moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
An Integrated Artificial Intelligence Approach for Predicting Analgesic Time Based on Nalbuphine Versus Morphine as Adjuvants to Bupivacaine in Ultrasound-Guided Supraclavicular Block
Trial ID: NCT07008443
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: PHASE4
Start date: 2024-01-01
Completion date: 2025-04-01
Sponsor: Alzahraa Ahmed Abbas
Conditions: Regional Anesthesia Block; Upper Limb Surgery
Interventions: Bupivacaine + saline; Bupivacaine + nalbuphine; Bupivacaine + morphine
Locations: Al-Zahraa University Hospital
Countries: Egypt
Enrollment: 60
Age range: 21 Years - 60 Years
Sex: ALL

Summary:
This study investigated the effect of adding nalbuphine or morphine to bupivacaine for supraclavicular brachial plexus block in upper limb surgeries. Sixty adult patients were randomized into three groups: control (bupivacaine + saline), nalbuphine, and morphine. The primary objective was to compare the duration of analgesia between the groups. A secondary goal was to assess whether artificial intelligence (AI), specifically the k-nearest neighbor (KNN) algorithm, could predict analgesic duration based on patient clinical and demographic data. The study concluded that both nalbuphine and morphine significantly prolonged analgesic duration and that the AI model showed high predictive accuracy.

Eligibility criteria:
Inclusion Criteria:

Adult patients aged 21-60 years

ASA physical status I or II

Scheduled for elective upper limb surgery below the elbow

Provided written

Outcomes:
Analgesic Duration
Early Detection of Concealed Cardiac Amyloidosis Using AI-ECG and CT-Derived Extracellular Volume in Patients With Atrial Fibrillation
Trial ID: NCT07567508
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-05
Completion date: 2029-12-31
Sponsor: Samsung Medical Center
Collaborators: Mayo Clinic
Conditions: Atrial Fibrillation (AF); Cardiac Amyloidosis
Interventions: AI-ECG analysis; CT-ECV analysis; Atrial fibrillation treatment
Locations: Samsung Medical Center
Countries: South Korea
Enrollment: 500
Age range: 19 Years - None
Sex: ALL

Summary:
This study investigates the clinical efficacy of a non-invasive screening protocol using AI-ECG and CT-ECV analysis for cardiac amyloidosis. The study targets on atrial fibrillation(AF) patients with "red-flag" indicators.

Participants are randomized 1:1 into either an early screening or usual care group.

* Early screening group : AI- ECG and/or CT-ECV analysis + AF treatment
* Usual care group : AF treatment Both groups followed for 2 years to compare CA detection rates and clinical outcomes.

Eligibility criteria:
Inclusion Criteria:

* Adults aged 19 or older who have provided voluntary written informed consent.
* Patients with a history of AF (paroxysmal or persistent) and one or more red-flag symptoms/signs.
* Patients who can undergo at least one of the following: AI-ECG analysis or CT-ECV analysis.

Exclusion Criteria:

* Patients previously diagnosed with cardiac amyloidosis (AL or ATTR).
* Patients with severe heart failure (NYHA class IV) or terminal illness with a life expectancy of less than 1 year.
* Patients deemed inappropriate for participation by the investigator.

Outcomes:
Cardiac amyloidosis(CA) detection rate
Prospective Randomized Study on the Use of Artificial Intelligence (Fujifilm) for Polyp Detection in Colonoscopy
Trial ID: NCT04894708
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-10-28
Completion date: 2024-09
Sponsor: Universitätsklinikum Hamburg-Eppendorf
Collaborators: FUJIFILM Deutschland, Branch office of FUJIFILM Europe GmbH
Conditions: Screening Colonoscopy; Colonoscopic Control After Polypectomy; Suspected Colon Polyps
Interventions: colonoscopy
Locations: Gastroenterologiepraxis Dr. Moog; Universitätsklinikum Leipzig; GastroZentrum Lippe; Gastroenterologie am Bayerischen Platz; University Hospital Bonn; St. Vinzenz-Hospital / Akademisches Lehrkrankenhaus der Universität zu Köln; University Hospital Eppendorf; University Hospital Magdeburg; Marienhospital Osnabrück; Asklepios Paulinen Klinik Wiesbaden
Countries: Germany; Germany; Germany; Germany; Germany; Germany; Germany; Germany; Germany; Germany
Enrollment: 1572
Age range: 35 Years - None
Sex: ALL

Summary:
Colonoscopy is currently the best method of detection of intestinal tumors and polyps, particularly because polyps can also be biopsied and removed. There is a clear correlation between the adenoma detection rate and prevented carcinomas, so adenoma detection rate is the main parameter for the outcome quality of diagnostic colonoscopy. The efficiency of preventive colonoscopy needs optimisation by increase in adenoma detection rate, as it is known from many studies that approximately 15-30% of all adenomas can be overlooked. This mainly applies to smaller and flat adenomas. However, since even smaller polyps may be relevant for colorectal cancer development, the aim of colonoscopy should be to preferably be able to recognize all polyps and other changes.The latest and by far the most interesting development in this field is the use of artificial intelligence systems. They consist of a switched-on software with a small computer connected to the endoscope processor; the patient's introduced endoscope is completely unchanged.

The present study therefore compares the adenoma detection rate (ADR) of the latest generation of devices with high-resolution imaging from Fujifilm with and without the connection of artificial intelligence.

Eligibility criteria:
Inclusion Criteria:

* Persons\> 35 years of age who are capable of giving informed consent
* Planned diagnostic colonoscopy (clarification of symptoms, polyp follow-up)
* Screening colonoscopy for men \>50 or women \> 55 years of age

Exclusion Criteria:

* Colon bleeding
* Colon carcinoma
* Known polyps for removal
* Inflammatory bowel disease
* Colonic stenosis
* Other suspected colon disease for further clarification
* Follow-up care after colon cancer surgery (partial colon resection)
* Anticoagulant drugs that make a biopsy or polypectomy impossible
* Poor general condition (ASA IV)
* Incomplete colonoscopy planned

Outcomes:
Adenoma detection rate
Utilization of Real-time Automatic Quality-control System in the Detection of Gastric Neoplasms
Trial ID: NCT03883035
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2019-03-20
Completion date: 2019-10-31
Sponsor: Yanqing Li
Conditions: Esophagogastroduodenoscopy
Interventions: automatic quality-control system
Locations: Department of Gastroenterology, Qilu Hospital, Shandong University
Countries: China
Enrollment: 1060
Age range: 40 Years - 80 Years
Sex: ALL

Summary:
The aim of our study was to develop an automatic quality-control system and assess the performance of this system in quality control of esophagogastroduodenoscopy.

Eligibility criteria:
Inclusion Criteria:

* patients undergoing EGD examination with anaesthesia;
* patients able to give informed consent.

Exclusion Criteria:

* patients with the contraindications to EGD examination;
* patients with a prior EGD examination within 1 year before the trial;
* patients with frankly upper gastrointestinal malignant lesions that were apparent on endoscopy;
* a history of upper gastrointestinal cancers;
* allergic to anaesthetic in previous medical history;
* patients refused to participate in the trial;
* the EGD procedure cannot be completed due to stenosis, obstruction, solid food, or complications of anaesthesia.

Outcomes:
detection rate of gastric neoplasms
Development of New Diagnostic Tools in Capsule Endoscopy
Trial ID: NCT06152289
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-02-10
Completion date: 2028-02
Sponsor: Assistance Publique - Hôpitaux de Paris
Conditions: Bowel Disease; Crohn Disease; Celiac Disease; Chronic Diarrhea; Tumor
Locations: Centre d'Endoscopie Digestive Hôpital Saint-Antoine
Countries: France
Enrollment: 10000
Age range: None - None
Sex: ALL

Summary:
Patients participating to this study will provide images and videos of capsule endoscopy to train, tune and evaluate technological bricks of artificial intelligence solutions, in order to improve diagnostic performances of the procedure, while reducing reading time by physicians.

Eligibility criteria:
Inclusion Criteria:

\- Any patient in whom a capsule endoscopy examination is performed as part of routine care.

Exclusion Criteria:

\- Opposition to the use of images and videos from daily, routine care for research purposes.

Outcomes:
Develop an artificial intelligence solution to help with diagnosis in VCE
Project 3 Example: Human-AI Collaboration Tester (HAICT) Exp. 7
Trial ID: NCT05272189
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-01-01
Completion date: 2025-11-04
Sponsor: Brigham and Women's Hospital
Collaborators: National Cancer Institute (NCI)
Conditions: Decision Making; Computer Aided Diagnosis
Interventions: Simulated Second Reader AI; Target Prevalence
Locations: Visual Attention Lab / Brigham and Women's Hospital
Countries: United States
Enrollment: 12
Age range: 18 Years - None
Sex: ALL

Summary:
The study is one part of a "bundle" of experiments that constitute Project Three of a National Eye Institute grant. Project Three includes a series of experiments that investigate how changing the input from a simulated AI can affect the decisions made by human observers in a two-alternative forced choice task (like the decision to recall a woman for further examination in mammography). HAICT 7, the experiment described here, investigates how changing prevalence affects human performance when AI is used as a Second Reader.

Eligibility criteria:
Inclusion Criteria:

* \- All welcome to enroll on line

Exclusion Criteria:

* Must pass the Ishihara color vision screening test
* 20/25 vision (with correction)

Outcomes:
D'; Criterion
Preact to Lower the Risk of Falling by Customized Rehabilitation Across Europe: the PRECISE Study In Italy
Trial ID: NCT05846776
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-10-26
Completion date: 2024-02-02
Sponsor: Istituto Nazionale di Ricovero e Cura per Anziani
Collaborators: European Union
Conditions: Elderly Subjects; Risk of Fall
Interventions: DigiPrehab system
Locations: IRCCS INRCA Hospital
Countries: Italy
Enrollment: 43
Age range: 65 Months - None
Sex: ALL

Summary:
The PRECISE study is a 12-week pilot intervention study to evaluate the usability of the new DigiPrehab technology application in elderly subjects. The DigiPrehab system will enable the early identification of seniors with significant risk factors for falling and will propose an individualized physical training plan at home.

Eligibility criteria:
Inclusion Criteria:

* Independent ambulation
* Fall risk assessed by Tinetti test
* Mini Mental State Examination ≥ 24
* Residents at home
* Familiarity with web applications
* Ability and willingness to sign informed consent

Exclusion Criteria:

* Unstable clinical condition by judgment of the physician
* Severe visual and/or hearing impairment
* Severe impairment (Activities of Daily Living) in medical record
* Absence of primary caregiver

Outcomes:
Change in Usability
The New Challenge of Decoding Rectal Cancer Signatures By Non-Invasive Imaging: A Retrospective Radiomics Study
Trial ID: NCT05331040
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-05-01
Completion date: 2025-05
Sponsor: NHS Grampian
Collaborators: Industrial centre for Artificial intelligence Research; Roland Sutton Academic Trust; Innovate UK; University of Aberdeen
Conditions: Rectal Cancer
Interventions: MRI
Locations: NHS Grampian
Countries: United Kingdom
Enrollment: 350
Age range: 18 Years - None
Sex: ALL

Summary:
This retrospective study aims to investigate whether initial imaging characteristics of rectal cancer on Magnetic Resonance Imaging (MRI) correlate with the underlying tumour pathology and oncological outcomes such as response to treatment. Using radiomic features, calculated using new high throughput analysis of previously acquired imaging, a statistically robust prognostic model will be created with the overall aim of developing imaging biomarkers.

Eligibility criteria:
Inclusion Criteria:

* All patients with newly diagnosed rectal cancer within NHSG for the five year period (2010-2015) who had a pre-operative staging MRI at NHSG for whom pathology reported within NHSG.

Exclusion Criteria:

* Those patients whose MRI scans are degraded from artifact (such as metal artifact from hip replacements)
* Patients lost to follow-up or moved out with NHS Grampian during follow-up.
* Patients with incomplete clinical or pathological data.

Outcomes:
Model
Integrating Deep Learning CT-scan Model, Biological and Clinical Variables to Predict Severity of Asthma in Children
Trial ID: NCT05140889
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2021-01-20
Completion date: 2026-06-30
Sponsor: Fondazione IRCCS Policlinico San Matteo di Pavia
Collaborators: Istituto per la Ricerca e l'Innovazione Biomedica; Università Ca' Foscari Venezia
Conditions: Asthma in Children
Locations: IRCCS Policlinico San Matteo
Countries: Italy
Enrollment: 25
Age range: 6 Years - 17 Years
Sex: ALL

Summary:
Artificial intelligence (AI) offers substantial opportunities for healthcare, supporting better diagnosis, treatment, prevention and personalized care. Analysis of health images is one of the most promising fields for applying AI in healthcare, contributing to better prediction, diagnosis and treatment of diseases.

Deep learning (DL) is currently one of the most powerful machine learning techniques. DL algorithms are able to learn from raw (or with little pre-processing) input data and build by themselves sophisticated abstract feature representations (useful patterns) that enable very accurate task decision making. Recently, DL has shown promising results in assisting lung disease analysis using computed tomography (CT) images.

Current severe asthma guidelines recommend high-resolution and multidetector CT as a tool for disease evaluation. CT scans contain prognostic information, as the presence of bronchial wall thickening, air trapping, bronchial luminal narrowing, and bronchiectasis are associated with longer disease duration and disease severity in adults. Only a small number of studies have reported chest CT findings in children with severe asthma, and their relationship to clinical and pathobiological parameters yielded inconsistent results. Thus, to which extent CT scans add prognostic information beyond what can be inferred from clinical and biological data is still unresolved in children.

The project is expected to build an DL-severity score to prognoses severe evolution for children with asthma, using a DL model to capture CT scan prognosis information.

Eligibility criteria:
Inclusion Criteria:

* age 6-17 years
* confirmed diagnosis of severe asthma according to ERS/ATS guidelines

Exclusion Criteria:

* other diseases that may mimic asthma according to ERS/ATS guidelines (i.e., cystic fibrosis, primary ciliary dyskinesia, tracheobronchomalacia, etc)

Outcomes:
Prediction of asthma severity in children
Application of Deep Learning for Screening Multiple Corneal Diseases
Trial ID: NCT06211218
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2020-12-06
Completion date: 2024-12-06
Sponsor: Tianjin Eye Hospital
Conditions: Deep Learning; Corneal Disease; Screening
Interventions: Cornea diseases diagnosed by artificial intelligence algorithm
Locations: Tiajin Eye Hospital
Countries: China
Enrollment: 3000
Age range: None - None
Sex: ALL

Summary:
This study developed a deep learning algorithm based on anterior segment images and prospectively validated its ability to identify corneal diseases.The effectiveness and accuracy of this algorithm was evaluated by sensitivity, specificity, positive predictive value, negative predictive value, and area under curve.

Eligibility criteria:
Inclusion Criteria:

1. The quality of slit-lamp images should clinical acceptable.
2. More than 90% of the slit-lamp image area including three main regions (sclera, pupil, and lens) are easy to read and discriminate.

Exclusion Criteria:

1）Insufficient information for diagnosis.

Outcomes:
Area under curve; Sensitivity and specificity
The AID Study: Artificial Intelligence for Colorectal Adenoma Detection
Trial ID: NCT04079478
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2019-09-25
Completion date: 2019-12-31
Sponsor: Istituto Clinico Humanitas
Conditions: Colon Cancer
Interventions: AI
Locations: Endoscopy Unit, Humanitas Research Hospital
Countries: Italy
Enrollment: 700
Age range: 40 Years - 80 Years
Sex: ALL

Summary:
Colonoscopy is clinically used as the gold standard for detection of colon cancer (CRC) and removal of adenomatous polyps. Despite the success of colonoscopy in reducing cancer-related deaths, there exists a disappointing level of adenomas missed at colonoscopy. "Back-to-back" colonoscopies have indicated significant miss rates of 27% for small adenomas (\< 5 mm) and 6% for adenomas of more than 10 mm in diameter. Studies performing both CT colonography and colonoscopy estimate that the colonoscopy miss rate for polyps over 10 mm in size may be as high as 12%. The clinical importance of missed lesions should be emphasized because these lesions may ultimately progress to CRC8.

Limitations in human visual perception and other human biases such as fatigue, distraction, level of alertness during examination increases such recognition errors and way of mitigating them may be the key to improve polyp detection and further reduction in mortality from CRC. In the past years, a number of CAD systems for detection of polyps from endoscopy images have been described. However, the benefits of traditional CAD technologies in colonoscopy appear to be contradictory, therefore they should be improved to be ultimately considered useful. Recent advances in artificial intelligence (AI), deep learning (DL), and computer vision have shown potential to assist polyp detection during colonoscopy.

Eligibility criteria:
Inclusion Criteria:

All 40-80 years-old subjects undergoing a colonoscopy.

Exclusion Criteria:

* subjects with personal history of CRC, or IBD.
* patients with inadequate bowel preparation (defined as Boston Bowel Preparation Scale \> 2 in any colonic segment).
* patients with previous colonic resection.
* patients on antithrombotic therapy, precluding polyp resection.
* patients who were not able or refused to give informed written consent.

Outcomes:
Additional diagnostic yield obtained by AI-aided colonoscopy to the yield obtained by the Standard (high-definition) colonoscopy
Application of Large Language Models in the Recommendation of Treatment Plans for Gastrointestinal Cancers
Trial ID: NCT06002425
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-08-29
Completion date: 2028-12-31
Sponsor: Chinese Academy of Sciences
Collaborators: ZhuHai Hospital; Fifth Affiliated Hospital, Sun Yat-Sen University; Jiangmen Central Hospital; Peking University Cancer Hospital (Inner Mongolia Campus); San Raffaele University Hospital, Italy; University Hospital Magdeburg, Germany; City of Hope Medical Center
Conditions: Gastrointestinal Neoplasm Malignant
Interventions: Clinician-Directed Treatment Plan; ChatGPT-Assisted Treatment Plan
Locations: City of Hope; Jiangmen Central Hospital; The Fifth Affiliated Hospital of Sun Yat-sen University; Zhuhai People's Hospital; Peking University Cancer Hospital (Inner Mongolia Campus); University Hospital Magdeburg; San Raffaele University Hospital, Italy
Countries: United States; China; China; China; China; Germany; Italy
Enrollment: 400
Age range: 18 Years - None
Sex: ALL

Summary:
This study will evaluate the utility of ChatGPT in recommending treatment plans for patients with gastrointestinal cancers, using both retrospective and prospective data.

Eligibility criteria:
Inclusion Criteria:

* Age ≥18 years, both male and female.
* Pathologically confirmed diagnosis of gastrointestinal cancer (gastric cancer or colorectal Cancer).
* Detailed medical records available prior to treatment (including chief complaint, history of present illness, radiological examinations, pathological examinations, laboratory tests, etc.).
* Participants will receive complete treatment in the participating hospitals.

Exclusion Criteria:

* Participants with cancers other than gastrointestinal cancers.
* Participants who receive treatment in multiple hospitals.

Outcomes:
Influence Rate of ChatGPT on Treatment Plans
A Retrospective, Multicenter, Controlled Clinical Trail: to Evaluate the Clinical Efficiency of Intracranial Aneurysm Assistive Software in the Morphological Measurement
Trial ID: NCT05804474
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-01-01
Completion date: 2023-03-25
Sponsor: Beijing Tiantan Hospital
Collaborators: Wuxi No. 2 People's Hospital; RenJi Hospital; Shanxi Provincial People's Hospital; Beijing Chao Yang Hospital; Beijing Friendship Hospital; Guangzhou Red Cross Hospital
Conditions: Intracranial Aneurysm
Interventions: Observational study
Locations: Beijing Tiantan Hospital
Countries: China
Enrollment: 1500
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
the goal of this clinical trail is to evaluate the clinical efficiency of intracranial aneurysm assistive software in the morphological measurement. The trail is designed as retrospective, multicenter, controlled clinical trail. Collecting probable cases images retrospectively according to inclusion criteria and exclusion criteria strictly. Controlling bias strictly, to make sure the measurement of intracranial aneurysm assistive software is evaluated reliably and precisely. In our trail, six physicions were recruited, containing three neuroradiologists, two radiologists and a residents. Software-assistant planning controls against no software-assisant planning，which aims to evaluate the improvement of morphological measurement efficiency of physicians.

Eligibility criteria:
Inclusion Criteria:

1. aged 18-75y, no gender limitation.
2. non-ruptured intracranial saccular aneurysm with specific intracranial CTA images.
3. Intracranial aneurysm was not treated.

Inclusion criteria of images:

1. the number of detector rows of Computed Tomography(CT) is more than 16 rows.
2. slice thickness ≤ 0.625mm, whole-brain image is considered.
3. Both plain scan sequences and enhancer sequence are required.

Exclusion Criteria:

1. combined with cerebral hemorrhage;
2. combined with cerevascular malformation or cerebral occupying lesion;
3. fusiform aneurysm or dissection aneurysm;
4. intracaverous internal carotid artery aneurysm;

Exclusion criteria of images:

1. none DICOM format;
2. quality score less than 3 scores;
3. metal artifacts existance.

Outcomes:
Intracranial aneurysm size