moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Diagnostic Performance of a Convolutional Neural Network for Diminutive Colorectal Polyp Recognition. A Multicentre, Prospective Observational Study
Trial ID: NCT03822390
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2018-10-16
Completion date: 2021-10-16
Sponsor: Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
Collaborators: Bergman Clinics; Frisius Medisch Centrum
Conditions: Artificial Intelligence; Colorectal Polyp
Interventions: CAD-CNN system
Locations: Academic Medical Centre
Countries: Netherlands
Enrollment: 292
Age range: 18 Years - None
Sex: ALL

Summary:
Rationale: Diminutive colorectal polyps (1-5mm in size) have a high prevalence and very low risk of harbouring cancer. Current practice is to send all these polyps for histopathological assessment by the pathologist. If an endoscopist would be able to correctly predict the histology of these diminutive polyps during colonoscopy, histopathological examination could be omitted and practise could become more time- and cost-effective. Studies have shown that prediction of histology by the endoscopist remains dependent on training and experience and varies greatly between endoscopists, even after systematic training. Computer aided diagnosis (CAD) based on convolutional neural networks (CNN) may facilitate endoscopists in diminutive polyp differentiation. Up to date, studies comparing the diagnostic performance of CAD-CNN to a group of endoscopists performing optical diagnosis during real-time colonoscopy are lacking.

Objective: To develop a CAD-CNN system that is able to differentiate diminutive polyps during colonoscopy with high accuracy and to compare the performance of this system to a group of endoscopist performing optical diagnosis, with the histopathology as the gold standard.

Study design: Multicentre, prospective, observational trial. Study population: Consecutive patients who undergo screening colonoscopy (phase 2)

Main study parameters/endpoints: The accuracy of optical diagnosis of diminutive colorectal polyps (1-5mm) by CAD-CNN system compared with the accuracy of the endoscopists. Histopathology is used as the gold standard.

Eligibility criteria:
Phase 1A -

\- Patients with one polyp subtype (based on histology)

Phase 1B Patients older than 18 years that underwent colonoscopy in one of the participating centres.

Phase 2:- Validation CAD-CNN system

Inclusion Criteria:

All patients older than 18 years old undergoing screenings colonoscopy in one of the participating centres.

Exclusion Criteria:

* Diagnosis of inflammatory bowel disease, Lynch syndrome or (serrated) polyposis syndrome.
* Boston Bowel Preparation Scale (BBPS) \<2 in one of the colon segments
* Patients who are unwilling or unable to give informed consent

Outcomes:
The accuracy of the CAD-CNN system for predicting histology of diminutive colorectal polyps (1-5mm) compared with the accuracy of the prediction of the endoscopist. Both the CAD-CNN system and the endoscopist will use NBI for their predictions.
The Research of Artificial Intelligence Assistance System Effectiveness for Novice Endoscopists Gastroscopy Training
Trial ID: NCT04682821
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-12-23
Completion date: 2021-06-26
Sponsor: Renmin Hospital of Wuhan University
Conditions: Gastroscopy; Training; Artificial Intelligence
Interventions: Artificial intelligence assistant system
Locations: Renmin Hospital of Wuhan University
Countries: China
Enrollment: 288
Age range: 18 Years - 70 Years
Sex: ALL

Summary:
In this study, we proposed a prospective study about the effectiveness of artificial intelligence system for gastroscope training in novice endoscopists. The subjects would be divided into two groups. The experimental group would be trained in painless gastroscopy with the assistance of the artificial intelligence assistant system. The artificial intelligence assistant system can prompt abnormal lesions and the parts covered by the examination (the stomach is divided into 26 parts). The control group would receive routine painless gastroscopy training without special prompts. Then we compare the gastroscopy operation score, coverage rate of blind spots in gastroscopy,check the average test score before and after training, training satisfaction, detection rate of lesions and so on between the two group.

Eligibility criteria:
Novice endoscopists：

Inclusion Criteria:

1. Males or females who are over 18 years old;
2. After qualified medical education and obtained the Certificate of Chinese medical practitioner;

Exclusion Criterial:

1. A doctor who has already been trained in gastroenteroscopy;
2. Doctors without qualified medical education and didn't obtaine the Certificate of Chinese medical practitioner;
3. The researcher believes that the subjects are not suitable for participating in clinical trials.

Outcomes:
Gastroscopy operation score
A Multicenter Study of Artificial Intelligence-assisted Contrast-enhanced Ultrasound Diagnosis of Thyroid Nodules
Trial ID: NCT05982821
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-06-03
Completion date: 2026-03-16
Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Conditions: Thyroid Nodule
Interventions: Artificial Intelligence
Locations: Sun Yat-sen Memorial Hospital, Sun Yat-sen University
Countries: China
Enrollment: 1955
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this intervention study is to learn about the diagnostic performance of artificial intelligence to assist the contrast-enhanced ultrasound diagnosis of thyroid nodules. The main questions it aims to answer are:

1. Can we develop an artificial intelligence model to assist the contrast-enhanced ultrasound diagnosis of thyroid nodules?
2. Can artificial intelligent reduce the unnecessary biopsy of thyroid nodules?

Participants will be asked to undergo contrast-enhanced ultrasound examination and ultrasound-guided fine-needle aspiration of thyroid nodules. Researchers will calculate the diagnostic performance of the contrast-enhanced ultrasound diagnosis of thyroid nodules with and without artificial intelligent assistance.

Eligibility criteria:
Inclusion Criteria:

* Patients with thyroid nodules with a solid component ≥5 mm confirmed by conventional ultrasound;
* Patients who underwent conventional ultrasound, contrast-enhanced ultrasound, and fine-needle aspiration biopsy;
* Patients with a final benign or malignant pathological results.

Exclusion Criteria:

* Patients with cytopathology of Bethesda I, III, or IV and without final benign or malignant pathology；
* Patients with a history of thyroid ablation or surgery;
* Patients with low-quality ultrasound images.

Outcomes:
Diagnostic performance; The necessity of biopsy
The Effect of Artificial Intelligence-Assisted Climate Change Education on Pregnant Women's Climate Change Concerns and Awareness: A Quasi-Experimental Pretest-Posttest Study
Trial ID: NCT07541885
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-04-01
Completion date: 2026-07-01
Sponsor: Derya Kaya Senol
Conditions: Climate Change; Maternal Health; Fetal Health; Midwifery; Artificial Intelligence (AI)
Interventions: Intervention 1; Control
Enrollment: 82
Age range: 18 Years - 35 Years
Sex: FEMALE

Summary:
This quasi-experimental pretest-posttest study aimed to evaluate the effect of artificial intelligence-assisted climate change education on pregnant women's climate change concerns and awareness. The study will be conducted with pregnant women attending a pregnancy school, and participants will be assigned to intervention and control groups. The intervention group will receive AI-supported climate change education in addition to routine training, while the control group will receive only routine education. Data will be collected using the Climate Change Anxiety Scale and the Maternal-Fetal Health Awareness of Climate Change Scale. The findings are expected to contribute to improving pregnant women's awareness and reducing concerns related to climate change through innovative educational approaches.

Eligibility criteria:
Inclusion Criteria:

* Aged 18 years or older
* In the second or third trimester of pregnancy
* Able to speak Turkish
* Willing to participate and provide informed consent

Exclusion Criteria:

* Presence of a high-risk pregnancy
* Multiple pregnancy (e.g., twins or higher-order gestation)
* Failure to complete either the pre-test or the post-test questionnaire(s)

Outcomes:
Change in Climate Change Awareness Score (Pretest-Posttest)
Ovarian Stimulation Outcomes During a GnRH Antagonist Protocol for In-vitro Fertilization When Comparing Trigger Day Decision by Physicians Versus an Artificial Intelligence-based Algorithm - A Randomized Control Study.
Trial ID: NCT07110285
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-05-21
Completion date: 2028-05-21
Sponsor: FertilAI
Conditions: IVF Outcomes
Interventions: Artificial intelligence-based algorithm
Locations: Herzliya Medical Center
Countries: Israel
Enrollment: 180
Age range: 18 Years - 45 Years
Sex: ALL

Summary:
To compare the number of mature oocytes following a GnRH antagonist in-vitro fertilization stimulation cycle when comparing trigger day timing decision by physicians versus an artificial intelligence-based algorithm

Eligibility criteria:
Inclusion Criteria:

* All patients undergoing an ovarian stimulation cycle utilizing the GnRH antagonist protocol for oocyte cryopreservation or for fertilization via intracytoplasmic sperm injection (ICSI) (any treatment where oocytes are denuded and maturity may be assessed).

Exclusion Criteria:

* Loss to follow-up, patient retracts consent to participate

Outcomes:
Mature oocyte yield
Validation of the Utility of a Universal Cataract Intelligence Platform
Trial ID: NCT03623971
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2013-01-01
Completion date: 2017-06-01
Sponsor: Sun Yat-sen University
Collaborators: Xidian University
Conditions: Cataract; Artificial Intelligence
Interventions: Cataract AI agent
Enrollment: 500
Age range: None - None
Sex: ALL

Summary:
This study established and validated a universal artificial intelligence (AI) platform for collaborative management of cataracts involving multi-level clinical scenarios and explored an AI-based medical referral pattern to improve collaborative efficiency and resource coverage.The datasets were labeled using a three-step strategy: (1) categorize slit lamp photographs into four separate capture modes; (2) diagnose each photograph as a normal lens, cataract or a postoperative eye; and (3) based on etiology and severity, further classify each diagnosed photograph for a management strategy of referral or follow-up. A deep residual convolutional neural network (CS-ResCNN) was used for the image classification task. Moreover, we integrated the cataract AI agent with a real-world multi-level referral pattern involving self-monitoring at home, primary healthcare, and specialized hospital services.

Eligibility criteria:
Inclusion Criteria:

Patients who underwent ophthalmic examination of the eye and recorded their ocular information in the primary healthcare center.

Exclusion Criteria:

The patients who cannot cooperate with the examinations.

Outcomes:
Diagnostic accuracy of the cataract AI agent
Artificial Intelligence-assisted Confocal Laser Endomicroscopy Identification of Intestinal Metaplasia Severity for Gastric Cancer Risk Assessment
Trial ID: NCT05462743
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-07-01
Completion date: 2023-12-30
Sponsor: Shandong University
Conditions: Intestinal Metaplasia; Artificial Intelligence; Confocal Laser Endomicroscopy
Locations: Qilu Hospital, Shandong University
Countries: China
Enrollment: 1000
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
Currently, the Correa cascade is a widely accepted model of gastric carcinogenesis. Intestinal metaplasia is a high risk factor for gastric cancer. According to Sydney criteria, mild intestinal metaplasia was not associated with gastric cancer, while moderate to severe intestinal metaplasia was strongly associated with the development of gastric cancer. Because intestinal metaplasia is distributed in various forms, the use of white light endoscopy lacks specificity, and the consistency with histopathological diagnosis is poor; Pathological biopsy is still needed to make a diagnosis. At present, national guidelines suggest that OLGIM score should be used to evaluate the risk of gastric cancer, and patients with OLGIM grade III/IV should be monitored by close gastroscopy. However, it requires at least four biopsies, which is clinically infeasible. Confocal laser endomicroscopy allows real-time observation of living tissue, comparable to pathological findings.

Eligibility criteria:
Inclusion Criteria:

* Patients aged 18-80 years undergoing confocal gastroscopy

Exclusion Criteria:

* Patients with severe cardiac, cerebral, pulmonary or renal dysfunction or psychiatric disorders who cannot participate in gastroscopy
* Patients with previous surgical procedures on the stomach
* Patients with contraindications to biopsy
* Patients who refuse to sign the informed consent form

Outcomes:
Accuracy of AI model in assessing degree of intestinal metaplasia; Sensitivity of AI model to assess degree of intestinal metaplasia; Specificity of AI model to assess degree of intestinal metaplasia
Accuracy of an Artificial Intelligence-Based System for Diagnosing Depressive Disorders: A Paired Comparison With Psychiatrist Clinical Diagnoses
Trial ID: NCT07311668
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-02-01
Completion date: 2026-03-15
Sponsor: Shanghai Mental Health Center
Conditions: Depressive Disorder
Locations: Shanghai Mental Health Center
Countries: China
Enrollment: 100
Age range: 18 Years - 60 Years
Sex: ALL

Summary:
The trial aimed to evaluate the accuracy of an artificial intelligence-based system for diagnosing depressive disorders. Specifically, it sought to determine whether the system's assessment validity is non-inferior to that of psychiatric specialists.

Eligibility criteria:
1. Subjects with anxiety disorders （1）Inclusion Criteria:

   * In accordance with ICD-11 for Depressive Disorders;
   * Between the ages of 18-60;
   * Ability to use computers or smartphone;
   * Native Chinese speaker;
   * Signing informed consent. （2）Exclusion Criteria:
   * With severe psychiatric symptoms requiring hospitalization, or unable to complete the required assessment and treatment;
   * With a high risk of suicide or self-injury;
   * With severe physical diseases, central nervous system diseases, or substance abuse;
   * With intellectual, visual, or auditory impairments that affect their ability to interact with aided-diagnostic systems.
2. Health Control （1）Inclusion Criteria:

   * Not meet ICD-11 criteria for Mental Disorders;
   * Between the ages of 18-60;
   * Ability to use computers or smartphone;
   * Native Chinese speaker;
   * Signing informed consent. （2）Exclusion Criteria:
   * With mental illness, or unable to complete the required assessment and treatment;
   * With severe physical diseases, central nervous system diseases, or substance abuse;
   * With intellectual, visual, or auditory impairments that affect their ability to interact with aided-diagnostic systems.
3. Psychiatrist （1）Inclusion Criteria:

   * Over 18 years old;
   * A minimum of three years' experience in anxiety and other mental health fields;
   * Intermediate or higher professional title;
   * Currently employed in the selected test region;
   * Signing informed consent.

Outcomes:
Diagnostic Accuracy (Sensitivity, Specificity, and Area Under the Curve) of the AI-Based Screening System for Depressive Disorders
Collecting Respiratory Sound Samples From Corona Patients to Extend the Diagnostic Capability of VOQX Electronic Stethoscope to Diagnose COVID-19 Patients
Trial ID: NCT04910191
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-05-27
Completion date: 2021-12
Sponsor: Sanolla
Conditions: COVID-19
Interventions: Electronic stethoscope
Locations: Barzilai Medical Center; Hille Yaffe Medical Center; Shamir Medical Center (Assaf Harofah)
Countries: Israel; Israel; Israel
Enrollment: 200
Age range: 18 Years - None
Sex: ALL

Summary:
Technological developments in the recent decades has enabled the integration of electronic and digital components in the stethoscope design, in an attempt to improve auditory performance and, moreover, to assist in improving user's diagnostic accuracy by incorporating computerized, digital technologies, artificial intelligence capabilities and deep-learning-based algorithms enhancing these devices.

We believe that these technologies can be used to significantly improve the diagnostic performance in the primary care phase, by means of a sophisticated stethoscope that enables auscultation to sounds and signals typically found in the sub-sound frequency level. Their transformation into the sound range, and the use of artificial intelligence and machine learning techniques to characterize sound patterns that correspond to specific problems or diseases can substantially enhance the physician's or other care giver's performance to the benefit of the patients.

At this stage, the software in development does not purport to make diagnostic decisions, but only to provide information that will enhance decision and diagnosis making process, therefore enable a more accurate and definitive diagnostic decision and perhaps decrease the number of additional diagnostic tests requested.

Eligibility criteria:
Inclusion Criteria:

* Patients over the age of 18 years
* RT-PCR positive for COVID 19
* Patients diagnosed with the following pulmonary pathology:

  * Pneumonia
  * Pulmonary edema
  * Bronchitis
  * Acute asthmatic attack
  * Emphysema
  * Or Normal (e.g. asymptomatic patients)
* The diagnosis is confirmed if possible, by:

  * Anamnesis
  * Physical examination
  * X-ray
  * Suggestive blood test - CBC
  * Pulse oximetry

Exclusion Criteria:

* Pregnant women
* Chest malformation
* Unconsciousness
* Subject that need a guardian
* Weigh above 150 Kg.
* Patients with current shortness of breath
* Patients currently assisted by breathing machine such as CPAP or other

Outcomes:
Performance outcome; Performance outcome
A Artificial Intelligence Model Predicts Pathological Complete Response of Lung Cancer Following Neoadjuvant Immunochemotherapy
Trial ID: NCT06285058
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2024-03
Completion date: 2026-03
Sponsor: Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Conditions: Deep Learning Model; Pathological Complete Response; Non-small Cell Lung Cancer; Neoadjuvant Chemoimmunotherapy
Interventions: No interventions
Enrollment: 1000
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
This study presents the development and validation of an artificial intelligence (AI) prediction system that utilizes pre-neoadjuvant immunotherapy plain scans and enhanced multimodal CT scans to extract deep learning features. The aim is to predict the occurrence of pathological complete response in non-small cell lung cancer patients undergoing neoadjuvant immunochemotherapyy.

Eligibility criteria:
Inclusion Criteria:

1. Patients' with non-small cell lung cancer, diagnosed through biopsy pathology and clinically classified as stage IB to III;
2. Patients who receive at least two cycles of neoadjuvant immunotherapy combined with chemotherapy induction therapy;
3. According to the IASLC guidelines, postoperative pathological evaluation was performed on the treatment response of the tumor primary lesion and lymph nodes.

Exclusion Criteria:

1. Missing or inadequate quality of CT;
2. Time interval between CT and start of treatment is greater than 1 month;
3. Incomplete clinicopathologic data.

Outcomes:
the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of predicting model
A Prospective, Multi-center Clinical Study on the Application of An Artificial Intelligence Enabled Disease Detection Software to Diabetic Retinopathy Screening Based on Fundus Images
Trial ID: NCT03602989
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2018-07-05
Completion date: 2019-08-31
Sponsor: Shenzhen SiBright Co., Ltd.
Collaborators: Zhongshan Ophthalmic Center, Sun Yat-sen University; Peking University People's Hospital; The Eye Hospital of Wenzhou Medical University
Conditions: Diabetic Retinopathy
Interventions: AI-enabled Diabetic Retinopathy Screening Software
Locations: Peking University People's Hospital; Zhongshan Ophthalmic Center; The Eye Hospital of Wenzhou Medical University
Countries: China; China; China
Enrollment: 1000
Age range: 18 Years - None
Sex: ALL

Summary:
Early detection and intervention of diabetic retinopathy (DR) is critical in preventing DR-related vision loss among type 1 (T1DM) and type 2 diabetic mellitus (T2DM) patients, currently estimated at over 100 million in China alone. Yet the healthcare resources, particularly retinal specialists, are in short supply and unevenly distributed. In order to help address this enormous mismatch and implement population-based screening, an artificial intelligence (AI) enabled, cloud based software is developed by training a custom-built convolutional neural network.

This study is designed to evaluate the safety and efficacy of such device in detecting referable diabetic retinopathy (moderate non-proliferative DR or worse).

Eligibility criteria:
Inclusion Criteria:

1. Subject must understand the study, has chosen to participate voluntarily, and has signed informed consent
2. Age 18 or older, no limitations on gender identity
3. Type 1 Diabetes Mellitus and Type 2 Diabetes Mellitus

Exclusion Criteria:

1. As it is difficult to obtain fundus images of satisfactory quality with small pupils, mydriasis is advisable under certain circumstances except if:

   1. the subject is allergic to mydriatic drugs;
   2. the subject's intraocular pressure (IOP) ≥ 22 mmHg;
   3. the subject is prone to post-dilation angle closure, pupillary block, etc.;
2. The subject has refractive media opacity and/or pupil abnormalities that affect fundus examination and imaging;
3. The subject has severe vitreous hemorrhage;
4. The subject has received fundus laser treatment;
5. The subject has had eye surgery such as scleral buckling, vitrectomy, macular transposition, etc., BUT cataract surgery or external eye surgery are exempt from exclusion criteria;
6. The subject is participating in other ophthalmic clinical trials;
7. In cases when the researchers believe the subject is not suitable for taking fundus photograph, including but not limited to:

   1. The subject had nystagmus and could not obtain a gradable fundus photograph;
   2. other disqualifying condition deemed by the researcher.

Outcomes:
Sensitivity and specificity