moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Prediction of Length of Hospital Stay in Hip Fracture Patients After Post-Anesthesia Care Unit Using Artificial Intelligence
Trial ID: NCT06392048
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-05-25
Completion date: 2025-05-07
Sponsor: Kocaeli University
Conditions: Hip Fractures
Locations: Kocaeli University
Countries: Turkey (Türkiye)
Enrollment: 366
Age range: 65 Years - 100 Years
Sex: ALL

Summary:
With increasing life expectancy, the elderly population is growing. Hip fractures significantly increase morbidity and mortality, particularly within the first year, among elderly patients. Managing anesthesia in these elderly patients, who often have multiple comorbidities, is challenging. Identifying perioperative factors that can reduce mortality will benefit the perioperative management of these patients.

The aim of this study is to develop and validate a machine learning based model to predict the length of hospital stay for hip fracture patients after PACU. Different machine learning algorithms such as R language Gradient Boosting, Random Forest, Artificial Neural Networks and Logistic Regression will be used in the study and the best performing model will be determined. In addition, the prediction mechanism of the model will be examined with SHAP analysis and its applicability in clinical decision processes will be evaluated. Thus, by predicting the length of hospital stay, clinicians will be enabled to manage patient care processes more effectively.

Eligibility criteria:
Inclusion Criteria:

* Patients who underwent hip fracture surgery at our institution between 2017 and 2024
* Patients aged 65 years or older
* Patients with hip fractures resulting from a low-energy trauma (simple fall from standing height)

Exclusion Criteria:

* Patients with pathological hip fractures due to malignancy
* Cancer patients with multiple organ metastases
* Patients who underwent revision hip fracture surgery

Outcomes:
Prediction of Length of Hospital Stay in Hip Fracture Patients After Post-Anesthesia Care Unit Using Artificial Intelligence
Development of an Artificial Intelligence Model for Predicting Intraoperative Changes in Cardiac Output Using Capnography During General Anesthesia
Trial ID: NCT07061548
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-07-03
Completion date: 2025-12-31
Sponsor: Samsung Medical Center
Conditions: General Anesthesia Using Endotracheal Intubation
Interventions: No Intervention: Observational Cohort
Locations: Samsung Medical Center
Countries: South Korea
Enrollment: 2005
Age range: 19 Years - 75 Years
Sex: ALL

Summary:
Conventional monitoring of cardiac output requires an invasive procedure and an additional device, which can lead to increased risk and cost. Investigators developed an artificial intelligence algorithm to predict intraoperative changes in cardiac output using capnography in patients undergoing surgery under general anesthesia.

Eligibility criteria:
Inclusion Criteria:

* Elective surgery under general anesthesia
* Adult patients (18 \< age \< 76)
* Patients who were monitored invasive arterial blood pressure (waveform) and capnography (numeric)

Exclusion Criteria:

* Emergency surgery
* Cardiovascular and thoracic surgery
* Known Asthma and Chronic obstructive pulmonary disease (COPD)
* Preoperative pulmonary function test (PFT) abnormality over moderate grade
* Intraoperative monitoring duration less than 30 minutes

Outcomes:
Predictability of algorithm
From Oocyte to Embryo: Analysis of Mechanics of Human Pre Implantation Development
Trial ID: NCT06408389
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-11
Completion date: 2028-11
Sponsor: Assistance Publique - Hôpitaux de Paris
Collaborators: Collège de France; Institut Curie; Centre National de la Recherche Scientifique, France; URC-CIC Paris Descartes Necker Cochin
Conditions: Infertility
Enrollment: 1500
Age range: 18 Years - 43 Years
Sex: ALL

Summary:
The time-lapse is a closed tri-gas incubator of the latest generation that provides optimal and stable culture conditions for the culture of embryos in In Vitro Fertilization (IVF). The integration of a camera within this incubator allows for continuous image capture, thus facilitating the monitoring of the entire embryonic development, from the day of fertilization to the moment of transfer into the uterus.

The contribution of the time-lapse system allows an evaluation of the embryos not only by their morphology, but also by their cell division kinetics, both being direct markers of cell mechanics. Together, these morpho-kinetic data finally allow for the best identification of embryos with greater implantation potential. Time-lapse imaging represents a further step towards an objective assessment of the embryo, but inter- and intra-embryologist variations in annotations partly compromise this objectivity. In addition, many decision algorithms based on the evaluation of morpho-kinetic parameters have been developed, but the lack of reproducibility from one Assisted Reproductive Technology (ART) center to another is a hindrance to the generalization of any particular algorithm. The aim of this retrospective study is to determine morpho-kinetic factors predictive of implantation using machine learning and to link these factors to human embryo mechanistic properties.

Eligibility criteria:
Inclusion Criteria:

* Couples enrolled in an IVF process and with embryos cultured in time-lapse
* Couples informed non opposed to research

Exclusion Criteria:

* Couples opposed to research
* Couples under curator or tutorship
* Couples under state xxx

Outcomes:
Prediction of embryo implantation by the machine learning algorithm from embryo morphokinetic parameters and patient health data.
An Artificial Intelligence Assisted Telehealth Intervention to Promote Self-Management in Patients With Type 2 Diabetes Mellitus (Qualitative Interview)
Trial ID: NCT03868813
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2019-07-01
Completion date: 2021-07-01
Sponsor: University of Alabama at Birmingham
Conditions: Diabetes Mellitus
Interventions: Semi-structured Interview
Locations: University of Alabama at Birmingham
Countries: United States
Enrollment: 20
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
This protocol involves a qualitative study of the needs/preferences of people with diabetes and health coaches regarding a telehealth coaching platform for glycemic control in people with diabetes.

Eligibility criteria:
Inclusion Criteria:

Health Coaches Group:

(i) age between 18 and 80 years (ii) fluent in English (iii) work in or carry expertise in physical activity, nutrition/diet or health behavior.

People with Diabetes Group (Patient/Caregiver Dyads):

(i) age between 18 and 80 years (ii) fluent in English (iii) have Type-II diabetes.

Exclusion Criteria:

None

Outcomes:
Users' Expectation from a Health Coaching System
AYA-PeerConnect: A Communication Coaching Intervention for Adolescent/Young Adult Cancer Survivors (AYACS)
Trial ID: NCT07454460
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-04-01
Completion date: 2026-06-30
Sponsor: Milton S. Hershey Medical Center
Conditions: Communication; Anxiety; Depression; Social Isolation
Interventions: Asynchronous coaching and artificial intelligence (AI) Yoodli coach sessions
Enrollment: 10
Age range: 18 Years - 35 Years
Sex: ALL

Summary:
This research is being done to test a communication coaching intervention pilot designed to improve communication quality between adolescent/young adult cancer survivors (AYACS) and their friends/peers. The results of the study will help to understand how to best support adolescent/young adult cancer survivors, and may inform the development or future implementation of similar programs for cancer survivor populations.

Eligibility criteria:
Inclusion Criteria:

* Participant is currently ≥18-35 years old
* Participant is receiving cancer-directed care at Penn State Cancer Institute or Penn State Children's Hospital
* Participant has completed all cancer-directed therapy and is at least 1 year from end of cancer treatment (based on date of last cancer-directed treatment)
* Participant has access to a computer with internet capability
* Participant is fluent in written and spoken English

Exclusion Criteria:

* Participant is not fluent in written and spoken English
* Participant with cognitive inability to participant in the study as determined by the study team
* Participants with history of relapsed cancer

Outcomes:
Intervention Feasibility (Quantitative); Intervention Feasibility (Qualitative); Intervention Acceptability (Quantitative); Intervention Acceptability (Qualitative); Communication quality Multiple Goals Score; Communication quality Multiple Goals Score
Multicentric Study for External Validation of a Deep Learning Model for Mammographic Breast Density Categorization
Trial ID: NCT05021055
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-09
Completion date: 2022-07
Sponsor: Hospital Italiano de Buenos Aires
Conditions: Breast Cancer
Enrollment: 277
Age range: None - None
Sex: ALL

Summary:
The correct categorization of breast density is essential to adapt the diagnostic examination to the needs of each patient. Assessment of breast density is performed visually by radiologists. Some authors have detected that this method involves considerable intra and interobserver variability. On the other hand, automated systems for measuring breast density are becoming more and more frequent. Machine learning is a domain of Artificial Intelligence, which comprises the process of developing systems with the ability to learn and make predictions using data. These systems are designed to aid healthcare professional decision making. In the present work, the multicenter study of external validation of a tool based on deep learning for the categorization of mammographic breast density is proposed.

Eligibility criteria:
Inclusion Criteria:

* Mammograms included in the study should meet the following criteria:

  * Female patients of 40 years of age or more.
  * To have at least one screening mammography exam performed at Saint John's
  * Cancer Institute during the study period. These exams will be included regardless of the brand of the mammography equipment.
  * Mammograms should be performed with digital equipment.

Exclusion Criteria:

* Mammograms with the following criteria will be excluded from the study:

  * Patients with gigantomastia, defined by the need for more than one image of each mammographic view (mediolateral oblique and craniocaudal) to evaluate the entire breast volume.
  * Patients with breast implants.
  * Patients with a history of breast surgery.

Outcomes:
Agreement between the majority report and Artemisia´s categorization of dense breasts/non-dense breasts; Agreement between the majority report and Artemisia in each one of the four breast density categories
Comparison of the Diagnostic Potential of Colonoscopy, and Artificial Intelligence-assisted Fecal Microbiome Testing for Colon Cancer
Trial ID: NCT05795725
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-05-01
Completion date: 2024-05-31
Sponsor: Istanbul Medipol University Hospital
Collaborators: Bozyaka Training and Research Hospital; Tepecik Training and Research Hospital; SB Istanbul Education and Research Hospital; Bursa City Hospital; Izmir Metropolitan Municipality Esrefpasa Hospital
Conditions: Colon Cancer; Microbiota; Colonoscopy
Interventions: Artificial Intelligence-assisted Fecal Microbiome Testing; Colonoscopy
Locations: Medipol University Esenler Hospital
Countries: Turkey (Türkiye)
Enrollment: 1000
Age range: 18 Years - 70 Years
Sex: ALL

Summary:
The goal of this clinical trial is to evaluate the diagnostic potential of Artificial Intelligence-assisted Fecal Microbiome Testing for the diagnosis of colon cancer. The main question it aims to answer is:

• Is Artificial Intelligence-assisted Fecal Microbiome Testing a reliable screening test for colon cancer?

Participants will be asked to provide fecal samples to be analyzed with next-generation sequencing techniques.

If there is a comparison group: Researchers will compare the diagnostic performance of AI-assisted Fecal Microbiome Testing with colonoscopy to see the correlation between the results of both interventions.

Eligibility criteria:
Inclusion Criteria:

* over 18 years not pregnant not meeting any of the exclusion criteria Voluntary consent form signer \* Indications for colonoscopy:

Colorectal cancer or adenomatous polyp in first-degree relatives Patients followed for more than 8 years with ulcerative colitis, Crohn's Disease, or individuals with a history of hereditary polyposis or non-polyposis syndrome. In these groups, the screening procedure should be started from the age of 40.

It is a population-based screening that begins at age 50 and ends at age 70 for all men and women (50 and 70 years will be included). However, especially in this group of patients;

Male patients presenting with iron deficiency anemia Female patients over 40 years of age presenting with iron deficiency anemia Patients with positive occult blood in stool in screening programs Patients presenting with rectal bleeding Patients with defecation irregularity, weight loss

Exclusion Criteria:

* under 18 years old
* Pregnant or planning to become
* Have another known diagnosis of gastrointestinal disease
* Abdominal surgery other than appendectomy or hysterectomy history
* Psychiatric comorbidity
* Chronic diseases that will affect the microbiome (cancer, diabetes, cardiovascular disease, liver diseases, neurological diseases, etc.)
* Use of drugs that may affect digestive function (including use in the last 4 weeks), probiotics, narcotic analgesics, lactulose (prebiotics) in the 4 weeks before the study
* Patients taking dietary supplements will not be included in the study.

Outcomes:
The diagnostic accuracy of the AI-assisted fecal microbiome testing in detecting colon cancer compared to colonoscopy
A Study on Ophthalmic Multimodal AI-Assisted Medical Decision-Making Based on Imaging and Electronic Medical Record Data
Trial ID: NCT06755190
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-12-20
Completion date: 2025-05
Sponsor: The Eye Hospital of Wenzhou Medical University
Conditions: Ocular Diseases
Interventions: Diagnostic Test: AI-Based Diagnostic and Prognostic Model for Ocular Diseases
Locations: ZhuHai Hospital, zhuhai, guangdong; First Affiliated Hospital of Wenzhou Medical University; Second Affiliated Hospital of Wenzhou Medical Universit; The Eye Hospital of Wenzhou Medical University; Macau University of Science and Technology Hospital
Countries: China; China; China; China; Macau
Enrollment: 5000000
Age range: None - None
Sex: ALL

Summary:
This is a multi-center, retrospective clinical study designed to evaluate the application and effectiveness of an AI-assisted medical decision support system, leveraging multimodal data fusion, in ophthalmic clinical practice.

Eligibility criteria:
Inclusion Criteria:

1.All patients who have received treatment at multiple centers, including The Eye Hospital of Wenzhou Medical University, First Affiliated Hospital of Wenzhou Medical University, Second Affiliated Hospital of Wenzhou Medical University, ZhuHai Hospital, and Macau University of Science and Technology Hospital.

2.Availability of comprehensive electronic health records (EHR), including: Ophthalmic images (e.g., fundus photography, OCT, or slit-lamp images). Electronic medical records (e.g., diagnosis, treatment, and follow-up notes). Examination results (e.g., visual acuity, intraocular pressure, or laboratory tests). 3.Patients with a clear and confirmed diagnosis of one or more ocular diseases. 4.Patients with sufficient follow-up records to allow assessment of disease progression or prognosis, if applicable.

1. All ophthalmology patients who have previously received treatment at the Department of Ophthalmology, the Eye Hospital of Wenzhou Medical University, First Affiliated Hospital of Wenzhou Medical University, Second Affiliated Hospital of Wenzhou Medical University, Zhuhai People's Hospital, and the University Hospital.
2. Availability of comprehensive electronic health records (EHR), including: Ophthalmic images (e.g., fundus photography, OCT, or slit-lamp images). Electronic medical records (e.g., diagnosis, treatment, and follow-up notes). Examination results (e.g., visual acuity, intraocular pressure, or laboratory tests).
3. Patients with a clear and confirmed diagnosis of one or more ocular diseases.
4. Patients with sufficient follow-up records to allow assessment of disease progression or prognosis, if applicable.

Exclusion Criteria:

1. Incomplete or missing critical EHR components.
2. Cases with ambiguous or unverified diagnoses that cannot be clearly categorized.
3. Duplicated or redundant data from the same patient.

Outcomes:
Area Under the Curve (AUC); Sensitivity; Accuracy Accuracy Accuracy; Specificity; False Positive Rate; False Negative Rate; Postoperative Complication Rate; Recurrence Risk Rate; Survival Rate; Effectiveness of Decision Support; Decision Time Efficiency
Artificial Intelligence Neuropathologist - Automated CNS Tumor Pathological Diagnosis Based on Deep Learning
Trial ID: NCT05300113
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-05-01
Completion date: 2024-12-01
Sponsor: Huashan Hospital
Collaborators: United Imaging Healthcare
Conditions: CNS Tumor; Neuropathology
Locations: Hushan Hospital, Fudan University
Countries: China
Enrollment: 1000
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
CNS tumor requires biopsy for pathological diagnosis, which is known as the "golden standard". We would like to achieve automated classification of brain tumors based on deep learning in digital histopathology images and molecular pathology results. We expect to develop an assistant system (including software and hardware), to help pathologists during their diagnosis for CNS tumor.

Eligibility criteria:
Inclusion Criteria:

The participants diagnosed with brain cancer by diagnosis of WHO 2016 classification of CNS tumors.

Exclusion Criteria:

Voluntarily quit

Outcomes:
Automated histopathological diagnosis outcome (software development); Positioning platform for microscope (hardware development); Combine automated molecular pathological diagnosis
Mixed Molecular Clinical Index (MMCI) in Diffuse Large B-cell Lymphoma (DLBCL)
Trial ID: NCT04300101
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2020-05-14
Completion date: 2028-05
Sponsor: Istituto Romagnolo per lo Studio dei Tumori Dino Amadori IRST S.r.l. IRCCS
Conditions: Diffuse Large B Cell Lymphoma
Interventions: Prospective cohort; Retrospective cohort
Locations: Irst Irccs; Ospedale S. Maria delle Croci RAVENNA; L'Azienda Ospedaliero-Universitaria Di Bologna Policlinico S. Orsola-Malpighi; Ospedale Infermi
Countries: Italy; Italy; Italy; Italy
Enrollment: 300
Age range: 18 Years - None
Sex: ALL

Summary:
This is a prospective and retrospective observational study. The primary objective is to identify new prognostic biomarkers for DLBCL patients in terms of progression-free survival (PFS) and able to add predictive capacity to recognized important clinical factors.

The secondary objectives are:

* to identify new biomarkers associated with overall survival (OS) and objective response rate (ORR)
* to characterize tissue and circulating immune microenvironment of DLBCL patients by bulk and single cell transcriptomics;
* to assess the correlation between the expression of immune checkpoint genes and mRNA signature;
* to describe the mutational status of a panel of genes relevant to DLBCL pathogenesis;.
* to assess the correlation between protein expression, mutational status and the messenger RNA (mRNA) signature.
* to investigate the association between radiomic features obtained from PET images and patient and tumour characteristics and clinical outcomes (PFS, OS, ORR).

For each enrolled patient, immunohistochemical determinations will be performed: Cell of origin (COO) (Germinal Cell -GC- or activated B-cell - ABC- type according with Hans algorithm ), evaluation of cluster of differentiation antigen 20 (CD20), cluster of differentiation antigen 5 (CD5), cluster of differentiation antigen 10 (CD10), Bcl6, Bcl2 (cut off\>50%), Multiple Myeloma 1 / Interferon Regulatory Factor 4 protein (MUM1/IRF4), c-myc (cut off\>40%) and Ki67, fluorescence in situ hybridization (FISH) for c-myc and if rearranged, for Bcl2 e Bcl6 ). Moreover, paraffin embedded (FFPE) tumor specimens will be collected for RNA extraction and mRNA expression mutational and proteomics analysis, centralized at IRST-IRCCS.

Eligibility criteria:
Inclusion Criteria for the prospective cohort:

* New diagnosis of High grade Diffuse large B cell Lymphoma undergoing first line standard treatment;
* Signed written informed consent;
* Availability of FFPE sample.

Inclusion Criteria for the retrospective cohort:

* Diagnosis of High grade Diffuse large B cell Lymphoma from 2011 to 2017;
* Availability of FFPE sample and clinical data.

Exclusion Criteria:

* Patients included in clinical trials.

Outcomes:
identification of new prognostic biomarkers
Computed Tomography for Coronavirus Disease 19 Diagnosis
Trial ID: NCT04355507
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-03-01
Completion date: 2020-10-16
Sponsor: Assistance Publique - Hôpitaux de Paris
Collaborators: Institut National de la Santé Et de la Recherche Médicale, France; GE Healthcare; Orange healthcare; TheraPanacea; URC-CIC Paris Descartes Necker Cochin
Conditions: COVID-19
Interventions: Chest computed tomography (CT); Reverse-transcription polymerase chain reaction (RT-PCR)
Locations: Cochin Hospital
Countries: France
Enrollment: 10735
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of this study is to build a large dataset of Computed Tomography (CT) images for identification of accurate CT criteria and development of deep learning-based solutions for diagnosis, quantification and prognostic estimation.

Eligibility criteria:
Inclusion Criteria:

* Age\>18 years
* CT examination performed for suspicion or follow-up of COVID-19
* Non opposition for use of data

Exclusion Criteria:

* Unavailability of RT-PCR results for SARS-Cov-2
* Failure of CT image anonymized export

Outcomes:
Predictive values of CT criteria
Pre-anesthesia Imaging-based Respiratory Assessment and Analysis
Trial ID: NCT06270797
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-03-01
Completion date: 2026-12-31
Sponsor: Kaohsiung Medical University Chung-Ho Memorial Hospital
Conditions: Clinical Decision Support System
Interventions: intubation for general anesthesia
Locations: Kaohsiung Medical University Chung-Ho Memorial Hospital
Countries: Taiwan
Enrollment: 30000
Age range: 18 Years - 85 Years
Sex: ALL

Summary:
This study is to establish a preoperative respiratory imaging assessment database and develop a difficult intubation risk prediction model and further risk analysis. We attempt to construct it into a pre-anesthesia intubation risk assessment software as the clinical decision support system.

Eligibility criteria:
Inclusion Criteria:

* Patients undergoing general anesthesia
* Patients who can undergo pre-anesthetic consultation and airway examination.

Exclusion Criteria:

* Patients unable to undergo pre-anesthetic consultation and airway examination.
* Patients requiring emergency surgery.
* Vulnerable populations.

Outcomes:
A pre-anesthesia evaluation; Perform non-invasive imaging capture.; difficult intubation prediction