moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
The Clinical Outcomes and Artificial Intelligence Prediction Model of Ultrasound-guided Thermal Ablation for the Treatment of Benign Thyroid Nodules：A Multicenter Prospective Study
Trial ID: NCT06340945
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-04-01
Completion date: 2029-12-31
Sponsor: Chinese PLA General Hospital
Conditions: Thyroid Nodules
Interventions: ultrasound-guided thermal ablation
Locations: Chinese PLA General Hospital
Countries: China
Enrollment: 1500
Age range: 18 Years - None
Sex: ALL

Summary:
1. To evaluate the clinical outcomes of ultrasound-guided thermal ablationfor the treatment of benign thyroid nodules;
2. To develop and validate a artificial intelligence model to predict the outcomes of ultrasound-guided thermal ablation in the treatment of benign thyroid nodules

Eligibility criteria:
Inclusion Criteria:

* \[1\]confirmation of benign nodule status on two separate fine- needle aspiration (FNA) or core-needle biopsy (CNB) \[2\]no suspicious malignant features on ultrasound examination \[3\]report of cosmetic and/ or symptomatic problems or concern of nodules growing rapidly or malignant transformation \[4\]refusal or ineligibility for surgery \[5\]follow-up time ≥6months

Exclusion Criteria:

1. follicular neoplasm or malignancy findings on biopsy
2. nodules with benign result on biopsy had suspicious of malignancy in US, including marked hypoechoic, ill-defined margins, taller-than-wide shape or microcalcifications
3. patients with cystic nodules
4. patients with contra-lateral vocal cord par- alysis
5. previous radiation to the head and neck
6. ; follow- up time less than 6 months

Outcomes:
volume reduction rate; rate of nodule regrowth
Establishment of a Cohort of Maternal Vascular Malperfusion-Related Fetal Growth Restriction (MVM-FGR) Based on an Etiology-Oriented Diagnostic Pathway: Artificial Intelligence-Assisted Multiparametric Ultrasound Prediction of Pregnancy Outcomes
Trial ID: NCT07473739
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-01-01
Completion date: 2028-11-30
Sponsor: Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
Conditions: Fetal Growth Restriction (FGR)
Interventions: new ultrasound index and MRI
Locations: Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine Shanghai, Shanghai, China
Countries: China
Enrollment: 300
Age range: 20 Years - 43 Years
Sex: FEMALE

Summary:
The goal of this prospective cohort study is to enroll pregnancies complicated by placental-related fetal growth restriction (FGR) and to develop predictive models for adverse short- and long-term outcomes. This will be achieved by collecting novel intrauterine monitoring indicators along the fetal brain-placenta-heart axis, combined with conventional fetal surveillance parameters, in order to improve risk stratification and guide clinical management, ultimately improving pregnancy outcomes.

The study will include pregnant women with singleton pregnancies complicated by isolated early-onset placental insufficiency-related FGR, preferably those with abnormal umbilical artery Doppler findings, who elect to continue the pregnancy.

The main question it aims to answer is:

• Whether a predictive model integrating novel intrauterine monitoring indicators along the fetal brain-placenta-heart axis with conventional monitoring parameters can accurately predict perinatal and neonatal adverse outcomes in pregnancies complicated by placental-related FGR.

Eligibility criteria:
Inclusion Criteria:

* Singleton pregnancy.
* Isolated early-onset placental insufficiency-related fetal growth restriction (FGR), with priority given to cases with abnormal umbilical artery Doppler flow.
* Pregnancies in which expectant management is continued.

Exclusion Criteria:

* Multiple pregnancy complicated by selective fetal growth restriction (sFGR).
* FGR caused by fetal structural anomalies, genetic abnormalities, or intrauterine infection.
* Twin pregnancy with intrauterine fetal demise (IUFD) of one fetus.

Outcomes:
pregnancy outcome
Hybrid Deep Learning Models Based on Multimodal CEUS and Enhanced MRI Can Optimize Treatment Decisions for Early-stage Hepatocellular Carcinoma
Trial ID: NCT07582419
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-04-30
Completion date: 2027-10-30
Sponsor: The First Hospital of Jilin University
Collaborators: Chinese PLA General Hospital
Conditions: Hepatocellular Carcinoma (HCC)
Interventions: surgery; ablation
Locations: The First Hospital of jilin University
Countries: China
Enrollment: 1424
Age range: 18 Years - 85 Years
Sex: ALL

Summary:
This study aims to address the issue of a lack of individualized basis for selecting liver resection (LH) or microwave ablation (MWA) in early-stage hepatocellular carcinoma (HCC) patients to reduce the early recurrence rate (≤2 years). Given that existing machine learning-based recurrence prediction studies have failed to guide the optimal treatment plan selection, and that multidisciplinary consultations rely on guidelines (universality) and experience (subjectivity) which have their limitations, we propose to utilize artificial intelligence (AI), specifically the advantages of multimodal deep learning technology (which outperforms traditional machine learning by integrating complementary information to provide more accurate predictions), to establish a hybrid deep learning model that integrates contrast-enhanced ultrasound (CEUS) and enhanced magnetic resonance imaging (MRI) features. This model will predict the probability of early recurrence (ER≤2 years) in patients and, based on this, recommend LH or MWA as the optimal first treatment option for newly diagnosed early HCC patients to optimize individualized treatment decisions.

Eligibility criteria:
Inclusion Criteria:

1. Preoperative enhanced imaging examination or pathological diagnosis is HCC;
2. CNLC Stage I, IIa, Child-Pugh Class A/B;
3. A single tumor with a diameter ≤ 5 cm or 2-3 tumors, with the maximum diameter ≤ 3 cm;
4. Perform liver resection surgery or MWA surgery treatment;
5. MRI and/or CEUS examinations performed within one month before surgery

Exclusion Criteria:

1：There is already extrahepatic metastasis or the presence of other malignant tumors;

2: History of other treatments prior to surgery;

3: Incomplete preoperative ultrasound contrast and/or MRI imaging data, with images missing or unclear;

4: Missing postoperative follow-up data

Outcomes:
Early recurrence status (binary: recurrence vs. non-recurrence within 2 years)
Research on a New Intelligent Mobile Screening and Diagnosis Pattern for Ocular Diseases
Trial ID: NCT07003165
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-07
Completion date: 2029-05
Sponsor: Zhongshan Ophthalmic Center, Sun Yat-sen University
Conditions: Ophthalmic Diseases (Specific Types Not Restricted); Cataract; Refraction Error; Diabetic Retinopathy (DR); Age Related Macular Degeneration (ARMD)
Enrollment: 15000
Age range: 7 Years - None
Sex: ALL

Summary:
The global distribution of primary ophthalmic medical resources is uneven, and the traditional eye disease screening model has problems such as low efficiency, high cost and limited coverage. With the development of artificial intelligence and other technologies, it provides technical support for the construction of intelligent mobile screening model for eye diseases. The investigator's team has developed the 5G intelligent ophthalmic vehicle and served tens of thousands of people in 108 cities nationwide, initially verifying the feasibility of the new intelligent mobile screening model. However, the application effect, acceptance and influencing factors of this model in different regions are not clear, and there is a lack of economic benefit analysis based on real-world data. In this study, the investigators will conduct a cross-sectional study of large-scale population screening for blinding eye diseases in grassroots areas through the smart mobile screening model, focusing on the screening effectiveness and cost-effectiveness of the smart mobile screening model, integrating real-world multimodal eye health data, developing multiple smart screening analysis models, and exploring its adaptability and direction of improvement in grassroots areas.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 7 years
* Voluntary informed consent

Exclusion Criteria:

-Inability to complete the required examinations with the help of others due to old age, infirmity, poor general condition, etc.

Outcomes:
Cost-effectiveness
The Reliability of the Computer Aided Multi-Spectral Screening System In the Diagnosis of Covid-19
Trial ID: NCT04860895
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-02-11
Completion date: 2021-05-15
Sponsor: Fable Biyoteknoloji San ve Tic A.S
Conditions: Covid19
Interventions: AP-23
Locations: Sancaktepe Şehit Prof.Dr. İlhan Varank Training and Research Hospital
Countries: Turkey (Türkiye)
Enrollment: 4400
Age range: 12 Years - 95 Years
Sex: ALL

Summary:
The aim of this research is to use the Multi-Spectral method to measure the biomolecular fingerprint of the virus directly instead of the virus's RNA (RT-PCR) or the body's immune response (antibody) measurement methods to diagnose the SARS-COV-2 virus, thereby determining whether COVID-19 can be diagnosed faster, cheaper and without requiring a medical laboratory environment.

Eligibility criteria:
Inclusion Criteria:

All people who applied to hospitals with suspicion of COVID-19

Exclusion Criteria:

Person who cannot give nasopharyngeal samples

Outcomes:
Assessment of AI's Learning Performance:; Evaluation of Artificial Intelligence's Test Performance)
Based on Multimodal Endoscopy and Weakly Supervised Deep Learning-Early Esophageal Squamous Cell Carcinoma Infiltration Depth Precise Prediction Study
Trial ID: NCT06412419
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-05-15
Completion date: 2024-10-30
Sponsor: Changhai Hospital
Collaborators: West China Hospital; Shandong Provincial Hospital; The First Affiliated Hospital of Soochow University; The First Affiliated Hospital of Henan University of Science and Technology; The First Affilated Hospital of the Medical College, Shihezi University
Conditions: Esophageal Neoplasms Malignant
Interventions: Magnifying Endoscopy and Endoscopic Ultrasonography
Locations: Changhai hospital
Countries: China
Enrollment: 450
Age range: 18 Years - None
Sex: ALL

Summary:
The objective of this project is to pioneer a novel protocol for the adjunctive screening of early-stage esophageal cancer and its precancerous lesions. The anticipated outcomes include simplifying the training process for users, shortening the duration of examinations, and achieving a more precise assessment of the extent of esophageal cancer invasion than what is currently possible with ultrasound technology. This research endeavors to harness the synergy of endoscopic ultrasound (EUS) and Magnifying endoscopy, augmented by the pattern recognition and correlation capabilities of artificial intelligence (AI), to detect early esophageal squamous cell carcinoma and its invasiveness, along with high-grade intraepithelial neoplasia. The overarching goal is to ascertain the potential and significance of this approach in the early detection of esophageal cancer.

The project's primary goals are to develop three distinct AI-assisted diagnostic systems:

An AI-driven electronic endoscopic diagnosis system designed to autonomously identify lesions.

An AI-based EUS diagnostic system capable of automatically delineating the affected areas.

A multimodal diagnostic framework that integrates electronic endoscopy with EUS to enhance diagnostic accuracy and efficiency.

Eligibility criteria:
Inclusion Criteria:

Patients requiring magnifying endoscopy and endoscopic ultrasonography. Individuals of either sex, aged 18 years or older.

Exclusion Criteria:

Inability to complete esophageal electronic endoscopy. Absence of biopsy or surgery, resulting in unobtainable pathological results. Patients who have undergone endoscopic lesion destruction or piecemeal resection, preventing the acquisition of an en bloc resection sample.

Patients with significant endoscopic, imaging, or pathological evidence of advanced esophageal cancer.

Patients presenting with marked esophageal stenosis or dilatation. Individuals with a history of other malignancies. Patients who have received neoadjuvant radiotherapy. Patients who declined to participate in the study and did not provide informed consent.

Outcomes:
Performance of models to diagnose low-grade intraepithelial neoplasia, high-grade intraepithelial neoplasia, and superficial esophageal squamous carcinoma
Validity of an AI-Supported Whole-Body Electrical Stimulation Device for Spinal Pain Assessment: A Comparison With Self-Reported Pain Scores
Trial ID: NCT07244237
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2025-11-15
Completion date: 2026-01-15
Sponsor: Amasya University
Conditions: Chronic Back Pain
Locations: AMASYAŞAM Sağlıklı Yaşam Merkezi
Countries: Turkey (Türkiye)
Enrollment: 78
Age range: 18 Years - 60 Years
Sex: ALL

Summary:
The goal of this observational study is to investigate the correlation between self-reported pain and the measurements obtained using an artificial intelligence-assisted whole-body electrical stimulation device (StimaWELL 120MTRS system) in individuals with chronic back pain. The main question it aims to answer is: Is there any correlation between the pain intensity assessed with StimaWELL 120MTRS system, dolorimeter and self-report in adults with chronic back pain? The patients who have back pain for the duration longer than 3 months and have been eximaned by a medical doctor will be assessed with StimaWELL 120MTRS system, dolorimeter, and will be asked to rate their pain intensity in Numerical Pain Rating Scale.

Eligibility criteria:
Inclusion Criteria:

* Experiencing back pain for more than three months
* Experiencing recurrent back pain over the last week
* Not having used analgesic or muscle relaxant medications in the last week
* Aged between 18 and 60 years
* Diagnosed with chronic low back pain by a medical doctor
* Literate and able to cooperate with study procedures
* Willing to participate in the study

Exclusion Criteria:

* Having neurological, inflammatory, radiculopathy, vertebral fracture, or similar conditions
* Experiencing acute-onset pain
* Being pregnant or at risk of pregnancy
* Having a psychiatric diagnosis and currently taking medication
* Having a skin disease or lesion in the area where electrodes will be applied
* Currently undergoing physical therapy
* Having vestibular, auditory, or cognitive impairments
* Possessing an electronic implant
* Having a cardiac arrhythmia
* Having a coronary or carotid stent
* Being diagnosed with epilepsy
* Having severe osteoporosis of the spine

Outcomes:
Pain intensity measured by the StimaWELL 120MTRS system; Pressure pain threshold; Patient-reported pain
MIRA Clinical Learning Environment (MIRACLE): Lung
Trial ID: NCT05689437
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-01-01
Completion date: 2023-12-31
Sponsor: University Health Network, Toronto
Collaborators: University of Toronto
Conditions: Lung Cancer
Interventions: Application of ILD prediction machine learning model to planning imaging; Routine, automatic presentation of ILD risk level for evaluation by the clinician.; Application of SGR machine learning model to diagnostic and planning imaging; Routine estimation of tumor specific growth rate (SGR) for lesions being considered for radiation therapy presented to clinician.; Application of CBCT machine learning model to on-treatment imaging; Routine monitoring of lung density changes during the course of treatment presented to clinician.
Locations: Princess Margaret Hospital
Countries: Canada
Enrollment: 1000
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this quality improvement (QI) study is to develop automated clinical pipelines to implement machine learning models in the care pathway of lung cancer patients. The main questions it aims to answer are:

* Can model-prompted risk classifications be incorporated into clinician workflows to enable informed clinical decision-making?
* What are clinicians' perceptions of the information from model outputs, and do they change their decision about data already available to them as a result of the model-prompted risk classification (i.e., to re-review or further assess patients identified by the models as being higher risk)?

Participating radiation oncologists will receive the risk prediction from the model and be asked to complete a survey to give feedback on how they used the prediction in their decision-making.

Eligibility criteria:
Inclusion Criteria:

* Diagnosed with lung cancer stage I-IV and planned for treatment with radiotherapy at Princess Margaret hospital. The three aims of this project have specific inclusion criteria as follows.
* Aim 1 ILD: All lung cancer patients receiving RT.
* Aim 2 SGR: Node negative lung cancer patients receiving stereotactic body RT.
* Aim 3 CBCT: Node positive lung cancer patients receiving standard RT.

Exclusion Criteria:

* No exclusion criteria

Outcomes:
Rates of true positive diagnosis of ILD increase with high/low patient risk predictions being made available to clinicians.; Previously difficult-to-assess information are made available during the clinical workflow as an easily accessible information source available to clinicians; Radiation oncologists use predictions provided from the model to support their clinical decision-making.
The Use of Artificial Intelligence Real-time Feedback to Improve Compliance to a Standardised Protocol and Procedural Quality During Oesophagogastroduodenoscopy (OGD)
Trial ID: NCT04883567
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-05-01
Completion date: 2022-06-30
Sponsor: Chinese University of Hong Kong
Conditions: Dyspepsia; Anemia; Acid Reflux; Dysphagia
Interventions: The use of artificial intelligence system Cerebro
Locations: The Chinese University of Hong Kong
Countries: Hong Kong
Enrollment: 210
Age range: 18 Years - None
Sex: ALL

Summary:
Early and accurate recognition of gastrointestinal lesions has implications for public health. The quality of oesophagogastroduodenoscopy (OGD) has great impact on the detection of oesophageal and gastric malignancies. An AI system, Cerebro, has been developed as a real-time recognition of a pre-defined 28 locations. This system would alert the endoscopist if the sites were not inspected with adequate time. The aim of the study is to investigate the use of AI in endoscopists-in-training during OGD to provide a real-time feedback for ensuring compliance to a standardized protocol for examination. The hypothesis is that the use of AI system Cerebro will significantly improve the compliance to the protocol and thereby reduce the blind spot rates of OGD, ensure adequate inspection time and ensure adequate photodocumentation.

Eligibility criteria:
Inclusion Criteria:

1. All patients aged \>= 18 years old
2. Undergoing diagnostic OGD for evaluation of their symptoms in the Prince of Wales Hospital will be included in the study.

Exclusion Criteria:

1. Patients in which a full endoscopic examination is not required including patients with a specific diagnostic purpose without the need for a full evaluation, follow-up endoscopy within 8 weeks for a previously diagnosed disease or condition,
2. Patients' condition requiring therapeutic endoscopy,
3. Early termination of endoscopy due to patient intolerance, presence of large amount of food residue, presence of mechanical obstruction or for reasons of safety,
4. Patients with altered anatomy such as history of esophagectomy, gastrectomy, gastric bypass surgery, or Whipple's operation,
5. Patient who are not suitable for EGD examination (such as acute peritonitis with suspected perforated bowel)
6. Pregnant females
7. Patients who refuse to participate in the study
8. Patients who are unfit for consent.

Outcomes:
The number of sites inspected during OGD
A Multi-center Registry for Clinical Information and 99mTc-pyrophosphate Scintigraphy in Patients With Suspected Cardiac Amyloidosis in Taiwan
Trial ID: NCT05593679
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-11-01
Completion date: 2027-12-31
Sponsor: Far Eastern Memorial Hospital
Conditions: Heart Failure; Cardiac Amyloidosis
Interventions: Tc-99m PYP Cardiac scan
Locations: Far Eastern Memorial Hospital
Countries: Taiwan
Enrollment: 1000
Age range: 20 Years - None
Sex: ALL

Summary:
Amyloid deposition in the heart is called cardiac amyloidosis (CA); 95% is immunoglobulin light chain amyloidosis (AL) and transthyretin amyloidosis (ATTR). Hereditary (ATTRm) or wild-type (ATTRwt) depends on whether the ATTRm gene is mutated or not. The most common mutation in Taiwan is A97S, 80% have left ventricular hypertrophy. The good prognosis depends on early diagnosis and correct treatment strategy.

Bone-avid tracers such as 99mTc-PYP/DPD/HMDP could detect CA. The mechanism is not clear yet, which may be related to the microcalcification. AL amyloidosis is mostly between visual score grade 0-2, and ATTR-CM is usually grade≥2 on PYP scan, or heart to contralateral (H/CL) ratio, and it might replace invasive myocardial biopsy. However, there are no large-scale clinical studies, lack of standardization data, and limited information in comparison between clinical and imaging parameters.

The project will enroll patients with suspected or diagnosed with CA according to CA diagnostic algorithm. Clinical data and image parameters are collected and compared. The project aims to set up prediction models based on the multi-parameters of PYP scan using artificial intelligence technology, including imaging registration and alignment technology, and standardization. The investigators further use the key cardiovascular data elements and imaging-derived database using model training network to extract image features to develop the diagnostic and prognostic prediction models, which are expected to validate the clinical significance and improve patient-centric performance and efficient clinical decision making.

Eligibility criteria:
Inclusion Criteria:

1. ≥ 20 years of age
2. Documented diagnosis or r/o of ATTR amyloidosis

Exclusion Criteria:

1. Pregnant or nursing (lactating) women.
2. Important systemic diseases (except heart disease or neurological diseases) such as liver cirrhosis, active malignant tumors, etc., with a life expectancy of less than 6 months.
3. Patient who refused clinical follow up.

Outcomes:
Death