moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Enhancing the Effectiveness of Home-delivery Based Produce Prescription Program Implementation Strategies
Trial ID: NCT06114199
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-07-26
Completion date: 2025-11-25
Sponsor: The University of Texas Health Science Center, Houston
Collaborators: American Heart Association
Conditions: Nutritional Deficiency in Pregnancy
Interventions: Produce Prescription Program (PPRx) Standard; Smartphone App
Locations: The University of Texas Health Science Center at Houston
Countries: United States
Enrollment: 200
Age range: 18 Years - 44 Years
Sex: FEMALE

Summary:
Harris Health System is currently participating, along with two other partners, GoldiFresh Inc., and Brighter Bites, in a cross-sector collaborative home delivery-based Produce Prescription Program (PPRx) pilot initiative that provides consistent access to healthy food plus nutrition education to Medicaid-eligible pregnant women in Harris County, TX, deemed to be at high risk of adverse pregnancy or birth outcomes. The objectives of this study are two-fold: (a) to examine utilization of food basket contents and improvements in diet quality in this home delivery based PPRx program, and (b) to evaluate if and the extent to which utilization of food basket contents outcomes improves with support from an interactive AI-based conversational agent to convert food into healthy meals.

Eligibility criteria:
Inclusion Criteria:

* receiving prenatal care at high-risk obstetric clinics
* \<= 20 weeks medically-confirmed viable pregnancy
* designated as high risk \[Age\>=35 years, or obese or overweight (BMI\>=25.0 at pre pregnancy self-report), or prior history of pregnancy hypertension or gestational diabetes\]
* within the zip code delivery radius

Exclusion Criteria:

* women who were medically recommended bed rest through pregnancy
* substance users

Outcomes:
Change in consumption of home-delivered produce from baseline to 6 months; Change in usual consumption of fruits and vegetables from baseline to 6 months
The Observational Study of Cardiac and Pulmonary Ultrasound and Evaluation of Treatment of Severe Patients With Novel Coronavirus Pneumonia
Trial ID: NCT04314271
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-03-20
Completion date: 2020-05-31
Sponsor: Xiangya Hospital of Central South University
Conditions: Novel Coronavirus Pneumonia
Locations: Xiangya Hospital, Central South University
Countries: China
Enrollment: 80
Age range: 18 Years - None
Sex: ALL

Summary:
Complete the examination of cardio-pulmonary ultrasound in accordance with the a-ccue process of patients with novel coronavirus bedside. To summarize and analyze the characteristics of cardiopulmonary ultrasound in patients with novel coronavirus pneumonia, and assess the relationship between pulmonary ultrasound imaging score and National Early Warning Score(NEWS) and prognosis. Auto line B is a method which is based on artificial intelligence is used to calculate the lungs ultrasonic B line numbers reviewing the status of patients with lung, and also evaluate patients' lungs using the traditional artificial semi-quantitative method, to evaluate those two kinds of evaluation methods for the evaluation of patients with lung condition effects are consistent or not, and verify consistency of ultrasonic evaluation method and the way of CT evaluation.

Eligibility criteria:
Inclusion Criteria:

1. age \>18 years old
2. identify the patients infected with novel coronavirus pneumonia;
3. the patient completed CT examination;
4. sign the informed consent.

Exclusion Criteria:

1. age \< 18;
2. those who cannot complete cardiopulmonary ultrasound according to the a-ccue process;
3. the patient or his guardian requests to withdraw from the researcher;

Outcomes:
characteristics of cardiopulmonary ultrasound
Efficacy and Safety of the Artificial Intelligence-assisted Insulin Dose Adjustment System for Glycaemic Control in Patients With Type 2 Diabetes Mellitus in General Wards: a Multicentre, Single-blind, Randomised Controlled Study
Trial ID: NCT06319300
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-04-19
Completion date: 2026-05-20
Sponsor: Shanghai Zhongshan Hospital
Collaborators: Shanghai Fifth People's Hospital
Conditions: Diabetes Type 2
Interventions: AI-assisted insulin dose adjustment model; doctor's insulin dose adjustment
Locations: Zhongshan Hospital, Fudan University
Countries: China
Enrollment: 140
Age range: 18 Years - None
Sex: ALL

Summary:
Our study was a single-blind, randomised, controlled, multicentre study. The study was planned to include 140 patients admitted to the general ward for subcutaneous insulin therapy, who were randomly divided into two groups in the ratio of 1:1, one group with an artificial intelligence assisted insulin dosimetry system to adjust the insulin dose to control their blood glucose, and the other group with a physician instituted insulin dosimetry adjustments to control their blood glucose. The effectiveness and safety of the system was confirmed by comparing the glycaemic control and risk of adverse events between the two groups.

Eligibility criteria:
Inclusion Criteria:

* Diagnosis of type 2 diabetes mellitus \> 3 months
* Age≥18 years old
* Receiving glucose-lowering therapy for at least 90 days
* Blood glucose:7.8-22.2 mmol/L
* Length of proposed hospitalisation ≥5 days

Exclusion Criteria:

* Type 1 diabetes mellitus, other special types of diabetes mellitus.
* BG\>22.2 mmol/L, or acute complications of diabetes, such as diabetic ketoacidosis, diabetic hyperosmolar state.
* History of severe or repeated hypoglycaemia
* BMI≥45 kg/m2
* Pregnant and lactating women
* Clinically relevant liver disease (established cirrhosis and portal hypertension);
* Presence of severe renal disease (serum creatinine ≥3.0 mg/dL or estimated glomerular filtration rate \<30 ml/min/1.73 m2);
* Severe cardiac insufficiency;
* Patients on cortisol-based hormone therapy (equivalent to a prednisone dose \>5 mg/day);
* Psychiatric abnormalities or impaired cognitive function;
* Patients with severe oedema, infection, or peripheral blood circulation disorder;
* Patients with severe illness or patients to be transferred to ICU for treatment;
* Surgery of the heart or abdomen that may have a significant impact on the test is planned during hospitalisation.

Outcomes:
time in target range (3.9-10.0 mmol/L )
Smart Bone: An Intelligent Fracture Risk Capture and Management System
Trial ID: NCT04384211
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2019-06-01
Completion date: 2021-05-31
Sponsor: Taipei Medical University WanFang Hospital
Collaborators: Quanta Computer Inc.
Conditions: Compression Fracture
Interventions: Computer Assisted Detection Software For Vertebral Fractures
Locations: Taipei Medical University WanFang Hospital
Countries: Taiwan
Enrollment: 1500
Age range: 50 Years - 90 Years
Sex: ALL

Summary:
This project aims to develop and validate an automatic detection and classification system for vertebral compression fractures on computer tomography (CT) images using an artificial intelligence (AI) system (named Smart Bone) by Quanta.

Eligibility criteria:
Inclusion Criteria:

* Cases with CT examinations acquired between 2010.01-2018.09
* Cases with CT examinations performed with one of the following protocol: whole body, abdomen, and spine
* Cases must be \>/= 50 years of age
* Cases with reports from CT examinations ditched as positive or negative compression fractures within a search range from T12 to L5 vertebrae.
* CT images with raw data that are allowed to be reconstructed in axial view with a slice thickness of 1.3 mm
* CT images with raw data that are allowed to be reconstructed in sagittal view with a slice thickness of 2.5 mm

Exclusion Criteria:

* CT images with imaging artifacts, foreign bodies, or implants
* Cases with comorbid conditions, such as infection, cancer metastasis, chronic osteomyelitis, or other nonosteoporotic compression fracture

Outcomes:
Concordance rate
Development of a Predictive Model for Gastric Cancer Peritoneal Metastasis and Cachexia Using BUB1 and Radiopathomics Data With Deep Learning
Trial ID: NCT06858644
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-03-01
Completion date: 2027-03-01
Sponsor: Qun Zhao
Conditions: Gastric (Stomach) Cancer
Interventions: BUB1-Integrated Deep Learning Model for Gastric Cancer Metastasis and Cachexia Prediction
Enrollment: 500
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
This clinical trial aims to develop a predictive model for gastric cancer (GC) peritoneal metastasis and cachexia by integrating BUB1 gene data with radiological and pathological data using advanced deep learning techniques. The study will focus on utilizing imaging genomics (radiomics) and histopathological data to identify early biomarkers for peritoneal metastasis and cachexia in GC patients. By leveraging deep learning algorithms, the project seeks to improve the accuracy and reliability of predictions, enabling earlier intervention and personalized treatment strategies. The ultimate goal is to enhance clinical decision-making and prognosis prediction in GC patients with peritoneal metastasis and cachexia.

Eligibility criteria:
Inclusion Criteria:

Adults aged 18-75 years diagnosed with gastric cancer (GC) at any stage. Histopathologically confirmed GC with available radiological (CT/MRI) and pathological data (biopsy samples).

Patients with or at risk of peritoneal metastasis and/or cachexia, as determined by clinical assessment and imaging.

Ability to provide informed consent and comply with study protocols. Willingness to undergo regular follow-up imaging and clinical evaluation for the duration of the study.

Exclusion Criteria:

Patients with other primary cancers or serious comorbidities (e.g., severe cardiovascular disease, uncontrolled diabetes).

Pregnant or breastfeeding women. Patients with contraindications to MRI or CT imaging. Those with insufficient clinical data (e.g., missing radiopathological information) for model training.

Patients who are unable or unwilling to comply with the study protocol, including follow-up visits and evaluations.

Outcomes:
Predictive Accuracy of the BUB1-Integrated Deep Learning Model for Gastric Cancer Peritoneal Metastasis and Cachexia
A Single-center Study of an AI-assisted Quantitative Bowel Readiness Assessment System in Predicting the Missed Detection Rate of Adenomas Under Colonoscopy
Trial ID: NCT05145712
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-07-21
Completion date: 2022-03-20
Sponsor: Renmin Hospital of Wuhan University
Conditions: Self Control; Prospective; Interventional
Interventions: Bowel readiness
Locations: Renmin Hospital of Wuhan University
Countries: China
Enrollment: 263
Age range: 45 Years - None
Sex: ALL

Summary:
To verify the correlation between the proposed artificial intelligence based bowel preparation assessment system and the missed detection rate of adenomas, and to evaluate whether the system can effectively assist doctors in identifying patients who need to be re-examined by colonoscopy due to poor intestinal cleanliness.

Eligibility criteria:
Inclusion Criteria:

At least 4 operators were included in this study, and the operating years of colonoscopy were ≥ 5 years, and the number of independent operating cases was ≥ 3000.

Subjects who meet all of the following specific criteria will be considered for participation in the study:

1. Male or female with age ≥ 45 years inclusive;
2. Able to read, understand and sign informed consent;
3. The investigator believes that the subjects can understand the process of the clinical study, are willing and able to complete all study procedures and follow-up visits, and cooperate with the study procedures.

Exclusion Criteria:

Subjects who meet any of the following specific criteria will be refused to participate in the study:

1. have a history of drug or alcohol abuse or psychological disorders within the last 5 years;
2. Pregnant or lactating women;
3. Patients with known multiple polyp syndrome;
4. patients with known inflammatory bowel disease;
5. known intestinal stenosis or space-occupying tumor;
6. known colon obstruction or perforation;
7. patients with a history of colorectal surgery;
8. Patients with previous history of allergy to pre-used spasmolysis;
9. Unable to perform biopsy and polyp removal due to coagulation disorders or oral anticoagulants;
10. High risk diseases or other special conditions that the investigator considers the subject unsuitable for participation in the clinical trial.

Outcomes:
Missed detection rate of ≥5mm adenoma
Application of Artificial Intelligence Deep Learning to the Correlation Between Cardiovascular Disease and Individualized Differences
Trial ID: NCT03877614
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2018-08-28
Completion date: 2022-06
Sponsor: National Cheng-Kung University Hospital
Conditions: Cardiovascular Diseases
Interventions: ASCVD risk score
Locations: Department of Internal Medicine, National Cheng Kung University Hospital
Countries: Taiwan
Enrollment: 5000
Age range: 18 Years - None
Sex: ALL

Summary:
An association study with large database from electronic medical record system, images, outcome analysis and genetic single nucleotide polymorphism variations by machine learning and artificial intelligence methods in a Taiwanese and Chinese medical center based population

Eligibility criteria:
Inclusion Criteria:

* Patients' selection criteria and enrollment plan:

We will enroll subjects from either cardiovascular clinics or inpatients from the National Cheng Kung University Hospital from 2018 to 2021 after the signature of inform consent from patients and their families. The major enrollment criteria include one of the flowing diseases or conditions:

A. Coronary artery disease:

1. History of myocardial infarction
2. Coronary artery disease with computer tomography angiography image study with at least one vessel luminal stenosis \>70%
3. Coronary artery stents implantation by hospital-based image database
4. Thallium-201 scan positive/treadmill test positive with additional 2 risk factors, including

   1. Diabetes mellitus
   2. Hypertension
   3. Dyslipidemia
   4. Family history of sudden death, coronary bypass surgery, cerebral vascular attacks (CVA), premature myocardial infarction
   5. Smoking behaviors

B. Congestive heart failure with reduced ejection fraction

1\. Echocardiography left ventricular ejection fraction \<40%

C. Hypertrophic cardiomyopathy:

1. Left ventricle interventricular septum（IVS) \>15 mm
2. Left ventricle mass index\> 200gm
3. Apical hypertrophy noted on the report with 4 chamber view

D. Atrial fibrillation

1. Recorded by Holter continuous EKG
2. Recorded by standard 12 leads complete EKG

E. Pulmonary hypertension

1. Echo with systolic pulmonary pressure (sysPAP)\> 40 mmHg
2. Diagnosis of idiopathic pulmonary hypertension
3. Under pulmonary hypertension medication

F. Fabry's disease

1. α-Galactosidase (a-GAL) enzyme deficiency
2. Genetic disorder

G. Patient with only risk factors (\<3 risk factors), recognized as the comparison group (\>500 cases)

1. Diabetes mellitus
2. Hypertension
3. Dyslipidemia
4. Family history of sudden death, coronary bypass surgery, cerebral vascular attacks, premature myocardial infarction
5. Smoking behavior

Exclusion Criteria:

* Patients unwilling to be enrolled
* Concentration of DNA collection was inadequate after 3 times of collection

Outcomes:
Major cardiovascular events
Home Monitoring for the Detection of Subclinical Atrial Fibrillation in Hypertensive Patients at Moderate to High Risk of Developing Arrhythmia. BP/AF MODE Study.
Trial ID: NCT07058831
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-03-01
Completion date: 2025-12-30
Sponsor: Cardiovascular Research Center Rosario British Sanatorium
Conditions: Asymptomatic Atrial Fibrillation
Interventions: Home ECG monitoring
Locations: British Sanatorium Cardiovascular Research Center
Countries: Argentina
Enrollment: 499
Age range: 65 Years - None
Sex: ALL

Summary:
Atrial fibrillation is a highly prevalent and incidental arrhythmia, often asymptomatic, and frequently detected incidentally or in association with a stroke. Subclinical atrial fibrillation increases cardioembolic risk, highlighting the need for timely diagnosis.

New wireless devices capable of recording heart rhythm, combined with innovative artificial intelligence tools, could be useful in the prediction and detection of this arrhythmia.

Objective: to determine the usefulness of home blood pressure and heart rhythm monitoring strategy in the detection of subclinical atrial fibrillation.

Methods: observational, cohort, prospective, multicenter study involving 25 researchers from six Latin American countries. Home blood pressure monitoring and single-lead electrocardiogram recording were performed in a population at moderate to high risk of developing atrial fibrillation. A minimum of twenty 30-second electrocardiographic and blood pressure recordings over 7 days using an Omron Complete Hem-7530 T ECG device will be uploaded from a mobile phone app and then sent to a database for analysis.

Conclusions: the results of this study can provide a simple and accessible home monitoring system for detecting subclinical atrial fibrillation and for optimizing the predictive capacity of arrhythmia risk scores through deep learning.

Eligibility criteria:
Inclusion Criteria:

* 1\. Hypertensive patients of both sexes over 75 years of age. 2. Hypertensive patients between 65 and 75 years of age are associated with one of the following risk factors:

  1. Obesity
  2. Type 2 diabetes
  3. Obstructive sleep apnea syndrome
  4. Electrocardiogram showing left ventricular hypertrophy.
  5. Left ventricular hypertrophy on echocardiogram.
  6. Left atrial dilation.
  7. Coronary artery disease
  8. Chronic kidney disease
  9. Heart failure with ejection fraction \> 50%
  10. Alcohol intake
  11. Frequent supraventricular ectopic beats

Exclusion Criteria:

* 1\. History of any type of atrial fibrillation. 2. Receiving pharmacological treatments with potential impact on heart rhythm. 3. History of potentially malignant ventricular arrhythmias. 4. Uncontrolled hypothyroidism or hyperthyroidism. 5. Significant chronic obstructive pulmonary disease.

Outcomes:
new atrial fibrillation; The absolute numbers of patients with new atrial fibrillation; The percentage of patients with new atrial fibrillation
Endoscopic Ultrasound (EUS) Assessment of Normal Mediastinal and Abdominal Organ/Anatomic Strictures Using a Novel Developed Artificial Intelligence Model
Trial ID: NCT05151939
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-10-01
Completion date: 2022-06-30
Sponsor: Instituto Ecuatoriano de Enfermedades Digestivas
Conditions: Abdomen; Mediastinum; Anatomic Abnormality; Strictures
Interventions: Identification or discharge visualization of mediastinal and abdominal organ/anatomic strictures through Endoscopic ultrasound (EUS) videos by an expert endoscopist; Recognition of mediastinal and abdominal organ/anatomic strictures through Endoscopic ultrasound (EUS) videos using artificial intelligence (AI)
Locations: Ecuadorian Institute of Digestive Diseases
Countries: Ecuador
Enrollment: 60
Age range: 18 Years - 79 Years
Sex: ALL

Summary:
Therefore, a high number of procedures is necessary to achieve EUS competency, but interobserver agreement still varies widely. Artificial intelligence (AI) aided recognition of anatomical structures may improve the training process and inter-observer agreement. Robles-Medranda et al. developed an AI model that recognizes normal anatomical structures during linear and radial EUS evaluations. We pursue to design an external validation of our developed AI model, considering an endoscopist expert as the gold standard.

Eligibility criteria:
Inclusion Criteria:

* Patients with no history of the thorax and abdominal abnormalities confirmed through an imaging test requested for healthcare purposes in the last twelve months (e.g., thorax X-ray and abdominal ultrasound or thorax and abdominal CT)
* Patients who undergo EUS assessment due to chronic dyspepsia.

Exclusion Criteria:

* Morphological alteration on at least one mediastinal and abdominal organ/anatomic strictures documented through any imaging test or EUS.
* Uncontrolled coagulopathy, kidney/liver failure, or any comorbidity with a meaningful impact on cardiac risk assessment (NHYA III/IV);
* Refuse to participate in the study or to sign corresponding informed consent.

Outcomes:
Overall accuracy of Endoscopic ultrasound (EUS) artificial intelligence (AI) model for identifying normal mediastinal and abdominal organ/anatomic strictures
Clinic Waiting Room-based Pilot of Swahili Language Artificial Intelligence-driven Symptom Assessments in Primary Health Care Facilities, Dar es Salaam, Tanzania
Trial ID: NCT04958577
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-08-03
Completion date: 2022-06-01
Sponsor: Ada Health GmbH
Collaborators: Muhimbili University of Health and Allied Sciences
Conditions: Abdominal Pain/Gastrointestinal Issues; Lower Respiratory System Conditions; Upper Respiratory System Conditions; Mental Health Conditions; Ophthalmology Conditions; Orthopedic Conditions; Cardiovascular System Conditions; Genitourinary System Conditions; ENT Conditions; Dermatology Conditions; Gynecology/Obstetrics Conditions; Neurological Conditions
Interventions: Ada Waiting Room App (WRA) and Ada Diagnostic Support Application (HDA)
Locations: Mbagala Rangi Tatu Hospital
Countries: Tanzania
Enrollment: 109
Age range: 2 Months - None
Sex: ALL

Summary:
This study will evaluate the accuracy of the condition suggestions and urgency advice of the Swahili language Ada symptom assessment application (SAA), when symptoms are input by a lay-person user and a medical professional; these SAA results will then be compared to the condition suggestions and urgency advice of different tiers of doctors and a "gold standard" created by a panel.

Eligibility criteria:
Inclusion Criteria:

* All patients who enter the clinic and that are willing/able to provide consent will be included, except those who fall under certain conditions listed below

Exclusion Criteria:

* Patients with severe injury/illness requiring immediate treatment
* Patients with traumatic injury (many of these patients require minimal anamnesis, and it is not rational to include them in a pilot study)
* Patients incapable of completing a health assessment (e.g. due to illiteracy, mental impairment or inebriation or other incapacity).

Outcomes:
Accuracy and Comprehensiveness measures of Ada WRA and HDA
Accuracy of Artificial Intelligence Compared to Conventional Methods in Digital Smile Designing
Trial ID: NCT06666907
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-12-01
Completion date: 2025-05-01
Sponsor: Cairo University
Conditions: Esthetics, Dental
Interventions: Smilefy; EXOCAD
Locations: Faculty of Dentistry
Countries: Egypt
Enrollment: 10
Age range: 18 Years - 30 Years
Sex: ALL

Summary:
The study aims to assess the accuracy and patient satisfaction of smile designs based on artificial intelligence versus conventional DSD.

Eligibility criteria:
Inclusion Criteria:

* Patients with esthetic problems that require digital smile design.
* Patient's age ranging from 19-30
* Good oral hygiene.
* Patients who have stable occlusion.

Exclusion Criteria:

* Poor oral hygiene.
* Patients with high caries or high plaque index.
* Patients with periodontal problems.
* Heavy bruxism habit or presence of any parafunctional habits.
* Pregnant or lactating women.
* Participating in another trial.

Outcomes:
Accuracy