moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Adaptive Digital Interventions Through Mobile App for Medication Adherence Improvement in Persons With Hypertension: a Pilot Study for Usability
Trial ID: NCT06228417
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-02-23
Completion date: 2021-03-01
Sponsor: Halmstad University
Collaborators: Vinnova
Conditions: Hypertension
Interventions: Adaptive digital intervention
Locations: Halmstad University
Countries: Sweden
Enrollment: 14
Age range: 40 Years - 70 Years
Sex: ALL

Summary:
A development and pilot test of an app targeting persons with hypertension. Content in the app was based on persons with hypertensions experiences and a literature review. The participants experience of using the app as well as testing a instrument for medication adherence.

Eligibility criteria:
Inclusion Criteria:

* 40-70 years of age,
* have hypertension (i.e., ICD 10 codes I10-I15 in the person's medical history) for ≥1 year
* have been prescribed medications to treat said hypertension
* speak and understand Swedish
* have a smartphone

Exclusion Criteria:

* having received medication with unit-dose packages (Apodos)
* previous stroke
* previous myocardial infarction
* psychological disorder or cognitive impairment (i.e. ICD 10 codes F01-F99)
* pregnancy-induced hypertension´
* insulin treatment
* kidney disease, defined as glomerulus filtration rate (GFR) \<60 ml/min.

Outcomes:
Medication adherence; Adherence hypertension medication
A Pilot Study Using AI Algorithms and PPG Technology for the Detection of Venous Diseases
Trial ID: NCT06433024
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-06
Completion date: 2024-09
Sponsor: The Whiteley Clinic
Conditions: Venous Disease
Interventions: PPG Diagnostic
Locations: The Whiteley Clinic
Countries: United Kingdom
Enrollment: 20
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
This clinical research aims to evaluate the effectiveness of using Photoplethysmography (PPG) signals combined with Artificial Intelligence (AI) algorithms, for the precise classification and diagnosis of Venous Diseases of the lower limb. This study invites a group of participants who currently undergoing investigations for venous disease at The Whiteley Clinic (hereinafter referred to as TWC). The Participants will be classified into control (healthy individuals with no significant venous disease) and chronic venous disease (CVD) (diagnosed with proven venous disease) groups. Prospective participants who express an interest in being included in the study will be given a patient information sheet and will undergo a briefing of the pilot study. If they consent and sign the relevant consent forms, the participants will perform a series of standardized exercises under the supervision of a consultant vascular surgeon. Throughout the exercises, a data acquisition device attached to the ankle records the PPG signals, capturing the changes in blood volume due to the reflected PPG signals from the red blood cells during the movement. Thus, once the data is collected and recorded, this allows for the analysis of the data of the control group and CVD group against each other. During the analysis of the two groups' PPG signals, the objective lies within the capability to detect subtle nuances in the patterns of the PPG signals during the performed movements using AI algorithms. The AI algorithms will distinguish patterns or features indicating the presence or absence of venous disease. This study seeks to contribute valuable insights into enhancing the diagnosis of venous disease using PPG and AI algorithms, paving novel approaches to Venous healthcare.

Eligibility criteria:
Inclusion Criteria:

* Patients are attending for investigation of suspected venous disease. Patients must be able to walk and mobile normally and have good skin integrity of the lower leg, where the PPG is attached.

All patients attending TWC are 18 years or older.

Exclusion Criteria:

* Subjects with known arterial occlusive disease or physical disability affecting gait or ankle movement will be excluded.

Patients unable to have a PPG attached to the lower leg (ie: active ulceration) will be excluded.

Patients unable to give consent. Pregnant female.

Outcomes:
Diagnostic Accuracy of an AI Model for Venous Disease Detection Using PPG Signals
Use of Artificial Intelligence in the Symptomatic BReAst Clinic SEtting - A Retrospective Study (AI-BRACE)
Trial ID: NCT06578988
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2024-03-01
Completion date: 2025-10-01
Sponsor: Royal Marsden NHS Foundation Trust
Collaborators: Imperial College Healthcare NHS Trust; St George's University Hospitals NHS Foundation Trust; Royal Surrey County Hospital NHS Foundation Trust
Conditions: Cancer, Breast
Interventions: Mammography Images
Locations: The Royal Marsden NHS Foundation Trust
Countries: United Kingdom
Enrollment: 25000
Age range: 18 Years - None
Sex: ALL

Summary:
Artificial Intelligence (AI) systems for the classification of mammography images have been developed and are beginning to be trialled and deployed in a breast cancer screening setting with encouraging results.

It is reasonable to think that these systems could be useful in the context of symptomatic breast clinic. However, these systems developed in the screening setting have unknown performance in the context of symptomatic breast clinic.

It is therefore important to test the performance of these systems in this alternative context.

This study will use retrospective data, from where it is possible to determine ground truth outcomes with greater confidence, accessing relatively large volumes of data with less patient burden when compared to prospective studies. This important cohort of patients has been less investigated to date, mainly because symptomatic data is typically more difficult to curate than screening data where key data is methodically prospectively collected.

The proposed work will be carried out in collaboration with a selected AI vendor and local clinical teams to define optimal use case scenarios for the symptomatic breast clinic.

Eligibility criteria:
Inclusion criteria

* Patients 18 years or older attending symptomatic breast clinic.
* Mammography images, including both full field two-dimensional digital mammography and digital breast tomosynthesis.
* Dates of attendance will be from January 2015\* to December 2019 at the lead data collection site. Dates of collection may be different at the other sites depending on local data curation consideration but will be a minimum of 2 years prior to study start to allow determination of ground truth.

  * If any mammography images prior to 2015 should be available at the lead site, these will be collected as well (a maximum of 3). Prior mammograms will also be collected at the other sites if available, depending on local PACS set-up.

Exclusion criteria

* Patients under the age of 18 years.
* Patients on the National data opt out.

Outcomes:
Performance of screening tool on symptomatic data, in terms of sensitivity and specificity.
CHAT-OA: Conversations in Health Literacy Using AI Technology for Osteoarthritis Patients
Trial ID: NCT06778486
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-01-06
Completion date: 2026-08
Sponsor: University of California, Davis
Conditions: Osteoarthritis; Osteoarthritis of the Hip or Knee
Interventions: Artificial Intelligence Chatbot
Locations: University of California Davis Health
Countries: United States
Enrollment: 100
Age range: 18 Years - 99 Years
Sex: ALL

Summary:
The goal of this clinical trial is to assess the use of a generative artificial intelligence large language model chatbot in improving decision making factors in patients with hip and knee osteoarthritis. The main questions it aims to answer are:

Does the use of an artificial intelligence chatbot have an effect on decisional conflict and anxiety related to decision making? Are changes in decisional conflict correlated with changes in patient reported outcomes? Are changes in decisional conflict correlated with health literacy? Participants will interact with an artificial intelligence chatbot prior to their clinic visit with an orthopaedic surgeon, using a structured prompt.

Eligibility criteria:
Inclusion Criteria:

* Adult
* Ability to read and write in English
* All races and ethnicities
* Clinical symptoms of hip and/or knee osteoarthritis

Exclusion Criteria:

* Inability to read and write in English

Outcomes:
Decisional Conflict Scale; Beck Anxiety Inventory
Artificial Intelligence Assisted Tracheal Video Intubation: an Observational Trial About Correctness of LarynGuide™
Trial ID: NCT06397261
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-06-03
Completion date: 2025-05-08
Sponsor: Insel Gruppe AG, University Hospital Bern
Collaborators: aiEndsoscopic
Conditions: Intubation
Interventions: LarynGuide
Locations: Inselspital
Countries: Switzerland
Enrollment: 110
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to asses if the application of the larynGuide™, an assistive software running on a video-laryngoscope platform guiding laryngoscopy and intubation advise correctly and reliably on the position of the tracheal tube after an intubation attempt.

Eligibility criteria:
Inclusion Criteria:

* male or female patients ≥18 years
* ASA class I to III
* undergoing elective surgery in general anesthesia with tracheal intubation

Exclusion Criteria:

* Contraindication for intubation with C-MAC video laryngoscope (Karl Storz, Tuttlingen, Germany) with a Macintosh nr 3 or 4 blade
* Patients \< 18 years
* No proper trained personnel for the device at the study site
* Expected impossible mask ventilation.
* High risk of aspiration (requiring rapid sequence induction intubation)
* Intracranial surgery
* Limited knowledge of German language or refusing informed consent

Outcomes:
Rate of correct assessment given by larynGuide™
Detection of Reduced Left Ventricular Ejection Fraction and Atrial Arrhythmias With Single Lead ECG Using Artificial Intelligence
Trial ID: NCT05010642
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-08-19
Completion date: 2022-04-29
Sponsor: Eko Devices, Inc.
Conditions: Left Ventricular Dysfunction; Ventricular Dysfunction; Heart Diseases; Cardiovascular Diseases
Interventions: Use of Eko DUO electronic Stethoscope
Locations: Ochsner Heart and vascular Institute
Countries: United States
Enrollment: 307
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of this research is to prospectively test and validate the single-lead Low EF algorithm in outpatients in order to test the performance of a single-lead ECG based algorithm to identify people with decreased left ventricular EF.

Eligibility criteria:
Inclusion Criteria:

* English-speaking adults who are 18 years and older
* Able and willing to provide informed consent
* Complete a clinical echocardiogram within 7 days before or after study procedures

Exclusion Criteria:

* Unwilling or unable to provide informed consent
* Patients who are hospitalized

Outcomes:
Single-lead ECG based algorithm development
Would Artifical Intelligence Reduce Delays to Nurse Response Times
Trial ID: NCT06043986
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-05-23
Completion date: 2024-09-02
Sponsor: The Leeds Teaching Hospitals NHS Trust
Conditions: Nurse's Role
Interventions: novel nurse call system
Locations: Leeds Teaching Hospital NHS Trust
Countries: United Kingdom
Enrollment: 40
Age range: 18 Years - 100 Years
Sex: ALL

Summary:
Patients are admitted to wards at all times of day and night and in various states of ill health. As this research is non interventional and does not impact on patient safety, the guidance from the ethics committee was reviewed and agreed that it would be appropriate to enrol every admission into the 2 bed bays and gain consent within 24 hours of admission.All data collected within the trial using the smart tablets will be associated to a study number, no patient details will be stored on the smart tablet and therefore the cloud data store.It has been discussed with the trust information governance and this complies with their regulations.

Any patient identifiable data will be kept by the research team. All data will be archived and stored as per the Sponsors policy. The novel nurse call system has been designed to be user friendly to all patients regardless of age, learning ability and first language used. By using colours, images and words in the hope that this will be accessible to all. The nursing staff on the ward advised on the main reasons for the nurse call system activation and therefore the icons used in the novel system were adapted from this. This trial was discussed in the patient and public involvement group. As this is a pilot trial, any adaptions that need to be made will be made before the large scale trial.

Eligibility criteria:
Inclusion Criteria:

All adults 18 years who are admitted to the cardiology admissions ward will be eligible to take part.

\-

Exclusion Criteria: Any patient who does not wish to participate will have their anonymised data removed from the trial.

\-

Outcomes:
study objective
Artificial Intelligence (AI) - Assisted Visual Impairment Screening Model: Community-based Implementation and Evaluation of Performance, Feasibility and Costs.
Trial ID: NCT06877988
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-06-27
Completion date: 2026-03-31
Sponsor: Singapore Eye Research Institute
Collaborators: Institute of High Performance Computing (IHPC), A*STAR Research Institutes; National University Polyclinics, Singapore
Conditions: Visual Impairment
Interventions: AI
Locations: Pioneer Polyclinic
Countries: Singapore
Enrollment: 400
Age range: 50 Years - None
Sex: ALL

Summary:
The goal of this observational study is to evaluate the performance, operational efficiency, acceptability, feasibility, and cost-effectiveness of an AI-assisted screening model for visual impairment in a community setting. The main questions it aims to answer are:

* Can the AI-assisted screening model improve screening and referral accuracy compared to the current traditional screening approach?
* Does the AI-assisted model enhance operational efficiency and reduce healthcare costs in a community setting?

Researchers will compare the AI-assisted model with the current traditional screening approach to assess its impact on screening accuracy, operational efficiency, and cost-effectiveness.

Participants will:

* Undergo vision screening using either the AI-assisted model or the traditional model.
* Provide feedback on the acceptability of the screening approach.
* Contribute to evaluating the feasibility and costs associated with each screening method.

Eligibility criteria:
Inclusion Criteria:

* Individuals aged 50 years old and above.

Exclusion Criteria:

* Individuals aged below 50 years old.

Outcomes:
Performance of AVIRI on the detection of visual impairment
Prediction Model of Mediastinal Group IV Lymph Node Metastasis in Non-small Cell Lung Cancer Based on CT Radiomics
Trial ID: NCT06496360
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2024-08-01
Completion date: 2025-06
Sponsor: Qilu Hospital of Shandong University
Conditions: Non-small Cell Lung Cancer
Interventions: Artificial Intelligence
Locations: Qilu Hospital of Shandong University
Countries: China
Enrollment: 150
Age range: None - None
Sex: ALL

Summary:
Mediastinal lymph node metastasis is a common metastasis pathway of non-small cell lung cancer (NSCLC), and its occurrence is closely related to the lymphatic drainage pattern, which is different in different pulmonary lobe NSCLC, which poses a challenge for the formulation of individualized treatment strategies. Accurate staging is the prerequisite for accurate treatment of NSCLC. Computed Tomograph (CT) examination is an important tool for evaluating mediastinal lymph node metastasis, which is crucial for making treatment plan and evaluating patient prognosis. However, it is difficult to diagnose metastatic lymph nodes with insignificant imaging features. Especially metastatic lymph nodes in areas 4 and 7. Both zone 4 and zone 7 are hot spots for mediastinal lymph node metastasis. However, clinical guidelines do not make clear provisions on lymph node dissection in zone 4, which makes preoperative clinical staging and prognosis evaluation of patients with NSCLC particularly important. By integrating and analyzing a large amount of data in CT images, the newly emerging CT radiomics technology captures subtle features that may be overlooked in conventional CT scans, showing great application prospects in the accuracy of non-invasive diagnosis of lymph node metastasis. This study aims to explore the mediastinal drainage pattern and the role of CT in evaluating mediastinal lymph node metastasis, in order to provide valuable imaging evidence for accurately judging mediastinal lymph node metastasis of NSCLC, formulating appropriate lymph node dissection scope, optimizing treatment strategy, and improving patient prognosis.

Eligibility criteria:
Inclusion Criteria:

1. Surgical resection and systematic lymph node dissection were performed in the department of thoracic surgery, and the postoperative pathological findings were confirmed as NSCLC and complete pathological diagnostic data were retained.
2. Chest CT enhancement scan was completed within 2 weeks prior to surgery
3. Image quality meets analysis standards and clinical data is complete.
4. Lymph nodes that were pathologically confirmed to be metastatic or non-metastatic at station 4 were selected

Exclusion Criteria:

1. Preoperative chemoradiotherapy or other treatment
2. Distant metastasis or other malignant tumors are present
3. Incomplete clinical data or image artifacts
4. No metastatic or non-metastatic lymph nodes were found at station 4

Outcomes:
Area under the Curve(AUC); Receiver operator characteristic curve(ROC)
Accuracy of an AI Model for Diabetic Retinopathy Screening in Real-life
Trial ID: NCT05704491
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-01-30
Completion date: 2025-12-31
Sponsor: West German Center of Diabetes and Health
Conditions: Diabetes Mellitus
Interventions: artificial intelligence (AI) algorithm of the MONA DR model
Locations: West German Center of Diabetes and Health
Countries: Germany
Enrollment: 100
Age range: 18 Years - None
Sex: ALL

Summary:
The increasing prevalence of diabetes mellitus represents a major health problem, especially since around 40% of diabetic patients develop diabetic retinopathy, which severely impairs vision and can lead to blindness. This development could be prevented by annual check-ups and timely referral for treatment. However, there are major differences in the quality of examinations and bottlenecks in examination appointments. A solution to the problem could be the use of artificial intelligence (AI), especially deep learning. Initial studies have shown that deep learning algorithms can be used successfully to detect diabetic retinopathy. However, it remains to be clarified whether the use of AI can achieve a sufficiently high level of accuracy in the detection of retinopathies. Therefore, in the present study, the positive predictive value (PPV), the negative predictive value (NPV), the sensitivity (SEN) and the specificity (SPEZ) of the AI algorithm 'MONA-DR-Model' in the detection of diabetic retinopathy should be measured. In addition, it is to be examined how well the classification into mild and severe retinopathy corresponds and how well this new examination method is accepted by the patients.

Eligibility criteria:
Inclusion Criteria:

* Diagnosis of diabetes mellitus
* Diabetes duration ≥ 5 years
* Age \> 18 years old
* Patient is able to give informed consent
* Fluent in written and spoken German, or interpreter present

Exclusion Criteria:

* History of laser treatment
* Contraindication to the fundus imaging systems used in the study

Outcomes:
PPV; NPV; SEN; SPEZ
Piloting of JUN to Enhance Self-Efficacy Pregnant Women
Trial ID: NCT06636110
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-03-03
Completion date: 2025-12-31
Sponsor: The University of Texas Health Science Center at San Antonio
Collaborators: The University of Texas at San Antonio
Conditions: Pregnancy Related; Pregnancy
Interventions: Jun mHealth app
Locations: University of Texas Health Science Center at San Antonio
Countries: United States
Enrollment: 50
Age range: 18 Years - 50 Years
Sex: FEMALE

Summary:
This research activity looks at the safety of an investigational intervention, a mobile health (mHealth) app called JUN, that uses artificial intelligence (AI) to track symptoms and give time-sensitive feedback to pregnant women.

JUN is an intervention being developed to provide education and assist with decision making during pregnancy.

The researchers hope to learn differences in how effectively the participant use of the app is amongst women with and without criminal justice oversight such as adult probation or parole.

Eligibility criteria:
Inclusion Criteria:

1. Pregnant female 18-50 years
2. Within first 6 months of pregnancy
3. Either on community supervision or without criminal system involvement
4. Located in Texas, Minnesota or Oregon

Exclusion Criteria:

1. Individuals who do not identify as women
2. Later pregnancy, beyond the first 6 months

Outcomes:
Self Rated Abilities for Health Practice (SRAHP); JUN mHealth app Timing; JUN mHealth app Frequency
Stanford Study of the Validation of the Pre-Interventional Preventive Risk Assessment (PIPRA) Tool In The At-Risk Orthopedic Surgery Population
Trial ID: NCT06567574
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-04-30
Completion date: 2024-06-30
Sponsor: Stanford University
Conditions: Delirium; Postoperative Delirium; Cognitive Impairment
Locations: Stanford Medical Center
Countries: United States
Enrollment: 250
Age range: 65 Years - None
Sex: ALL

Summary:
The purpose of this study is to determine the accuracy of an AI-based tool in the prediction of postoperative delirium (POD), in a population at moderately high risk of developing this syndrome (i.e., elderly orthopedic subjects). The population to be studied has already been enrolled in a parallel study regarding the likelihood of developing delirium, its relationship to the type of anesthetic, and the relationship between anesthetic type, development of delirium and risk for post-operative cognitive impairment (including risk for dementia) (Protocol ID#55169 \[David Drover-Principal investigator; José Maldonado-Co-investigator\]).

Eligibility criteria:
Inclusion Criteria:

* Surgical patients
* Greater than or equal to 65 years old
* ASA I, II or III
* Greater than 90 min of surgery
* Non-cardiac surgery

Exclusion Criteria:

* Dementia
* Alzheimer's
* Parkinson's
* Current Benzodiazepine use
* Alcoholism
* Previous surgery within the last 12 months or expected in the 12 months to come (except the expected surgery for which they are being enrolled).
* Color-blindness

Outcomes:
Number of subjects with postoperative delirium accurately predicted by the PIPRA Tool; Development of post-operative Delirium; Determination of Delirium Phenotype; Impact of delirium on post-operative cognitive impairment; Sensitivity and specificity of the PIPRA tool for detecting postoperative delirium.; Receiver operating characteristic (ROC) curve analysis of PIPRA tool