods: A two-arm RCT will randomize patients with gout to either the intervention (Gout Buddy) or control (standard care). Two study visits within 3-6 months will assess changes in dietary literacy and gout management behaviours. Qualitative interviews will be conducted with intervention arm participants and multidisciplinary care team members to explore their experiences with Gout Buddy till the point of data saturation.

Expected Outcomes: The current trial is expected to demonstrate the effectiveness of Gout Buddy in improving dietary literacy and gout management compared to standard care. Qualitative data will provide rich insights into user engagement, perceived benefits, challenges, and the feasibility of integrating the chatbot into routine gout management.

Significance: This study will provide evidence on the potential of AI chatbots to enhance gout self-management. The findings will inform the development and implementation of digital health tools for chronic disease management, potentially improving patient outcomes and reducing the burden of gout.

Eligibility criteria:
Inclusion Criteria:

* Age \> 21 years
* At least one serum uric acid \> 360 μmol/L or an episode of acute gout attack within the last 18 months
* Able to read and speak English
* Access to an internet-enabled device compatible with Gout Buddy

Exclusion Criteria:

* Not a Singapore citizen or permanent resident
* Pregnant
* End-stage kidney disease or on renal replacement therapy
* Known terminal illness
* Visual and/or hearing impairment
* Cognitive impairment or mental illness
* Unable to provide informed consent
* Individuals who do not have a personal mobile device with internet access

Outcomes:
Food Frequency Questionnaire; Dietary literacy about gout; Medication adherence report scale- 5 items; Health related quality of life (HRQoL)
Technology Revisiting Ultrasound Solutions of Tomorrow - Artificial Intelligence for Improved Echocardiography
Trial ID: NCT04580095
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-09-29
Completion date: 2021-06-30
Sponsor: Helse Nord-Trøndelag HF
Collaborators: St. Olavs Hospital
Conditions: Heart Diseases
Interventions: AI algorithm for apical foreshortening in echocardiography
Locations: St. Olav University Hospital
Countries: Norway
Enrollment: 88
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of this study is to assess the effect of artificial intelligence algorithms on image quality in echocardiography.

Eligibility criteria:
Inclusion Criteria:

* Capacity to consent

Exclusion Criteria:

* Indication for echocardiographic contrast agents

Outcomes:
Left ventricular apical foreshortening
CT-based Radiomics, Two-dimensional and Three-dimensional Deep Learning Models to Predict Thyroid Cartilage Invasion in Laryngeal Carcinoma: a Multicenter Study
Trial ID: NCT06463756
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-08-13
Completion date: 2024-10-13
Sponsor: First Affiliated Hospital of Chongqing Medical University
Collaborators: Nankai University
Conditions: Laryngeal Carcinoma; Thyroid Cartilage
Interventions: AI
Locations: The First Affiliated Hospital of Chongqing Medical University
Countries: China
Enrollment: 400
Age range: 18 Years - 81 Years
Sex: ALL

Summary:
This retrospective study was to develop and verify CT-based AI model to preoperatively predict the thyroid cartilage invasion of laryngeal cancer patients, so as to provide more accurate diagnosis and treatment basis for clinicians. In addition, the researchers investigated the prediction of survival outcomes of patients by the above optimal models.

Eligibility criteria:
Inclusion Criteria:

1. Availability of complete clinical data
2. Surgery-proven or biopsy-proven diagnosis of laryngeal squamous cell carcinoma
3. CT examination performed within 2 weeks before surgery

Exclusion Criteria:

1. Patients who received preoperative chemotherapy or radiation therapy
2. CT images with significant artifacts
3. Patients with tumor recurrence

Outcomes:
Area under the curve, AUC
AI4HOPE - Artificial Intelligence Based Health, Optimism, Purpose and Endurance in Palliative Care for Dementia: Pilot Study 1 Digital Toolkit Assessment
Trial ID: NCT06289322
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-02
Completion date: 2026-12
Sponsor: University of Bonn
Collaborators: University College Cork; University of Leeds; Fundacion Intras; Universidade do Porto; University Medical Centre Maribor
Conditions: Dementia
Interventions: Digital toolkit for assessment
Enrollment: 150
Age range: None - None
Sex: ALL

Summary:
The pilot study will develop and test a standardized assessment toolkit that will provide conclusive information with a tolerable burden for cognitively impaired patients with dementia.

Eligibility criteria:
Inclusion Criteria:

* Mild to moderate dementia of any type
* Montrea Cognitive Assessment (MoCA) score of 16-25
* Living at home, or in residential or nursing home care
* Receiving adequate social support

Exclusion Criteria:

* Moderate/severe cognitive impairment (MoCA \< 16)
* No cognitive impairment (MoCA \>25)
* Inpatient or acute hospital treatment
* Infectious diseases or lokal skin conditons preventing the use of wearable body sensors

Outcomes:
Feasibility and acceptability of toolkit; Accuracy of toolkit assessments
Intelligent Cataract Screening With Portable Slit-lamp
Trial ID: NCT05204095
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-02-10
Completion date: 2023-06-30
Sponsor: Sun Yat-sen University
Conditions: Catarct Screening; Artificial Intelligence
Interventions: Portable slit-lamp photography
Locations: Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity
Countries: China
Enrollment: 3000
Age range: None - 100 Years
Sex: ALL

Summary:
Based on the portable slit-lamp connected to the smartphone, the artificial intelligence machine learning algorithm is used to establish a cataract screening model by prospectively collecting the anterior segment photographic data, and a portable slit lamp intelligent screening referral model based on the smartphone connection is also established.

Eligibility criteria:
Inclusion Criteria:

* 0 to 100 years old

Exclusion Criteria:

* (1) History of eye disease except for age-related cataracts (2) History of eye trauma or eye surgery (3) Unable to cooperate with ophthalmology and general examination (4) Individuals with incomplete information (5) Unwilling to participate in this study

Outcomes:
The accuracy of referral
Anatomical Validity of Experts and ScanNav Anatomy PNB When Identifying Sono-anatomical Structures for Ultrasound-Guided Regional Anaesthesia
Trial ID: NCT04983771
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-09-01
Completion date: 2022-05-06
Sponsor: IntelligentUltrasound Limited
Collaborators: University of Oxford
Conditions: Ultrasound Imaging of Anatomical Structures
Interventions: Annotate ultrasound scans
Locations: Intelligent Ultrasound Limited
Countries: United Kingdom
Enrollment: 19
Age range: None - None
Sex: ALL

Summary:
This is a multi-centre, prospective, observational study, conducted under the auspices of the University of Oxford to evaluate ultrasound image analysis by human experts and an artificial intelligence device in the context of ultrasound-guided regional anaesthesia (UGRA).

Eligibility criteria:
Inclusion Criteria:

Member of a relevant professional society (e.g., RA-UK, ESRA, ASRA) and meets at least three of the following criteria:

* Completed advanced training in UGRA or have \>10 years of independent practice in UGRA
* Hold a qualification related to UGRA (e.g., EDRA, higher degree or equivalent)
* Regularly delivers direct clinical care using UGRA, including for 'awake' surgery where indicated
* Regularly teaches UGRA in the course of their clinical work, including advanced techniques (Plan B/C/D blocks) where indicated

Exclusion Criteria:

• none

Outcomes:
Experts' variability; ScanNav anatomy PNB comparison
Health Care Workers Cohort Study in the EuCARE Project (EuCARE-HCW)
Trial ID: NCT05534009
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-04-01
Completion date: 2025-12-15
Sponsor: Euresist Network GEIE
Collaborators: Centro Hospitalar Lisboa Ocidental; ASST Santi Paolo e Carlo; Bach Mai Hospital; Hospital Regional de Alta Especialidad "Dr. Juan Graham Casasus"; Heinrich-Heine University, Duesseldorf; Istituto Europeo di Oncologia; Vilnius University Hospital Santaros Klinikos
Conditions: COVID-19
Interventions: Follow up of the first known immunization event
Locations: Heinrich Heine university; Azienda Socio-Sanitaria Territoriale Santi Paolo e Carlo; Ieo Istituto Europeo Di Oncologia; Viesoji Istaiga Vilniaus Uiversiteto Ligoninė Santaros klinikos; Hospital Juan Ghaham Casasus; Centro Hospitalar de Lisboa Ocidental; Bach Mai Hospital
Countries: Germany; Italy; Italy; Lithuania; Mexico; Portugal; Vietnam
Enrollment: 1800
Age range: 18 Years - None
Sex: ALL

Summary:
The WP3 healthcare workers cohort in EuCARE is an observational multicentre study including collection of retrospective (historical) and prospective data and sample collection from health care workers with either a vaccination or a confirmed SARS-CoV-2 infection. Samples from HCW are sent to central laboratories for WP2 study were the impact of different variants in humoral and cellular immunity is to be analysed by biostatistical methods and with artificial intelligence in WP5. This analysis will focus on the impact on vaccine escape of viral variants / viral sequences as well as on any escape from any combination of natural and vaccine induced immunity. Eight countries will participate (Portugal, Italy, Germany, Lithuania, Georgia, Russia, Vietnam and Mexico).

Eligibility criteria:
Inclusion Criteria:

The study subjects will be selected based on the following inclusion criteria:

1. Adult (\>18 years) HCW with mandatory information on their SARS-CoV-2 infection and vaccination status.
2. For those with a history of SARS-CoV-2 infection, diagnosis obtained by detection of SARS-CoV-2 RNA
3. Having a signed informed consent when required by ethical approval

Exclusion Criteria: NONE

Outcomes:
Number of HCW involved in the study
Artificial Intelligence Application of Neurogenic Bowel Training in Patients With Spinal Cord Injury
Trial ID: NCT07369349
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-02-20
Completion date: 2026-11-01
Sponsor: Senem Duman
Conditions: Spinal Cord Injury; Neurogenic Bowel Dysfunction
Interventions: Mobil Application
Enrollment: 34
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
The aim of this study is to evaluate the effectiveness of patient education delivered through an artificial intelligence (AI)-based mobile application in the management of neurogenic bowel dysfunction in individuals with spinal cord injury. Within the scope of the study, it will be examined whether the mobile application reduces neurogenic bowel scores, contributes to maintaining stool scores within the normal range, and improves the quality of life of individuals. Accordingly, the AI-based education model to be developed is expected to facilitate the integration of bowel management practices into daily life, prevent complications, and enhance social participation.

\-

Eligibility criteria:
Inclusion Criteria:

* Volunteers who consent to participate.
* Age over 18 years.
* Diagnosed with spinal cord injury (SCI).
* Diagnosed with neurogenic bowel dysfunction (NBD).
* Not in spinal shock (return of the bulbocavernosus reflex).
* Having received routine NBD education at the study center within the last month.

Exclusion Criteria:

* Individuals with mental retardation.
* Individuals with visual and/or hearing impairments.
* Individuals with communication problems.
* Individuals with a history of additional injuries other than SCI (e.g., head trauma, pelvic injury, pelvic surgery).
* Individuals diagnosed with neurological disorders such as stroke, Parkinson's disease, polyneuropathy, or multiple sclerosis.
* Individuals with irritable bowel syndrome (IBS) or inflammatory bowel disease (IBD).
* Individuals diagnosed with gastrointestinal malignancy.

Outcomes:
Change in Neurogenic Bowel Dysfunction (NBD) Score Assessed by the Neurogenic Bowel Dysfunction Scale; Change in Bowel Care Time Measured in Minutes
Precision Diagnosis and Prognostic Prediction of Hypertrophic Cardiomyopathy Using Artificial Intelligence: A Multicenter Study
Trial ID: NCT07263204
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-01-01
Completion date: 2026-12-31
Sponsor: Second Affiliated Hospital, School of Medicine, Zhejiang University
Conditions: Hypertrophic Cardiomyopathy (HCM); Left Ventricular Hypertrophy
Locations: Second Affiliated Hospital, Zhejiang University School of Medicine
Countries: China
Enrollment: 15000
Age range: 18 Years - None
Sex: ALL

Summary:
By harnessing artificial intelligence to decode the 12-lead electrocardiogram, the project will enable precise ECG-based phenotyping of hypertrophic cardiomyopathy-accurately classifying septal, apical, and other morphologic subtypes-while simultaneously differentiating HCM from hypertensive heart disease, aortic stenosis, and other phenocopy disorders.

Eligibility criteria:
Inclusion Criteria:

1. Adults aged ≥ 18 years.
2. HCM cohort: Adults diagnosed with hypertrophic cardiomyopathy in accordance with the \*2023 Chinese Guidelines for the Diagnosis and Treatment of Hypertrophic Cardiomyopathy in Adults\*.
3. HCM phenocopy cohort: Adults with an LV wall thickness ≥ 13 mm at any site on echocardiography.
4. Healthy-control cohort: Adults with no history of cardiac disease and no evidence of myocardial hypertrophy on echocardiography.

Exclusion Criteria:

Patients from whom analyzable ECG data cannot be obtained.

Outcomes:
model diagnostic performance
Effect of Using Artificial Intelligence Chatbot About Electronic Fetal Monitoring on Maternity Nursing Students' Performance
Trial ID: NCT07051343
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-06-01
Completion date: 2025-12-01
Sponsor: Mansoura University
Conditions: Artificial Intelligence (AI); Electronic Fetal Monitoring; Nursing Students
Interventions: designed artificial intelligence Chatbot education about electronic fetal monitoring; traditional teaching method
Locations: Faculty of Nursing, Mansoura University
Countries: Egypt
Enrollment: 84
Age range: None - None
Sex: ALL

Summary:
* The study aims to investigate the effect of using artificial intelligence Chatbot education about electronic fetal monitoring on maternity nursing students' performance.
* The aim will be achieved through the following,

  1. Designing AI Chatbot about electronic fetal monitoring.
  2. Exploring the effect of using AI Chatbot about electronic fetal monitoring on students' performance, interest in education, self-directed learning \& feedback satisfaction.
* The students will be divided into two groups, the intervention group will use EFM Chatbot, and the control group will receive the traditional learning

Eligibility criteria:
Inclusion Criteria:

* third level students at faculty of nursing Mansoura university who will register midwifery course of academic year 2024/2025

Exclusion Criteria:

* students who refuse to participate in the study and those not registered in the course

Outcomes:
Maternity nursing students who received EFM Chatbot education will have better theoretical knowledge regarding EFM within 3 months.; Maternity nursing students who received EFM Chatbot education will have satisfactory practical interpretation skills regarding EFM within 3 months.; Maternity nursing students who received EFM Chatbot education will have better clinical reasoning confidence regarding EFM within 3 months.