moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Prospective User Study and Multicenter Validation of Multimodal Medical Imaging Large Models in the Diagnosis of Common Systemic Diseases
Trial ID: NCT07555002
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-01-01
Completion date: 2026-12
Sponsor: The Third Affiliated Hospital of Southern Medical University
Conditions: Diagnostic Imaging; Common Systemic Diseases; Artificial Intelligence (AI)
Interventions: Standalone Radiologist Interpretation; AI-assisted Radiologist Interpretation
Locations: The Third Affiliated Hospital of Southern Medical University
Countries: China
Enrollment: 1000
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to evaluate the diagnostic performance and clinical utility of a multimodal medical imaging large model in identifying common systemic diseases. Through a retrospective reader study involving multiple centers, the research will compare the diagnostic accuracy, sensitivity, and specificity of radiologists with and without AI assistance. The goal is to validate the model's robustness and its impact on the diagnostic efficiency of clinicians across diverse healthcare settings.

Eligibility criteria:
Inclusion Criteria:

* Patients who underwent systemic medical imaging examinations (e.g., CT or MRI) at participating centers for common systemic diseases.
* Imaging data must have confirmed clinical reference standards, expert consensus, or pathological diagnosis.
* Availability of complete DICOM format images with standard acquisition protocols.

Exclusion Criteria:

* Poor image quality (e.g., severe motion or metal artifacts) that precludes definitive diagnosis.
* Cases with incomplete clinical or pathological reference standards.
* Corrupted image files or duplicate cases.

Outcomes:
Area Under the Receiver Operating Characteristic Curve (AUC)
Assisted Method for the Quantification, Localization and Automated Diagnosis of Stroke in Computed Tomography With the Use of Artificial Intelligence
Trial ID: NCT03874702
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2019-08-15
Completion date: 2025-06-01
Sponsor: University of Guadalajara
Conditions: Stroke; Ischemic Stroke
Locations: Departamento de neurociencias
Countries: Mexico
Enrollment: 350
Age range: 18 Years - None
Sex: ALL

Summary:
The use of the systems of machine learning for the quantification, location and diagnosis of ischemic stroke in non-contrasted head computed tomography, is a method with high efficacy, accessible and susceptible for standardization, for the assistance in the clinical decision making in the absence of specialized health personnel for the attention of this disease.

Eligibility criteria:
Inclusion Criteria:

* Patients with ischemic stroke.
* Patients with \<4.5 hours of symptom onset.

Exclusion Criteria:

* Patients with hemorrhagic stroke diagnosis.
* Patients with other cerebrovascular diseases.

Outcomes:
Alberta Stroke Program Early Computed Tomography Score
Usage Of Artificial Intelligence-Integrated Usg In Application of Pectoral Block In Mastectomies
Trial ID: NCT06480045
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-11-05
Completion date: 2023-05-05
Sponsor: Baskent University
Conditions: Breast Neoplasms
Interventions: Performed PECS II block with AI integrated USG; Performed PECS II block with conventional USG
Locations: Çağla Yazar, MD
Countries: Turkey (Türkiye)
Enrollment: 70
Age range: 18 Years - 70 Years
Sex: FEMALE

Summary:
The investigator's goal in this study is to apply PECS II block, before starting surgery, by either conventional USG or artificial intelligence integrated USG to patients who will undergo mastectomy under general anesthesia to determine the effect of the block on hemodynamics and opioid consumption in the intraoperative period. Besides, the investigatorwanted to investigate the contribution of artificial intelligence integrated USG use in the success of a 4-year anesthesiology resident for imaging the injection site.

Eligibility criteria:
Inclusion Criteria:

* 18-70 years
* ASA I and II
* Female
* Underwent mastectomy surgery
* Without local anesthetic allergy

Exclusion Criteria:

* Patients with ASA III and above
* Obesity (body mass index \>30)
* Psychotropic drug use
* Bleeding diathesis
* Local anesthetic allergy
* Chest wall deformity
* Infection at the injection site
* Chronic analgesic usage

Outcomes:
Pain in the postoperative period was evaluated using the VAS score.
Optical-Coherence Tomography for the Non-invasive Diagnosis and Subtyping of Basal Cell Carcinoma
Trial ID: NCT05041777
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2017-02-15
Completion date: 2021-12-30
Sponsor: Maastricht University Medical Center
Collaborators: Maastricht University
Conditions: Basal Cell Carcinoma; Optical Coherence Tomography; Deep Learning; Artificial Intelligence
Interventions: Optical Coherence Tomography
Locations: Maastricht University Medical Centre+
Countries: Netherlands
Enrollment: 963
Age range: 18 Years - None
Sex: ALL

Summary:
Rationale:

To date, the diagnosis and subtyping of basal cell carcinoma (BCC) is verified with histopathology which requires a biopsy. Because this technique is invasive, new non-invasive strategies have been developed, including Optical Coherence Tomography (OCT). This innovative technique enables microscopically detailed examination of lesions, which is useful for diagnosing and identification of various subtypes of BCC. The diagnostic value of the VIVOSIGHT OCT in daily clinical practice, has not been established to date.

Eligibility criteria:
Inclusion Criteria:

* Adult patients (18 years or older) receiving a skin biopsy of a lesion clinically suspected for a non-melanoma skin cancer or premalignancy

Exclusion Criteria:

* Patients who were incompetent to sign informed consent were excluded

Outcomes:
Diagnostic accuracy of OCT in diagnosis and subtyping of BCC
AI-assisted Analgesia Copilot System for Proper Management of Nociception
Trial ID: NCT07253012
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-01-29
Completion date: 2027-10-27
Sponsor: Pontificia Universidad Catolica de Chile
Conditions: Nociception; Artificial Intelligence (AI); Target Controlled Infusion (TCI); Remifentanil Consumption
Interventions: Hemodynamic monitor, BIS, TOF, ANI, anesthesia machine and infusion pumps; SEASCAPE
Locations: Division de Anestesiologia; Hospital Clinico UC Christus
Countries: Chile; Chile
Enrollment: 150
Age range: None - None
Sex: ALL

Summary:
The primary objective of the SEASCAPE project is to design, develop, and to apply a clinical implementation tool of a machine learning (ML) and artificial intelligence (AI)-based co-pilot system for the real-time monitoring and control of nociception during general anesthesia (GA).

The ultimate clinical purpose is to optimize individualized pain management by achieving precise titration of intravenous opioids (specifically remifentanil), thereby minimizing the incidence of over- and under-dosing. This optimization is projected to enhance patient outcomes, reduce opioid-related complications, and improve overall cost-effectiveness of anesthetic procedures.

The main scientific question guiding this work is: Can a novel algorithm be generated and validated to provide superior analytical precision for analgesic management by reliably differentiating genuine nociceptive responses from confounding physiological variables-such as inadequate neuromuscular blockade or changes in depth of anesthesia-thereby significantly improving the clinical decision-making framework for intraoperative nociception control? This project addresses the recognized challenge in anesthesiology: defining an objective measure to quantify nociception and antinociception during GA.

Study Population: Patients scheduled for elective surgical procedures requiring general anesthesia (GA).

Existing Intervention: The standard anesthetic regimen includes continuous intravenous infusion of the remifentanil for intraoperative analgesia, typically governed by a Target Controlled Infusion (TCI) system utilizing a pharmacokinetic/pharmacodynamic (PK/PD) model (Eleveld TCI model).

Project Focus: The research seeks to improve the accuracy and efficacy of this existing analgesic strategy by integrating a multivariate patient data stream with the newly developed SEASCAPE co-pilot AI. This aims to refine the remifentanil dose predictions beyond the current TCI model's capabilities, personalized system.

Eligibility criteria:
Inclusion Criteria:

* Patients scheduled for elective surgery with general anesthesia.
* Surgeries scheduled to last at least two hours.

Exclusion Criteria:

* Patients undergoing emergency surgery.
* Pregnant women.
* Presence of a mental or intellectual disability before the hospitalization.
* Drug dependence.
* Surgeries scheduled for more than 4 hours.
* Intraoperative complications requiring changes in routine behavior.

Outcomes:
To develop and implement the SEASCAPE
Unified platforM for a Better integRal Evaluation of MyeLodyspLastic Syndromes in SpAin-Strategy for Unraveling Personalized genoMic Medicine in Public heAlth System (UMBRELLA-SUMMA)
Trial ID: NCT06379945
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-01-01
Completion date: 2026-12-31
Sponsor: Instituto de Investigación Biomédica de Salamanca
Collaborators: Carlos III Health Institute
Conditions: Myelodysplastic Syndromes
Locations: Fundación Instituto de Investigación Germans Trias i Puyol; Hospital Universitario Vall d´Hebron; Clínica Universitaria de Navarra; Complejo Asistencial Universitario de Salamanca
Countries: Spain; Spain; Spain; Spain
Enrollment: 300
Age range: 18 Years - None
Sex: ALL

Summary:
Myelodysplastic Syndromes (MDS) are heterogeneous clonal diseases characterized by difficult diagnosis, complex prognostic stratification and unsatisfactory treatment. Based on that, UMBRELLA SUMMA aims to provide better clinical management and personalized medicine to MDS patients in Spain through improving diagnosis (1), prognosis (2 and 3), and treatment (2), and facilitating future investigations (4) of the disease.

More concretely, we propose: 1. The application of new technologies such as Optical Genome Mapping (OGM) in the diagnosis of those MDS cases whose cytogenetic alterations cannot be identify by other methods, as well as the implementation of this technology using peripheral blood avoiding more invasive methods for patients. 2. To provide all Spanish Group of MDS (GESMD) members who require it with the newly prognostic stratification of their patients (IPSS-M) by making Next Generation Sequencing (NGS) accessible for all of them. 3. Validate and improve a new prognostic system (AIPSS-MDS) previously developed within the GESMD, thanks to artificial intelligence, one of the tools with the most projection in the field of medicine currently. 4. To build and register ISCIII collections of cells, genetic material and/or plasma from all prospective MDS patients.

On the other hand, the dynamics of coexisting mutations in a specific context of chromosomal abnormalities could be defining the clinical fate of each patient. Based on that, the IBSAL team recently proposed three models of MDS evolution based on NGS data from three different cytogenetic subgroups: normal karyotype, trisomy 8 and 5q deletion. The IBSAL proposal aims to deepen into the pathophysiological mechanisms of MDS evolution in these three models through in vitro and in vivo functional studies and single-cell multiomics approaches.

Eligibility criteria:
Inclusion Criteria:

* Patients over 18 years old
* Patients with a confirmed diagnosis of MDS by cytogenetics and/or morphological analysis
* Patients with complete clinical data
* Patients who sign the informed consent

Exclusion Criteria:

* Patients under 18 years old
* Patients who do not sign the informed consent

Outcomes:
Structural variants and complex rearrangements in MDS; Mutations in MDS; Overall survival and leukemia-free survival in patients with MDS; ISCIII collections of viable samples
Efficacy and Safety of Lactobacillus Plantarum and Pediococcus Acidilactici as Co-adjuvant Therapy for Reducing the Risk of Severe Disease in Adults With SARS-CoV-2 and Its Modulation of the Fecal Microbiota: A Randomized Clinical Trial
Trial ID: NCT04517422
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-08-19
Completion date: 2021-02-02
Sponsor: AB Biotics, SA
Collaborators: Innovacion y Desarrollo de Estrategias en Salud; Hospital General Dr. Manuel Gea González; Hospital Angeles del Pedregal
Conditions: SARS-CoV Infection
Interventions: Probiotics; Placebo
Locations: Hospital General Dr. Manuel Gea Gonzalez
Countries: Mexico
Enrollment: 300
Age range: 18 Years - 60 Years
Sex: ALL

Summary:
Clinical research focused to evaluate the effect as coadyuvant of a combination of L. plantarum and P. acidilactici in adults positive for SARS-CoV-2 with mild clinical COVID-19 symptoms. Main objective is to evaluate how this combination of probiotics reduce the risk to progress to moderate or severe COVID and associated advantages such as reduce the risk of death. Adittionnally this RCT is launching to explore the benefits of this combination of strains to modulate fecal microbiome and explore how this correlate with clinical improvement.

Eligibility criteria:
Inclusion Criteria:

* Women or men, \>18y to 60 years
* RTq-PCR to COVID positive
* Presence of cough, fever, dyspnoea, or headache, onset \<= 7 days
* Mild severity of COVID-19
* Peripheral Oxygen Saturation (SpO2) \>90%
* Able to read, understand and sign the informed consent
* To have at least one family member to collaborate with the follow-up in the clinical findings at beginning and throughout of the study.

Exclusion Criteria:

* Severe Obesity (BMI\>40)
* Uncontrolled Type II diabetes (HbA1C \>8.0)
* Uncontrolled systolic hypertension (\>160mmdeHg)
* Acute pancreatitis
* Chronic diarrhea or constipation
* Inflammatory bowel disease
* Blood clotting disease
* Immunosuppression derived from Cancer, post-transplantation, auto-immune disease or HIV
* Severe and active seasonal allergies
* Pregnancy or lactation
* Glucose 6P-dehydrogenase deficiency
* Regular use of probiotic or antibiotic within 2 weeks before entering the trial
* Severe or uncontrolled Chronic Respiratory Diseases (Asthma, COPD or Cystic Fibrosis)

Outcomes:
Severity progression of COVID-19; Stay at ICU; Mortality ratio
A Multi-Reader Multi-Case Study for Evaluating the Impact of Legit.Health Plus Device on the Healthcare Practitioners' Assessment of Skin Lesions
Trial ID: NCT07428954
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-06-01
Completion date: 2024-10-10
Sponsor: AI Labs Group S.L
Conditions: Dermatitis; Melanoma (Skin Cancer); Alopecia; Urticaria; Granuloma Annulare; Seborrheic Keratoses; Herpes; Tinea; Psoriasis; Onychomychosis; Acne; Pressure Ulcer (PU); Nevus
Interventions: AI-based medical device for aided diagnosis in Dermatology
Locations: AI Labs Group S.L.
Countries: Spain
Enrollment: 16
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to determine if an artificial intelligence (AI) medical device can help doctors more accurately identify a wide variety of skin conditions and improve the efficiency of patient consultations. While many patients visit primary care for skin issues, general doctors may sometimes have different opinions from specialists, which can lead to delays in getting the right treatment.

The researchers hypothesized that using the AI tool would increase the true diagnostic accuracy of healthcare professionals for multiple skin conditions. To test this, 16 doctors (including 10 general practitioners and 6 dermatologists) evaluated 29 different medical images.

For each case, the doctors followed a structured process:

* Initial Assessment: Doctors first gave a diagnosis based only on the patient's image and medical history.
* AI Support: Doctors were then shown the AI's top five suggested diagnoses and confidence levels to see if they wished to adjust their final decision.
* Clinical Utility: Doctors also indicated if the patient required a specialist referral and if the case could be handled through a remote (online) consultation.

The primary question the study tried to answer was whether AI support could significantly improve correct diagnoses across 13 different types of skin pathologies-ranging from common rashes to skin cancer-while also making the consultation process faster and more effective for both doctors and patients.

Eligibility criteria:
Inclusion Criteria:

* Board-certified primary care practitioners and dermatologists, regardless of their professional experience.
* High-quality images of patients with different skin conditions.

Exclusion Criteria:

* Low-quality images of patients which can not be properly analyzed.

Outcomes:
Diagnostic Accuracy for Multiple Dermatological Conditions with and without Artificial Intelligence Support
The Glaucoma and Retinopathy Screening Study (GRaSS)
Trial ID: NCT06882356
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-07-16
Completion date: 2030-09
Sponsor: Massachusetts Eye and Ear Infirmary
Collaborators: Centers for Disease Control and Prevention
Conditions: Glaucoma
Interventions: AI-based glaucoma screening; IOP measurement; Virtual Reality Visual Field Testing
Locations: Brigham and Women's Primary Care Associates of Longwood; MGH Chelsea HealthCare Center; NSPG Lynn; NSPG Salem; NSPG Saugus
Countries: United States; United States; United States; United States; United States
Enrollment: 2000
Age range: 40 Years - None
Sex: ALL

Summary:
The goal of this clinical trial is to learn if a new screening approach including an artificial intelligence algorithm that analyzes fundus photographs, measurement of eye pressure and visual field testing works to screen for glaucoma.

Participants will:

Have an image of their fundus (back of the eye) taken as part of their diabetic eye screening Have a measurement of their eye pressure If needed, have a test of their side vision using a headset

Eligibility criteria:
* Individuals with diabetes undergoing AI-based screening for diabetic retinopathy using the LumineticsCore (Digital Diagnostics) system for clinical care at primary care centers.
* Individuals who are able and willing to provide informed consent for participation in the study.

Outcomes:
Glaucoma detection
Early Detection of Cancer in Cases of Barrett's Esophagus Using Robotic Endoscopy Image Analysis Through Deep Image Retrieval
Trial ID: NCT04478929
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-08
Completion date: 2021-08
Sponsor: Leigh Taylor
Collaborators: Cancer Research UK
Conditions: Barrett Esophagus
Interventions: Gastroscopy
Locations: NBT, Southmead Hospital
Countries: United Kingdom
Enrollment: 300
Age range: 18 Years - None
Sex: ALL

Summary:
Barrett's oesophagus is a condition in which the area between the oesophagus and stomach no longer closes, allowing acidic contents of the stomach to enter the oesophagus and damage the lining. The project aims to assist clinicians by offering informed biopsy process, in which the system presents the operator with clinical outcomes of patients with visually similar GI tracts. The project goal is to assess the use of artificial intelligence-based similarity detection systems to better inform biopsy placement, increasing the reliability of bi-yearly inspections.

Eligibility criteria:
Inclusion Criteria:

* Adult
* Attending Southmead Hospital for on OGD
* Has capacity
* Initially any indication, later patients with known Barrett's oesophagus

Exclusion Criteria:

* Children (\<18)
* Patients without capacity
* Urgent and emergency procedures
* Limited English

Outcomes:
gastroscopy image data set
Sleep in Late Pregnancy - Artificial Intelligence Development for the Detection of Disturbances and Disorders (SLeeP AID4): An Observational, In-Home, Single-Group, Feasibility Study
Trial ID: NCT05376475
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-07-06
Completion date: 2024-06-30
Sponsor: Shiphrah Biomedical Inc.
Collaborators: Mitacs; University of Toronto
Conditions: Pregnancy Related; Sleep; Sleep-Disordered Breathing; Sleep Disturbance; Sleep Disorder
Interventions: Infrared video-audio camera; Level III home sleep apnea test; Maternal-fetal transabdominal electrocardiographic heart rate monitor; Pressure sensing mat
Locations: University of Toronto
Countries: Canada
Enrollment: 60
Age range: 18 Years - 50 Years
Sex: ALL

Summary:
SLeeP AID4 is a single-group, single-arm, Canada-wide, non-randomised, unblinded, in-home, observational study to prospectively collect and build a data set of natural sleep behaviour and physiology in maternal-fetal dyads (and bed partners, if applicable) in the third trimester of pregnancy in the home setting and subsequently investigate the feasibility of using computer vision technology (CVT) to monitor sleep during and across the third trimester of pregnancy in the home setting for research purposes by eliciting participants' attitudes toward CVT and training, validating, and testing a CVT model to accurately, unobtrusively, non-invasively, and objectively detect and quantify various sleep behaviours, disturbances, and disorders.

Eligibility criteria:
Participant Inclusion Criteria:

* Healthy (see American Society of Anesthesiologists Physical Status \[ASA-PS\] classification; participant must be ASA-PS class II or lesser)
* Have a singleton pregnancy
* Be in the third trimester (gestational age between 28 weeks and 0 days through 40 weeks and 6 days, inclusive, by first-trimester ultrasound)
* Live in a home with a 2.4 GHz Wi-Fi network
* Sleep in a bed at night (i.e., not a La-Z-Boy chair or similar)

Participant's Bed Partner (if applicable) Inclusion Criteria:

* Sleep in the same bed as the pregnant study participant.
* Healthy (see American Society of Anesthesiologists Physical Status \[ASA-PS\] classification; participant must be ASA-PS class II or lesser)

Participant Exclusion Criteria:

* Non-English speaking, reading, or writing
* ASA-PS class III or greater

Participant's Bed Partner (if applicable) Exclusion Criteria:

* Non-English speaking, reading, or writing
* ASA-PS class III or greater

Outcomes:
Maternal supine sleep position
Comparison of the Hologic Genius Digital Diagnostics System With the Liquid-based Cytology (LBC) Manual Microscopic Approach
Trial ID: NCT04965857
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-09-01
Completion date: 2023-12-31
Sponsor: Hologic Deutschland GmbH
Conditions: Cervical Cancer Screening
Interventions: Hologic's Genius Digital Diagnostics System
Locations: Zotz Klimas Partner für Diagnostik und Prävention MVZ Düsseldorf-Centrum
Countries: Germany
Enrollment: 3000
Age range: 35 Years - 90 Years
Sex: FEMALE

Summary:
Quality assurance of the laboratory examinations. Prospective non-interventional study with ThinPrep slides collected during the German Co-Screening Program and routinely sent to the lab to prospectively evaluate the clinical performance of the Hologic Genius Digital Cytology (DC) system vs. the liquid-based cytology (LBC) manual microscopic approach

Eligibility criteria:
Inclusion Criteria:

* LBC ThinPrep® samples are collected by gynaecologists and sent routinely to cytology laboratory Zotz \& Klimas as part of the German Cervical cancer screening program.

Outcomes:
Sensitivity and specificity of detection of pre-cancerous lesions using Hologic Genius Digital Cytology Imaging scanner.