moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Evaluation of Gixam's Efficacy Predicting Pre-cancerous Colorectal Polyps and Colorectal Cancer
Trial ID: NCT06688110
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-02
Completion date: 2026-04
Sponsor: Jubaan Ltd.
Collaborators: Jubaan GmbH
Conditions: Colorectal Cancer; Colorectal Adenomatous Polyp; Colorectal Neoplasms
Interventions: Gixam System
Locations: Erasmus MC
Countries: Netherlands
Enrollment: 800
Age range: 50 Years - 80 Years
Sex: ALL

Summary:
This is a prospective, single arm, multi-center clinical investigation aim to demonstrate the efficacy of Gixam in predicting the presence of premalignant colorectal polyps and colorectal cancer (CRC) in a fecal immunochemical test (FIT) positive population.

Eligibility criteria:
Inclusion Criteria:

1. Subjects aged ≥50 - ≤80 years.
2. Able to provide a signed informed consent.
3. Underwent FIT screening within the past 6 months with a positive result.
4. Scheduled for a FIT positive screening colonoscopy at investigational site.

Exclusion Criteria:

1. Has undergone colonoscopy within the past nine (9) years, except for a failed colonoscopy due to poor bowel preparation, which must have been performed within the past year without therapeutic intervention.
2. Personal history of CRC.
3. Family history of CRC, defined as having one or more first-degree relatives (parent, sibling, or child) with CRC at any age.
4. Subject has a diagnosis or medical / family history of any of the following conditions, including:

   * Familial adenomatous polyposis (also referred to as "FAP", including attenuated FAP and Gardner's syndrome),
   * Hereditary non-polyposis CRC syndrome (also referred to as "HNPCC" or "Lynch Syndrome"),
   * Other hereditary cancer syndromes including but are not limited to Peutz-Jeghers Syndrome, MYH-Associated Polyposis (MAP), Turcot's (or Crail's) Syndrome, Cowden's Syndrome, Juvenile Polyposis, Neurofibromatosis, or Familial Hyperplastic Polyposis.
5. Subject has a diagnosis or personal history of inflammatory bowel disease (IBD) including chronic ulcerative colitis, and/or Crohn's disease, and/or IBD-undefined.
6. Subjects with the disability to extend their tongue.
7. Subjects with tongue piercing.
8. Dental visit in the 7 days prior to the Gixam test.
9. Intake of pro-biotics over the past 3 months pre-Gixam test.
10. Subject has any condition that in the opinion of the Investigator should preclude participation in the study.

Outcomes:
Safety - number of device related adverse events and serious adverse events; Efficacy - Gixam's sensitivity and specificity
A Multi-centre, Cluster Randomized Controlled Trial of an Artificial Intelligence-Based Clinical Decision Support System for Integrated Management of Patients With Acute Ischemic Stroke: The GOLDEN BRIDGE II Trial
Trial ID: NCT04524624
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-09-01
Completion date: 2023-06-30
Sponsor: Beijing Tiantan Hospital
Conditions: Stroke
Interventions: AI-based CDSS
Locations: Beijing Tian tan Hospital
Countries: China
Enrollment: 21689
Age range: 18 Years - 90 Years
Sex: ALL

Summary:
This study will conduct a cluster randomized controlled trial to evaluate the impact of an artificial intelligence-based clinical decision support system for the integrated management of patients with acute ischemic stroke on the adherence to guideline-based therapies and the incidence of new clinical vascular events.

Eligibility criteria:
Inclusion Criteria:

* Cluster Inclusion Criteria:

  * Secondary or tertiary hospitals with an emergency department and neurologic wards that receive patients with AIS.
  * Hospitals with available brain MRI scans (1.5T or 3.0T).
* Patient Inclusion Criteria:

  * Patients of 18 years or older.
  * Patients admitted with neurological deficit consistent with ischemic stroke within 7 days of of symptoms onset. (\*Symptom onset is defined by the "last seen normal" principle)
  * Confirmation of new ischemic stroke by objective modality of CT scan and MRI (had relevant lesions on DWI).
  * Informed consent from patient or legally authorised representative (primarily spouse, parents, adult children, otherwise indicated).

Exclusion Criteria:

* Cluster Exclusion Criteria:

  * Grade-one hospitals and rural hospitals.
  * The specialized hospitals, such as women and children specialist hospital and tumor hospital.
  * Hospitals with less than 20 patients with suspected AIS per month.
* Patient Exclusion Criteria:

  * Diagnosed DWI negative stroke.
  * Diagnosed other types of cerebrovascular diseases, such as transient ischaemic attack, hemorrhagic stroke, cerebral venous sinus thrombosis, and so on.
  * Diagnosed non cerebral vascular disease, such as stroke mimic, seizures, central nervous system infections, metabolic encephalopathy, and so on.
  * Received carotid endarterectomy (CEA) and carotid angioplasty and stenting (CAS).
  * Involving in other investigational drug or device clinical trials.
  * Patients with malignant disease and life expectancy of less than 3 months or patients who are unable to complete the study for other reasons.
  * Women who are pregnant or postpartum (≤6 weeks).
  * Patients refuse to participate in this study.

Outcomes:
Incidence rate of new clinical vascular events (ischemic stroke, hemorrhagic stroke, myocardial infarction, or vascular death)
Intelligent Support for Radiological Reporting of Lung Neoplasms - SPOILERS Study
Trial ID: NCT07360145
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-03-23
Completion date: 2026-02-15
Sponsor: Azienda Ospedaliera SS. Antonio e Biagio e Cesare Arrigo di Alessandria
Conditions: Pulmonary Nodules
Interventions: Collection of variables identified for the study
Locations: SSD Laboratori di Ricerca (DAIRI) - AOU Alessandria
Countries: Italy
Enrollment: 329
Age range: 18 Years - None
Sex: ALL

Summary:
Lung cancer is one of the most common cancers and has one of the worst prognoses, mainly due to the difficulty of early diagnosis. In Italy, there are an estimated 41,000 new cases each year, and in 2021, the disease was responsible for approximately 34,000 deaths. The social impact is significant, as the disease is often diagnosed at an advanced stage, when the chances of survival are reduced: the 5-year survival rate is around 18% in advanced stages, while it can reach 90% if diagnosed at an early stage.

Early-stage lung cancer mainly manifests itself in the form of pulmonary nodules, which can be detected by computed tomography (CT). However, the diagnosis of these nodules often requires invasive procedures, such as bronchoscopy, CT-guided needle biopsy, or surgical biopsies, which affect patients' quality of life and healthcare costs. For this reason, the ability to accurately distinguish between benign and malignant nodules is a central theme in clinical research.

In recent years, artificial intelligence, particularly deep learning techniques, has shown considerable potential in supporting CT screening. Results show that AI can achieve performance superior to that of individual radiologists and comparable to that of a multidisciplinary team, using histological reports as a diagnostic reference. This confirms the value of AI as a tool to support clinical decision-making.

Considering the multimodal nature of clinical data (images, text reports, diagnostic tests), there is growing interest in models capable of integrating multiple sources of information. In this context, the research project aims to develop a system capable of automatically recognizing pulmonary nodules and generating natural language text descriptions of the findings.

Eligibility criteria:
Inclusion Criteria:

1. Age ≥18 years
2. Evidence of pulmonary nodule documented radiologically by chest CT scan
3. Presence of CT scan report
4. Presence of histological report (pulmonary nodule biopsy)
5. Presence of written informed consent, signed

Exclusion Criteria:

1. Previous cancer
2. Previous lung surgery
3. Previous radiation therapy and/or chemotherapy

Outcomes:
Development of a AI computer model
Scanning the Meiotic Spindle in Assisted Reproductive Techniques to Assess Oocyte Quality and Embryo Ploidy Evaluated by Artificial Intelligence (SMARTAI Study)
Trial ID: NCT06539104
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-01-05
Completion date: 2028-03-31
Sponsor: Charles University, Czech Republic
Collaborators: Czech Academy of Sciences; Czech Technical University in Prague; General University Hospital, Prague
Conditions: Infertility; Infertility, Female; Infertility, Male; Infertility Unexplained
Interventions: arteficial inteligence evaluation of oocyte and embryo development
Locations: General University Hospital in Prague; Czech Technical University in Prague; Institute of Physics AS CR; Biocev As Cr
Countries: Czechia; Czechia; Czechia; Czechia
Enrollment: 1000
Age range: 18 Years - 49 Years
Sex: ALL

Summary:
The assisted reproduction success rate is affected by several factors including the age of the women, oocyte quality and maturation state, as well as sperm quality. Imaging of the meiotic spindle may be crucial for determining the oocyte maturation. Artificial intelligence (AI) will be applied to establish the complex oocyte quality, embryo ploidy and pregnancy success probability from the sequence of data, starting with the recording of the meiotic spindle in polarized light, through paternal factors up to the time lapse recording of early embryo development. This strategy should reduce the cost of fertility treatment thanks to increased efficiency in choosing the most promising candidates and reducing the need for costly laboratory analyses.

Eligibility criteria:
Inclusion Criteria:

* Intracytoplasmatic Sperm Injection
* Preimplantation genetic testing
* Time lapse embryo record
* Singned informed consent

Exclusion Criteria:

* Gynecological diseases
* Genetical diseases of parents

Outcomes:
The relative number of embryos whose ploidy was correctly predicted by AI
A Deep Learning Approach to Submerged Deciduous Teeth Classification and Detection
Trial ID: NCT04309851
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2019-01-01
Completion date: 2020-03-01
Sponsor: Eskisehir Osmangazi University
Conditions: Artificial Intelligence; Submerging Tooth
Interventions: deep learning
Locations: Seçil Çalışkan
Countries: Turkey (Türkiye)
Enrollment: 74
Age range: 5 Years - 12 Years
Sex: ALL

Summary:
Objectives: The study aimed to compare the success and reliability of an artificial intelligence application in the detection and classification of submerged teeth in orthopantomography (OPG).

Methods: Convolutional neural networks (CNN) algorithms were used to detect and classify submerged molars. The detection module, which is based on the state-of-the-art Faster R-CNN architecture, processed the radiograph to define the boundaries of submerged molars. A separate testing set was used to evaluate the diagnostic performance of the system and compare it to the expert level.

Results: The success rate of classification and identification of the system is high when evaluated according to the reference standard. The system was extremely accurate in performance comparison with observers.

Conclusions: The performance of the proposed computer-aided diagnosis solution is comparable to that of experts. It is useful to diagnose submerged molars with an artificial intelligence application to prevent errors. Also, it will facilitate pediatric dentists' diagnoses.

Eligibility criteria:
Inclusion Criteria:

Exclusion Criteria:

OPG images of poor quality (metal artifact, artifacts due to position errors during shooting, etc.) were excluded.

Outcomes:
Submerged Tooth Detection
NiOS (Non-Invasive Oocyte Sorting) Study
Trial ID: NCT06521372
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-01-01
Completion date: 2027-01-02
Sponsor: Ovation Fertility
Collaborators: Future Fertility Inc.
Conditions: Infertility Primary
Interventions: Magenta AI image analysis tool
Locations: Ovation Fertility Las Vegas; Ovation Fertility Cincinnati; Ovation Fertility - Nashville
Countries: United States; United States; United States
Enrollment: 450
Age range: None - None
Sex: ALL

Summary:
The aim of this study is to determine if oocyte sorting for group culture using an artificial intelligence image analysis tool (MagentaTM) increases the usable blastocyst yield and subsequent pregnancy in patients undergoing IVF.

Eligibility criteria:
Inclusion Criteria:

* Fresh MII oocytes
* ICSI cycles with patient's or donor's sperm
* Patient's or donor's oocytes
* Full IVF cycle information available (including total number of oocytes retrieved, total MII oocytes, fertilization, embryo and blastocyst development, as well as ploidy status or implantation results, and 6-7 week ultrasound, if applicable)
* Good quality images of MII oocytes taken with a specified camera (Basler) and Future Fertility Software
* Single embryo transfers (SETs) at blastocyst stage.
* Blastocyst freeze-all cycles

Exclusion Criteria:

* Severe male factor infertility (\<1 million motile), including surgically retrieved sperm.
* ICSI cycles inseminating in vitro matured (IVM) oocytes
* Rescue ICSI oocytes (oocytes failed standard IVF and are re-inseminated by ICSI)
* Low quality images - Poor quality images on day of upload or before unblinding
* Lack of correlating reproductive outcomes (full IVF lab results not available).
* Autologous or donor frozen/warmed oocytes
* Day 3 embryo transfers or cleavage stage embryo freezing
* Patients with less than 6 mature oocytes

Outcomes:
Compare blastocyst development rate
Associated Factors of Ovarian Cancer and Endometrial Cancer in Indonesia. A Study for Developing Artificial-Intelligence-Based Screening Tools
Trial ID: NCT05697601
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-02-28
Completion date: 2024-06-30
Sponsor: Hasanuddin University
Collaborators: Chulalongkorn University
Conditions: Ovarian Cancer; Endometrial Cancer; Endometrial Hyperplasia
Interventions: Artificial-Intelligence Based Screening Tools; Pathology analysis
Locations: Hasanuddin University Hospital
Countries: Indonesia
Enrollment: 2905
Age range: None - None
Sex: FEMALE

Summary:
The goal of this observational study is to explore the possible associated factors of ovarian cancer and endometrial cancer in Indonesia and develop screening tools that could predict the risk of both types of cancer

The specific objectives of the study are

1. Elaborating the situation of ovarian and endometrial cancer in Indonesia
2. Exploring the possible clinical, demography and laboratory predictors of these diseases
3. Develop artificial-intelligence-based screening tools for both type of cancer based on possible predictors

This study will utilize the patient registry diagnosed with ovarian and endometrial cancer. We assumed that several demography, clinical, and laboratory predictors might possess good screening performance with higher sensitivity and specificity (\>80%).

Eligibility criteria:
Inclusion Criteria:

* Women with gynaecological symptoms but not limited to

  1. Irregular menstruation
  2. Heavy bleeding during menstruation
  3. pelvic pain
  4. vaginal discharge
  5. sudden weight loss
  6. pain during sexual intercourse
* Women who underwent routine gynaecological examination

Exclusion Criteria:

* unable to undergo serial gynaecological follow-up

Outcomes:
Number of People developing ovarian cancer; Number of People developing endometrial cancer
NEW MODEL OF ORGANIZATION OF AN OPERATIVE BLOCK (BLOC-OP)
Trial ID: NCT05106621
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-11-01
Completion date: 2022-11-30
Sponsor: University of Parma
Conditions: Artificial Intelligence in Operating Room
Locations: Azienda Ospedaliera-Universitaria di Parma
Countries: Italy
Enrollment: 142
Age range: 18 Years - None
Sex: ALL

Summary:
Perioperative medicine is characterized by a very delicate path; it is composed, in fact, of a series of highly specialized clinical measures managed by various professionals (surgeons, anesthetists, intensivists, nurses, etc.), who work together to ensure the best quality of all phases of the path (preoperative , intra and postoperative). On the other hand, it is necessary to underline the huge resources needed to provide surgical services. Organizational optimization, based on specific analyzes, could lead to a more careful management of resources in this area, avoiding waste due to early closure of the operating room or unexpected extension of the same. In recent years, precisely to respond to the need to analyze large quantities of information, the use of artificial intelligence techniques, and in particular of machine learning, is becoming increasingly popular, a branch of artificial intelligence that aims, through the use of algorithms and statistical model, to infer new knowledge in a way automatic. Such technologies appear to possess excellent analytical skills both in the clinical and, above all, organizational fields. The data that are emerging in the literature on this issue, although still the first in this regard, seem to confirm this hypothesis.

Eligibility criteria:
Inclusion Criteria:

all patients undergoing surgery who sign the informed consent form will be included.

Exclusion Criteria:

refusal of the patient to the study in question.

Outcomes:
Surgical Time Prediction
The Influence of Artificial Intelligence (AI) Assisted Polyp Detection (Discovery System) on Visual Gaze Patterns During Real-time Colonoscopy
Trial ID: NCT05619614
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-06-01
Completion date: 2024-11-30
Sponsor: Radboud University Medical Center
Collaborators: Utrecht University; Lund University
Conditions: Colonic Neoplasms
Interventions: eye-tracking glasses
Locations: Radboud university medical center Nijmegen
Countries: Netherlands
Enrollment: 52
Age range: None - None
Sex: ALL

Summary:
In this study, the investigators aim to assess the influence of a computer-aided detection (CADe) system on the visual gaze patterns of endoscopists in order to further understand the effect of AI during real-time colonoscopy.

Eligibility criteria:
Inclusion Criteria of patients:

* Referred for diagnostic, screening or surveillance colonoscopy

Exclusion Criteria of patients:

* Therapeutic procedure
* Boston Bowel Prep Score (BBPS) score of \<6

Outcomes:
Mean gaze time per area of influence
Kidney AI-enabled Care Transformation
Trial ID: NCT05056909
Status: WITHDRAWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-11
Completion date: 2024-10
Sponsor: Medical University of Graz
Collaborators: Fraunhofer Institut für Arbeitswirtschaft und Organisation; Empirica; IN-JET APS; CNET Svenska AB; XIGME; European ESKD Patients' Federation; Catholic University of the Sacred Heart; University of Manchester; Università degli studi di Verona; University of Leeds; Roche Diagnostics International AG; Federación Nacional ALCER
Conditions: Chronic Kidney Diseases
Interventions: Kidney ACTion; Standard of care
Locations: Università Cattolica del Sacre Cuore; University of Leicester
Countries: Italy; United Kingdom
Enrollment: 0
Age range: 18 Years - None
Sex: ALL

Summary:
In the Kidney ACTion study, CKD (Chronic Kidney Disease) patients at increased risk of progression to ESRD (End Stage Renal Disease) will be randomly assigned to either standard of care in a specialist nephrology outpatient clinic or to receiving CKD care supported by a novel AI-supported (Artificial Intelligence) software solution.

Eligibility criteria:
Inclusion Criteria:

* Male or female aged ≥18 years at the time of signing the informed consent
* Chronic kidney disease with eGFR \<45 mL/min/1.73 m2 and UACR \>300 mg/g creatinine (eGFR=estimated glomerular filtration rate; UACR=Urine Albumin-to-Creatinine Ratio)
* Patient is willing to comply with the study requirements for therapy during the entire study period
* Patient is capable of providing written informed consent to participate in the study

Exclusion Criteria:

* Patients with underlying renal disease likely to receive disease-specific therapy other than SoC (Standard of Care)
* Patient expected to require renal replacement therapy within less than one year from study inclusion

Outcomes:
Urinary Albumin to Creatinine Ratio (UACR)
Effects of Virtual Reality and Proprioceptive Neuromuscular Facilitation on Upper Limb Sensorimotor Control Among Patients With Parkinson's Disease.
Trial ID: NCT07308093
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-01-31
Completion date: 2026-07-31
Sponsor: Lahore University of Biological and Applied Sciences
Conditions: Parkinson Disease
Interventions: Intervention 1; artificial intelligence
Locations: Lahore University of Biological and Applied Sciences
Countries: Pakistan
Enrollment: 28
Age range: 50 Years - 80 Years
Sex: ALL

Summary:
Parkinson's Disease is a chronic neurodegenerative disorder marked by the progressive loss of dopaminergic neurons in the substantia nigra and accumulation of α-synuclein (Lewy bodies).It impacts both motor and non-motor systems, with growing global prevalence-affecting 7-10 million people, mostly men over 60 years-due to increasing life expectancy and demographic shifts.

PD shows motor symptoms like tremors and bradykinesia, along with non-motor issues like depression, REM sleep disorder, and cognitive decline. Diagnosis is often delayed due to subtle early signs like constipation or shoulder pain and atypical cases lacking classical tremors.

VR offers immersive, task-specific therapy that enhances motor learning and neuroplasticity in a safe, engaging environment. By integrating sensory feedback and real-time motion tracking, VR reduces tremors, promotes cortical reorganization (notably in the sensorimotor cortex and cerebellum), and increases rehabilitation motivation through gamified experiences.

PNF is a hands-on therapy using tactile, auditory, and proprioceptive cues to improve coordination, strength, and movement patterns. Especially useful in Parkinson's rehabilitation, PNF targets rigidity, bradykinesia, tremors, and postural instability by reinforcing neuromuscular coordination and improving movement execution in functional, diagonal planes.

Sensorimotor control is the dynamic integration of sensory input, motor planning, and execution, which becomes disrupted in PD due to impaired proprioception and feedback loops. Patients rely excessively on visual feedback, making movement effortful, highlighting the need for focused upper limb rehabilitation strategies.

Eligibility criteria:
Inclusion Criteria:

1. Idiopathic Parkinson's Disease according to Gelb's criteria
2. Stage II-IV Hoehn \&Yahr scale
3. Intact cognition status (MMSE \> 24)
4. 50-80 years of age

Exclusion Criteria:

1. History of PD surgery
2. Phobia of virtual gaming systems
3. Visual anomalies
4. Cardiovascular problems
5. Severe dyskinesia or "on-off" fluctuations

Outcomes:
Kinetic Tremors; Bradykinesia; Proprioception