moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Prospective Evaluation of Machine Learning Algorithms to Predict Postoperative Pain in Pedaitric Ambulatory Surgical Procedures
Trial ID: NCT07274995
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-08-01
Completion date: 2026-11-30
Sponsor: Başakşehir Çam & Sakura City Hospital
Conditions: Pain, Acute Post-Operative; Ambulatory Surgical Procedures; Pediatric Pain; Pediatric Patient (1m-21y)
Locations: Başakşehir Çam ve Sakura Şehir Hastanesi
Countries: Turkey (Türkiye)
Enrollment: 90
Age range: 1 Year - 3 Years
Sex: ALL

Summary:
This study aims to predict pain after surgery in children of ages 1 to 3 years by using computer programming (machine learning). Participant children will be observed before, during, and after surgery.

Before surgery, the investigators will record each child's age, sex, weight, and the parent's level of anxiety using a short questionnaire (STAI: State Trait Anxiety Inventory).

During surgery, the investigators will note the type of the surgery, how long it takes, and the medication given for pain relief.

After surgery, the child's pain will be checked using the FLACC (Face, Legs, Activity, Cry, Consolability) scale, which assesses the child's face, legs, activity, crying, and how easy they are to comfort. For each assesment the children will be given points from 0 to 2. Pain will be measured 2 times. Firstly when the child reaches to the postoperative recovery room after they are monitorized. Secondly after 30 minutes spending in recovery room. At both times the pain scores and vital signs (pulse pressure and saturation) will be noted. No additional medication or intervention will be done throughout the study.

All information will be stored without names or personal details. A computer model will study 80% of the data and then test itself on the remaining 20% of the collected data to see how well it can predict pain.

Eligibility criteria:
Inclusion Criteria:

* Children aged 1 to 3 years
* scheduled for ambulatory (day-case) surgical procedures under general anesthesia
* American Society of Anesthesiologists (ASA) Physical Status I-II
* informed consent obtained from parent/legal guardian

Exclusion Criteria:

* Known developmental delay or neurological disorder interfering with pain assessment
* Chronic analgesic or sedative medication use
* Emergency surgery cases
* Incomplete data or refusal of parental consent

Outcomes:
Postoperative pain score (FLACC: Face, Legs, Activity, Cry, Consolability)
Development and Evaluation of an Artificial Intelligence Model to Support Read Out of Skin Prick Automated Test Results for the Diagnosis of Aeroallergies
Trial ID: NCT05918354
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-07-10
Completion date: 2024-09-11
Sponsor: Hippocreates
Conditions: Allergy
Interventions: Skin Prick Automated Test
Locations: Clinique Universitaires Saint-Luc; Ziekenhuis Oost Limburg; UZ Leuven; CHU de Liège; GZA ziekenhuizen; Klinikum rechts der Isar
Countries: Belgium; Belgium; Belgium; Belgium; Belgium; Germany
Enrollment: 993
Age range: 18 Years - None
Sex: ALL

Summary:
To improve accuracy and take out human error, Hippo Dx previously developed S.P.A.T., an automated skin prick test. Now, Hippo Dx will develop and evaluate a S.P.A.T. artificial intelligence (AI) medical software to support physicians in the read out of the test results from an automated skin prick test.

In this study 700 images will be collected to develop the AI model, an additional 200 images will be collected to evaluate performance of the AI model. In a last independent cohort of 100 patients, performance of read-out by the physician will be compared with read out by the physician supported by the S.P.A.T. AI medical software.

The primary endpoint is the accuracy of the maximal wheal size measurement by S.P.A.T. AI versus the maximal wheal size measurement by the physician.

It is a prospective multicentric study that will include 1000 study participants from different countries.

Eligibility criteria:
Inclusion Criteria:

\- Adults aged 18 years or above, consulting the outpatient clinic for an allergy diagnosis to aeroallergens will be included.

Exclusion Criteria:

* Skin pathology like chronic or exuberant urticaria, dermographism, chronic dermatitis that needs daily treatment
* Use of antihistaminic medication \< 7 days before the start of the study
* Use of tricyclic antidepressants (antihistamine activity) \< 7 days before the start of the study
* Use of topical corticoids on the forearm \< 7 days before the start of the study
* Use of Omalizumab \< 6 months before the start of the study
* Pregnancy: there is a remote possibility of inducing a systemic allergic reaction that could induce uterine contractions or necessitate the use of epinephrine (thought to cause constriction of the umbilical artery)
* Incapacitated individuals
* Individuals who do not speak the local language (Dutch - French)
* Individuals who cannot read or write

Outcomes:
Equivalency of S.P.A.T. artificial intelligence versus physician
Identification of Benign and Malignant Breast Nodules Using Opalogram Ultrasonography: A Multicenter Study
Trial ID: NCT06468566
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-02-01
Completion date: 2026-02-28
Sponsor: Xin-Wu Cui
Collaborators: Hubei Cancer Hospital; Guangxi National Hospital; First Affiliated Hospital of Kunming Medical University
Conditions: Breast Cancer Female
Interventions: Ultrasound light scattering imaging equipment
Locations: Xin-Wu Cui
Countries: China
Enrollment: 2000
Age range: 18 Years - 85 Years
Sex: FEMALE

Summary:
Ultrasonic light scattering imaging is a new functional imaging technology that combines traditional B-mode ultrasound imaging and light scattering tomography (DOT). It can improve the accuracy of early diagnosis of breast cancer based on the characteristics of abnormal blood supply and oxygen consumption of lesions. This study aims to evaluate the value of ultrasonic light scattering imaging in the differential diagnosis of benign and malignant breast nodules, and to evaluate the consistency between ultrasonic light scattering imaging and examiners in the differential diagnosis of benign and malignant breast nodules.

Eligibility criteria:
Inclusion Criteria:

Had breast lesions detected by ultrasound Age 18 or older Upcoming FNAB or surgery Signing informed consent

Exclusion Criteria:

Patients who had received a biopsy of breast lesion before the ultrasound examination Can not cooperate with the test operation Patients who were pregnant or lactating Patients who were undergoing neoadjuvant treatment.

Outcomes:
Benign breast tumor; Malignant breast tumor; Breast lymph node metastasis; No lymph node metastasis
Development of Predictive Models Based on Artificial Intelligence for the Analysis of the Psychosocial Profile of the Patient With Fibromyalgia on Pain and Severity of the Disease.
Trial ID: NCT04918602
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-06
Completion date: 2021-11
Sponsor: University of Castilla-La Mancha
Conditions: Fibromyalgia
Locations: Hospital General Nuestra Señora del Prado
Countries: Spain
Enrollment: 150
Age range: 18 Years - None
Sex: ALL

Summary:
The primary goal of this research project is to develop different prediction models in fibromyalgia disease through the application of machine learning techniques and to assess the explainability of the results.

As specific objectives the research project intends: to predicting Fibromyalgia severity of patients based on clinical variables; to assess the relevance of social-psycho-demographic variables on the fibromyalgia severity of the patients; to predict the pain suffered by the patients as well as the impact of the fibromyalgia on patient's life; to categorize fibromyalgia group of patients depending on their levels of Fibromyalgia severity.

Eligibility criteria:
Inclusion Criteria:

* Age between 18 and 65 years.
* Fullfilled the 2010 American Collegue of Rheumathology criteria for fibromyalgia.
* Understanding of spoken and written Spanish.

Exclusion Criteria:

* Diagnosed psychiatric pathology.
* Rheumatic pathology not medically controlled.
* Neurological pathologies that make evaluations difficult.

Outcomes:
Pain intensity; Disease severity.; Referred pain area after suprathreshold pressure stimulation.
Randomized Study Of Novel Enuresis Alarm (GoGoband® )Vs Standard Nocturnal Enuresis Alarm (SNEA)
Trial ID: NCT05214131
Status: TERMINATED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-04-29
Completion date: 2025-06-13
Sponsor: Yale University
Collaborators: Southwest pediatric Device Consortium; Food and Drug Administration (FDA)
Conditions: Bedwetting; Nocturnal Enuresis; Urinary Incontinence
Interventions: gogoband; SNEA group
Locations: Yale New Haven Health
Countries: United States
Enrollment: 16
Age range: 6 Years - 21 Years
Sex: ALL

Summary:
Identify the dry night rate in patients using the GoGoband® nocturnal enuresis device vs a standard Pflaundler bedwetting alarm.

Eligibility criteria:
Inclusion Criteria:

1. Provision of signed and dated informed consent form
2. Stated willingness to comply with all study procedures and availability for the duration of the study
3. Male or female, aged 6 to 21 years
4. In good general health as evidenced by medical history and diagnosed with MNE
5. Ability to and be willing to adhere to the treatment regimen. -

Exclusion Criteria:

1. No patient may have had treatment of MNE in the past 6 months whether with medications or alarms
2. Current use of on ADHD medications, Tricyclics, SSRI's, NRI's or any antipsychotic medications.
3. Presence of Autistic Spectrum disorder, ADHD, genetic syndrome associated with developmental or learning disabilities
4. Treatment with another investigational drug or other intervention within last 6 months
5. Any form of Diabetes Mellitus or Diabetes Insipidus
6. No patient with known Chronic renal disease with moderate to severe renal impairment (defined as a creatinine clearance below 50mL/min).
7. No patient with known hyponatremia or a history of hyponatremia.

Outcomes:
Number of Wet Nights
Doctor of Medical Sciences
Trial ID: NCT05628077
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-12-30
Completion date: 2023-04-20
Sponsor: Peking University People's Hospital
Conditions: Breast Cancer; Aromatase Inhibitors; Endocrine Therapy; Pain; Anxiety; Depression; Sleep
Interventions: No intervention
Locations: Peking University People'S Hospital
Countries: China
Enrollment: 1200
Age range: 18 Years - 90 Years
Sex: FEMALE

Summary:
We obtained the occurrence of pain sensation, pain mood, sleep, etc. during endocrine therapy in breast cancer patients through telephone follow-up, and analyzed risk factors through artificial intelligence

Eligibility criteria:
Inclusion Criteria:

\- Breast cancer survivors on aromatase inhibitors

Exclusion Criteria:

* Advanced breast cancer

Outcomes:
Prevalence and risk factors for pain and related adverse reactions among breast cancer survivors on aromatase inhibitors
Baseline Study of Vaginal Microbiota in Healthy Chinses Female Population
Trial ID: NCT04887636
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-05-21
Completion date: 2022-07-15
Sponsor: Peking Union Medical College Hospital
Collaborators: Cancer Institute and Hospital, Chinese Academy of Medical Sciences
Conditions: Vagina Disease; Vaginal Community State Type
Interventions: [None]
Locations: Peking Union Medical College Hospital
Countries: China
Enrollment: 1000
Age range: 20 Years - 45 Years
Sex: FEMALE

Summary:
The reproductive tract microecology is an important part of the human microbial system. Microorganisms and the host reproductive tract environment form a stable symbiotic relationship. When pathogens invade or microecology imbalance，it can lead to serious reproductive tract infection.Reproductive tract infection is one of the most common disease among women，and it is a global social and public health problem. Reproductive tract infection is characterized by high morbidity, low cure rate and high recurrence rate.Data from the studies shows that 40% of Chinese women suffer from reproductive tract infections of varying degrees, that is, at least 200 million. Long-term chronic infections can lead to serious consequences such as infertility, tumor, abortion and premature birth. At present, the diagnostic classification of reproductive tract infection is mainly divided into Aerobic vaginitis（AV), Bacterial vaginosis（BV), Vulvovaginal candidiasis（VVC) and Trichomonas vaginitis（TV). The reproductive tract microbiota is complex, but the clinical classification is relatively simple, which makes the treatment lack of precision. More precise treatment，permanent cure and reducing the recurrence rate are the top priorities in the field of medical and health.

Eligibility criteria:
Inclusion Criteria:

* Women of childbearing age who are physically and mentally healthy, have regular menstruation and are between 20-45 years old;
* Women who are evaluated as normal by vaginal microbiome morphological characterization;
* Women who agree to participate in this study and have signed an informed consent form;
* Those who have full capacity for civil and legal conduct;
* The quality of vaginal samples meets the evaluation requirements of this program.

Exclusion Criteria:

* The sampling quality of vaginal secretions does not meet the research study requirements;
* The quality or format of the slide collection of vaginal smears does not meet the requirements;
* Women with severe physical or mental illness;
* Users of hormone therapy and antibiotic drugs;
* Women with insufficient compliance with this study;
* Women who are participating in other coinciding clinical drug or medical device trials.

Outcomes:
Nugent score; Next generation sequencing
Research on Facial Prediction Technology for Edentulous Implant-Supported Fixed Prostheses Based on Multimodal Data Fusion
Trial ID: NCT06080633
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-06-01
Completion date: 2026-12-01
Sponsor: KU Leuven
Conditions: Edentulous Jaw
Locations: Hongyang Ma
Countries: Belgium
Enrollment: 24
Age range: 50 Years - 100 Years
Sex: ALL

Summary:
According to data from the World Health Organization, approximately 160 million people worldwide are edentulous. The incidence increases with age, and the proportion of edentulous patients is higher in the population aged 60 and above. Loss of teeth or edentulism can affect facial appearance, causing people to feel self-conscious and loss confidence in social situations, and even lead to psychological illnesses. Therefore, edentulous patients not only pay close attention to the recovery of oral function but also attach great importance to facial contour improvement. For a long time, due to technological limitations, clinicians have been unable to depict the changes in facial contour after implant placement for patients before surgery. However, with the development of artificial intelligence technology, deep learning-based methods for predicting soft tissue facial deformation have made this mission a possibility. This study established a multi-modal dataset for edentulous patients before and after implant restoration to lay the foundation for predicting facial contour changes after implant treatment. A graph generative adversarial network based on multi-modal data was proposed to achieve fast and high-precision facial contour prediction. To address the common challenges of slow computation and excessive computational resource consumption in current triangular mesh deformation simulation methods, this project innovatively proposed a graph generative adversarial network that uses multi-modal data and incorporates self-attention mechanisms to achieve fast and high-precision facial contour prediction for edentulous patients after implant restoration.

Eligibility criteria:
Inclusion Criteria:

* Patients with complete edentulism,
* aged 50 years or above,
* in good physical health,

Exclusion Criteria:

* patients who refuse to participate in the study，
* patients who cannot undergo facial scanning.

Outcomes:
Changes in Soft Tissue Volume in the Lip Region after Implant Dentistry
Clinical Validation of PROPECG-TMT: Evaluation of Ability of the Artificial Intelligence Model, PROPECG-TMT, to Improve Physician's in Screening Patients Who Needs Coronary Artery Revascularization Based on TMT-ECG.
Trial ID: NCT06624852
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-02-13
Completion date: 2024-02-13
Sponsor: Yonsei University
Conditions: Coronary Artery Disease
Interventions: Screening for coronary artery revascularization based on TMT-ECG
Locations: Department of Biomedicine Systems Informatics, Yonsei University College of Medicine
Countries: South Korea
Enrollment: 100
Age range: 21 Years - 80 Years
Sex: ALL

Summary:
This clinical validation evaluates the performances of physicians with and without learning with PROPECG-TMT assistance for screening coronary artery revascularization on TMT-ECG.

There are two endpoints used to evaluate performances: Accuracy (primary endpoints with pair specificity/sensitivity, and PPV/NPV), and time needed for screening (secondary endpoints with time measurements in all cases).

Eligibility criteria:
Inclusion Criteria:

* TMT ECG exams adjudicated as equivocal cases

Exclusion Criteria:

* Exams not converting to median beat ECG
* Exams not containing interpretable exercise

Outcomes:
measurement of screening performances
A Study on the Effectiveness of the Application of an Artificial Intelligence Algorithm for Calibrating PPG With ECG to Improve the Accuracy of Atrial Fibrillation Burden Estimation
Trial ID: NCT06552468
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-01-14
Completion date: 2024-11-27
Sponsor: Beijing Anzhen Hospital
Conditions: Atrial Fibrillation
Interventions: AF monitoring by a smartwatch with PPG
Locations: Beijing Anzhen Hospital
Countries: China
Enrollment: 1054
Age range: 18 Years - None
Sex: ALL

Summary:
Use the ECG watch to collect W-PPG and W-ECG data. Through artificial intelligence algorithms, compare the W-PPG data collected by the ECG watch and the W-PPG data calibrated by the W-ECG data of the ECG watch with the P-ECG data manually annotated after being collected by the ECG recorder. Then evaluate the effectiveness of the calibrated algorithm in improving the accuracy of estimating atrial fibrillation burden.

Eligibility criteria:
Inclusion Criteria

Patients must meet all of the following criteria to be included in the study:

1. Aged 18 and above;
2. There was a clear electrocardiogram record of atrial fibrillation in the past, and atrial fibrillation ablation surgery will be performed during this hospitalization；
3. Patients who agree to wear both the "ECG watch" and the "ECG recorder" simultaneously；

Exclusion Criteria

Patients who meet any of the following criteria cannot be included in this study:

1. Unable to complete the wearing due to problems such as limited mobility.
2. Severe skin diseases such as severe skin allergies or skin ulcers.
3. Those with chronic insomnia who have not received drug treatment.
4. Patients with severe cardiovascular and cerebrovascular diseases who may need emergency treatment at any time.
5. Those with a history of Parkinson's disease, schizophrenia, or epilepsy.
6. Black people and those with overly dark skin.

Outcomes:
Automatically analyze the W-PPG data and the W-PPG data calibrated by W-ECG.
Non-contrast Magnetic Resonance Angiography Efficacy and Safety Assessment for Perforators Mapping in Deep Inferior Epigastric Perforator Flap Breast Reconstruction
Trial ID: NCT06061835
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-05-06
Completion date: 2024-07-30
Sponsor: I.M. Sechenov First Moscow State Medical University
Conditions: Breast Cancer; Postsurgical Acquired Absence of Organ
Interventions: Non-contrast MRA in preoperative perforator vessels mapping for deep inferior epigastric perforator (DIEP) flap breast reconstruction planning.; CTA with iodine contrast in preoperative perforator vessels mapping for deep inferior epigastric perforator (DIEP) flap breast reconstruction planning.
Locations: Lancet Clinic; IMSechenovMMA
Countries: Russia; Russia
Enrollment: 135
Age range: 18 Years - 70 Years
Sex: FEMALE

Summary:
The aim of this study is to evaluate the efficacy and safety of contrast-free magnetic resonance angiography (MRA) for deep inferior epigastric perforator flap planning. The investigators propose to perform a non-contrast MRA to achieve the following extremely important objectives: to avoid radiation exposure, minimize the risk of potentially harmful effects of contrast agents, and reduce the cost of the study.The investigators assume that the use of non-contrast MRA following our protocol including certain patient's positioning, a special pulse scanning sequence and perforators' projection method is effective and allows mapping of perforators without injecting a potential harmful contrast agent.

Eligibility criteria:
Inclusion Criteria:

* A female patients who underwent unilateral mastectomy due to breast cancer, in remission.
* At least 1 year after completion of radiotherapy.

Non-inclusion Criteria:

* A history of abdominal donor site free flaps or abdominoplasty.
* Inability to give consent.
* The expander with magnetic ports, claustrophobia, pregnancy and breastfeeding.
* Patients with severe hypertension, COPD, autoimmune disorders, SLE or poorly regulated diabetes
* The presence of psychiatric illness preventing participation in the study.

Exclusion Criteria:

* Patient's pregnancy diagnosed during the research period.
* Patient's refusal of surgery, patient's refusal of further participation in the study.
* The presence of concomitant diseases preventing participation in the study.

Outcomes:
The concordance ratio.; The overall operation time (min).; The time of the pedicle cut (min).; The length of the fascia incision (cm).; Intraoperative evaluation of flap perfusion.; Postoperative flap necrosis (%).