moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Artificial Intelligence Guided Echocardiographic Screening of Rare Diseases
Trial ID: NCT05139797
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2021-11-18
Completion date: 2027-06-01
Sponsor: Cedars-Sinai Medical Center
Conditions: Cardiac Amyloidosis
Interventions: EchoNet-LVH screening for cardiac amyloidosis
Locations: Cedars-Sinai Medical Centre (Los Angeles)
Countries: United States
Enrollment: 300
Age range: 18 Years - None
Sex: ALL

Summary:
Despite rapidly advancing developments in targeted therapeutics and genetic sequencing, persistent limits in the accuracy and throughput of clinical phenotyping has led to a widening gap between the potential and the actual benefits realized by precision medicine.

Recent advances in machine learning and image processing techniques have shown that machine learning models can identify features unrecognized by human experts and more precisely/accurately assess common measurements made in clinical practice.

The investigators have developed an algorithm, termed EchoNet-LVH, to identify cardiac hypertrophy and identify patients who would benefit from additional screening for cardiac amyloidosis and will prospectively evaluate its accuracy in identifying patients whom would benefit from additional screening for cardiac amyloidosis.

Eligibility criteria:
Inclusion Criteria:

* Patients who have a high suspicion for cardiac amyloidosis by AI algorithm

Exclusion Criteria:

* Patients who decline to be seen at specialty clinic
* Patients who have passed away

Outcomes:
Number of New Diagnoses of Cardiac Amyloidosis Found
Detection and Automatic Segmentation of Liver Nodules in Patients With Colorectal
Trial ID: NCT04834596
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-05-02
Completion date: 2022-05-02
Sponsor: University Hospital, Angers
Conditions: Detection of Liver Metastases
Interventions: retrospectives analysis on hepatic scanner
Locations: UHAngers
Countries: France
Enrollment: 300
Age range: 18 Years - None
Sex: ALL

Summary:
Evaluation of the diagnostic performance of artificial intelligence-based software for the detection of liver nodules on scanner in patients at risk of liver metastases of colorectal cancer

Eligibility criteria:
Inclusion Criteria:

* Patients consecutively treated for colorectal cancer a
* Availability of imaging by injected abdominal scanner including at least one portal phase
* Age over 18

Exclusion Criteria:

* Patients with treatment of the hepatic nodule by ablation or previous hepatic surgery
* Imaging follow-up \<12 months
* Non-interpretable scanner (artefact, injection quality, etc.)
* Patients with more than 10 liver metastases

Outcomes:
To evaluate the performance of the software for detection of hepatic nodules on CT scan of patients with colorectal cancer compared to a reference constituted by the evaluation established by two radiologists in consensus.
Acquiring Images of Pigmented Skin Lesions With the Artificial Intelligence Dermatology Assistant (AIDA™) and Concurrent in Vivo Assessment for the Evaluation of Diagnostic Accuracy
Trial ID: NCT03621462
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2018-10-15
Completion date: 2020-10-15
Sponsor: Elucid Labs Inc.
Conditions: Melanoma (Skin); Basal Cell Carcinoma of the Skin; Squamous Cell Carcinoma of the Skin
Interventions: Artificial Intelligence Dermatology Assistant (AIDA™)
Locations: Total Skincare Centre
Countries: Canada
Enrollment: 4000
Age range: 18 Years - None
Sex: ALL

Summary:
Diagnosis of melanoma involves physical examination of the lesion with many dermatologists adjunctively employing dermoscopes. The rate of misdiagnosis of melanoma remains significant, along with a high rate of referral to biopsy. Elucid Labs (Waterloo, Ontario) has developed a novel handheld, digital dermoscope with accompanying visualization and analysis software - the Artificial Intelligence Dermatology Assistant (AIDA™). Apart from collecting conventional demoscopic images, AIDA also collects images at various spectral bands. The aim of this study is to understand and quantify the value of this novel adjunctive information for dermatologists diagnosing atypical skin lesions.

Eligibility criteria:
Inclusion Criteria:

1. Is 18 years of age or older
2. Has provided informed consent to participate in the study
3. Is being evaluated by a dermatologist for at least one pigmented skin lesionscheduled for biopsy and meeting the following:

   1. Has a diameter of at least 1 mm and at most 10 mm
   2. Is accessible to the study device (allows for complete seal of device with skin-ie. no ambient light leakage)
   3. Is not within 1 cm of the eyes, on mucosal surfaces, palmar hands, plantarfeet, or under nails
   4. Is intact (eg. no open sores, ulcers, bleeding)
   5. Has not previously been biopsied, excised or traumatized
   6. Does not contain visible scarring or foreign material (eg. tattoos, etc.)

Exclusion Criteria:

1\. Any allergy to isopropyl alcohol

Outcomes:
Sensitivity of in-clinic dermatologist diagnosis using AIDA™ compared to standard dermoscopy and physical examination alone; Specificity of in-clinic dermatologist diagnosis using AIDA™ compared to standard dermoscopy and physical examination alone
AI Reliance in Diagnostic Radiology Among Intern Doctors in Palestine: A Triple-Arm, Triple-Blind, Parallel-Design Randomized Controlled Trial
Trial ID: NCT07558746
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-04-10
Completion date: 2026-05-01
Sponsor: Al-Quds University
Conditions: Radiology; Internship and Residency; AI (Artificial Intelligence)
Interventions: AI prompt (Correct); AI prompt (Incorrect)
Locations: Al-Quds University
Countries: Palestinian Territories
Enrollment: 159
Age range: None - None
Sex: ALL

Summary:
This study aims to enroll intern doctors and have them sit one of three identical radiology exams. The only difference between them is an AI-assistant. The differences between these groups will be used to measure the extent of AI reliance among intern doctors in Palestine.

Eligibility criteria:
Inclusion Criteria:

* Intern doctor in Palestine
* Completion of at least 3 months from their 1 year internship
* Confirmed prior training in radiologic interpretation

Exclusion Criteria:

* Does not consent to the study
* Completion of the internship
* Non-completion of at least 3 months of their 1 year internship

Outcomes:
AI Reliance; Exam time
DNA Methylation Combined With Artificial Intelligence Imaging for Early Benign and Malignant Differentiation of Lung Nodules:an Observational,Single-center Clinical Trial(MAGIC Study)
Trial ID: NCT06220305
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-12-01
Completion date: 2024-12-31
Sponsor: The First Affiliated Hospital with Nanjing Medical University
Conditions: Lung Cancer Diagnosis
Interventions: DNA methylation joint AI imaging
Locations: Jiangsu Province Hospital
Countries: China
Enrollment: 100
Age range: 20 Years - 80 Years
Sex: ALL

Summary:
Lung cancer is the leading cause of cancer deaths,and the key to reducing mortality in lung cancer patients is early diagnosis and treatment.Currently,peripheral blood DNA methylation is a novel in vitro molecular marker for tumors.Meanwhile artificial intelligence diagnostic system can further improve the diagnostic ability.The purpose of this study is to apply the overall DNA methylation level (i.e.,methylation profile) and the altered methylation degree of specific genes as tumor diagnostic indexes,and combined with the artificial intelligence imaging technology for the early and accurate diagnosis of lung cancer,to achieve the early detection,diagnosis,and treatment of lung cancer,and to effectively reduce the mortality rate of lung cancer.

Eligibility criteria:
Inclusion Criteria:

* (1) Patients with pulmonary nodules diagnosed by imaging;
* (2) No contraindications to blood collection;
* (3) Voluntary completed DNA methylation testing;
* (4) Signed informed consent and performed all the study mandated procedures.

Exclusion Criteria:

* (1) Pregnant or lactating(6 months) women;
* (2) History of malignancy or known malignancy or precancerous lesions or known tuberculosis within 3 years;
* (3) Autoimmune system disorders;
* (4) Undergoing any diagnostic puncture therapy, such as percutaneous lung biopsy, transbronchial, pre-enrollment biopsy, or surgical procedures (within 6 months); (5) Recipients of organ transplants or prior non-autologous (allogeneic) bone marrow transplants or stem cell transplants; (6) Received antibiotic therapy within 14 days or applied a drug that elevates white blood cells within 45 days prior to the blood draw.

Outcomes:
Efficacy of early diagnosis of benign and malignant lung nodules by blood DNA methylation results combined with AI imaging
Characterization of the Postural Habits of Wheelchair Users in an Ecological Situation and Analysis of the Acceptability of International Recommendations in the Prevention of Pressure Sores Risk by Using a Connected Textile Sensor Integrating an Artificial Intelligence Algorithm
Trial ID: NCT04335942
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-09-30
Completion date: 2023-06-01
Sponsor: Assistance Publique - Hôpitaux de Paris
Conditions: Duchenne Muscular Dystrophy; Spinal Muscular Atrophy
Interventions: Alert "AFNOR 3.6".; Alertes " AFNOR 3.6 " et alertes " Guidelines ".
Locations: Hôpital Raymond Poincaré
Countries: France
Enrollment: 36
Age range: 18 Years - None
Sex: ALL

Summary:
Spinal cord injuries and people with Duchenne Muscular Dystrophy or Infant Spinal Muscular Atrophy (ISA) are prone to pain and pressure sores associated with prolonged sitting. For this reason, it is recommended that people with spinal cord injuries release pressure every 15 to 30 minutes and motorized wheelchair users use the electric positioning functions at least 1 minute every hour.

The aim is to prevent and/or reduce pain and pressure sores. These devices could help to observe daily the variability of users' pressure maps, their impact on occupational performance, the link with pain and redness and could propose customized adjustments.

Eligibility criteria:
Inclusion Criteria:

* Men or women over 18 years of age,
* Daily user of a FR (more than 3 hours per day):

  * Persons with Duchenne Muscular Dystrophy (DMD) or DMB using an FRE,
  * People with Infant Spinal Muscular Atrophy (ISA) using an FRE,
  * WB persons using an MRA with sensitivity disorders (ASIA A).
* FRE allowing a switchover of at least:

  * 25° of the seat and 120° of the backrest,
  * 45° of sitting in one block,
* Patient who has signed an informed and written consent,
* Affiliation to a social security scheme (beneficiary or beneficiary).

Exclusion Criteria:

* Refusal of the patient to participate in the study,
* School level lower than cycle 3 not allowing to understand the use of the embedded device,
* Severe incontinence,
* BM without sensitivity disorders,
* Participant in another study or therapeutic trial,
* Patient under guardianship or curatorship,
* Pregnant women.

Outcomes:
Evaluate the impact of a device, monitoring the wheelchair user's risk of pressure sores and issuing alerts based on international recommendations, on the support reliefs provided by the subject in an ecological situation
Secukinumab (Cosentyx®) Effectiveness in Axial Spondyloarthritis and Psoriatic Arthritis Patients Using Artificial Intelligence (SpAINET): Spanish Multicenter, Retrospective, Real World Evidence Study
Trial ID: NCT05677542
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-06-02
Completion date: 2021-12-31
Sponsor: Novartis Pharmaceuticals
Conditions: Axial Spondyloarthritis; Psoriatic Arthritis
Interventions: secukinumab
Locations: Novartis Investigative Site; Novartis Investigative Site; Novartis Investigative Site
Countries: Spain; Spain; Spain
Enrollment: 758
Age range: 18 Years - None
Sex: ALL

Summary:
This was a multicenter, retrospective, and non-interventional study using secondary data captured in the Electronic Health Records (EHRs). The extraction of the data captured in the EHRs was performed with EHRead® by SAVANA, an innovating data-driven system based on Natural Language Processing (NLP) and big data analytics. Data was extracted and analyzed at Index Date, Follow Up, or as specified for each variable.

Eligibility criteria:
Inclusion Criteria:

* Aged ≥ 18 years.
* Diagnosed with axSpA or PsA.
* Had received or are receiving bDMARD, independently of the line of treatment, from January 2018 to data collection.
* Had at least 3 months follow up from first bDMARD administration.
* Patients with EHRs.

Exclusion Criteria:

• Receiving bDMARD within a clinical trial from January 2018 to data collection.

Outcomes:
Proportion of patients achieving disease control status at 6 months (±3 months) of secukinumab treatment
Automated Meningioma Segmentation and Volumetry Using a nnU-Net Based Architecture on Contrast-enhanced MRI
Trial ID: NCT05093751
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2013-03-23
Completion date: 2021-09-30
Sponsor: Seoul National University Hospital
Conditions: Meningioma; Artificial Intelligence
Interventions: Observation
Enrollment: 600
Age range: 18 Years - None
Sex: ALL

Summary:
U-Net-based architectures will be applied to 500 contrast-enhanced axial MR images of different patients from a single institution after manual segmentation of meningioma, of which 50 were used for testing. Tumor volumetry after autosegmentation by trained U-Net-based architecture is final goal.

Eligibility criteria:
Inclusion Criteria:

* Radiologically diagnosed meningioma by MRI

Exclusion Criteria:

* under 18 years old
* Multiple meningiomas
* Orbital meningioma
* Any prior treatment for intracranial meningioma before registration

Outcomes:
Accuracy compared with ground truth
OPTIMAL - OsteoPorosis Treatment Identification Using Machine Learning
Trial ID: NCT05678569
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-02
Completion date: 2023-06
Sponsor: NHS Greater Glasgow and Clyde
Conditions: Osteoporosis Risk; Osteoporosis
Interventions: Dual Energy Xray Absorptiometry
Locations: West of Scotland Innovation Hub
Countries: United Kingdom
Enrollment: 250
Age range: 50 Years - 80 Years
Sex: ALL

Summary:
OPTIMAL is a pilot feasibility study for a machine learning (ML) based enhanced screening software for osteoporosis.

This tool has been created using machine learning, based on data from NHS Greater Glasgow and Clyde. The study will contact individuals deemed at high risk by the study (750 patients will be re-identified, and these will be contacted starting from the highest risk until 250 patients are recruited) and perform DXA scans, clinical review, and bloods tests that are relevant to osteoporosis. This data will then be compared to the predictions made by the OPTIMAL enhanced screening tool, in order to test how effective it is.

Eligibility criteria:
Inclusion Criteria:

* Age \>=50y
* Age \<=80y
* Identified as high risk by initial model

Exclusion Criteria:

* Age \<50y
* Age \>80y
* BMI \<=18 \& \>=30
* Recorded diagnosis of osteoporosis
* Prior prescription of bone protective agents such as:

bisphosphonate (alendronic acid, risedronate, ibandronate, zoledronic acid) / denosumab / raloxifene / strontium ranelate / teriparatide / romosozumab

* History of prior DXA imaging (prior quantification of BMD)
* History of metabolic bone disease
* History of primary hyperparathyroidism

Outcomes:
Osteoporosis Diagnosis
Visualization Engineering Platform for Pulse Diagnosis of Traditional Chinese Medicine-The Research of Similar Moiré Feature Analyzing Approach Based on Recurrent Neural Network to Process the Measured Slip Pulse Wave-Images With Chun, Guan and Chy
Trial ID: NCT04661605
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-12
Completion date: 2021-12
Sponsor: China Medical University Hospital
Conditions: Slippery Pulse
Interventions: Recurrent Neural Network
Locations: China Medical University Hospital
Countries: Taiwan
Enrollment: 80
Age range: 20 Years - 90 Years
Sex: ALL

Summary:
The diagnoses processes of Traditional Chinese Medicine (TCM) focus on the following four main types of diagnoses methods consisting of inspection, olfaction, inquiry, and palpation. The most important one is palpation also called pulse diagnosis which is to measure wrist artery pulse by TCM doctor's fingers to detect patient's health state. The pulse diagnosis has three parts, namely 'Chun', 'Guan' and 'Chy', with the location. Wrist measurements correspond to different parts of the body's organs. However, the pulse information is analyzed by TCM doctor's pulse diagnoses process, which is picked only a single waveform from a long-term pulse measured process and often discarded the other waveforms contained in the same information, e.g. Slip Pulse waveform. The research object of this project is to divide the TCM diagnosis patients into two groups, one is the Slip pulse group and the other is a Flat Pulse group, in which the cases are collected at least 50 cases for the first gruop and 30 cases for the second group, individually. The purpose of this project will integrate the TCM doctor's experiences and standardize them in order to construct an effective Slip pulse diagnosis system based on visualization engineering platform. It could refine Slip pulse diagnoses processes and reduce their diagnoses loading by using the proposed approach during this project. Therefore, we will propose a similar Moiré feature analyzing approach based on Recurrent Neural Network to process the measured Slip Pulse wave-images with 'Chun', 'Guan' and 'Chy' in order to prepare this visualization engineering platform. We will measure the waveform images of the three parts of the wrist as 'Chun', 'Guan' and 'Chy' with the moiré feature technology proposed in this project. It mainly extracts the moiré features such as pulse image from numerous measurement 'Chun', 'Guan' and 'Chy' signals, and further analyzes and summarizes them with AI technology. We could extract the pulse characteristics of Slip pulse automatically from the many measurement signals and enhance the automatic judging ability of the current pulse instrument to the Slip pulse waveform. Provide important pulse information to the TCM doctors as an important reference for precision clinical diagnoses.

Eligibility criteria:
Inclusion Criteria:

1. Already sign test consent permit.
2. More than 20-year-old.

Exclusion Criteria:

1\. There is a wound or inflammation at the wrist skin measurement.

Outcomes:
Slippery Pulse
Voice Biomarkers Predictive of Depression and Anxiety: An Observational Study
Trial ID: NCT05455905
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-09-14
Completion date: 2022-09-11
Sponsor: Kintsugi Mindful Wellness, Inc.
Conditions: Depression, Anxiety; Healthy; Mental Health Wellness 1
Interventions: Patient Health Questionnaire-9; Generalized Anxiety Disorder-7; Hamilton Depression Rating Scale; Hamilton Anxiety Rating Scale
Locations: Kintsugi Mindful Wellness, Inc.
Countries: United States
Enrollment: 19533
Age range: 18 Years - None
Sex: ALL

Summary:
The objective of this study is to collect data to improve the sensitivity and specificity of Kintsugi Voice's (formerly KiVATM) predictions on audio files. A web-based audio recording tool will be used to record voice sample and ask participants the PHQ-9 and GAD-7 questions. A voluntary subset of participants will then be assessed by mental health professionals with the Hamilton Depression Rating Scale (HAM-D) and Hamilton Anxiety Rating Scale (HAM-A) scores.

Eligibility criteria:
Inclusion Criteria:

* Adult males and or females over the age of 18 at the time of informed consent. Adults with a known history of depression and/or anxiety and adults without a known history of depression and/or anxiety will be included in this study.
* Access to a laptop, smart-phone or tablet with a functioning microphone
* Stated willingness to comply with all study procedures and availability for the duration of the study
* Fluency in English

Exclusion Criteria:

* Visual impairment that would make it difficult for the participant to see the web-based survey
* Motor impairment that would make it difficult for the participant to use the web-based survey

Outcomes:
Hamilton Depression Rating Scale; Hamilton Anxiety Rating Scale
Bladder Point-of-Care Ultrasound to Assess Patient Readiness for Pelvic Ultrasound With the Use of Artificial Intelligence
Trial ID: NCT07535294
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-08-14
Completion date: 2026-02-11
Sponsor: Nicklaus Children's Hospital f/k/a Miami Children's Hospital
Conditions: Abdominal Pain; Pelvic Pain
Interventions: Experimental AI POCUS; Standard of Care Ultrasound
Locations: Nicklaus Children's Hospital
Countries: United States
Enrollment: 520
Age range: 8 Years - 21 Years
Sex: FEMALE

Summary:
Point-of-Care Ultrasound (POCUS) is a relatively quick and non-invasive technique to assess bladder fullness which will be provided under standard of care. The goal of this study is to assess for bladder fullness by measuring bladder volume using a handheld POCUS with AI technology to objectively determine patient readiness for pelvic ultrasound.

Eligibility criteria:
Inclusion Criteria:

* Females aged 8 - 21 years presenting to the Emergency Department with lower abdominal pain and with an order for a transabdominal pelvic ultrasound.

Exclusion Criteria:

* Patients less than 8 years or greater than 21 years
* Males
* Patients with a history of anatomical anomalies or known pregnancy
* Patients that are critically ill, non-verbal or with significant developmental delay

Outcomes:
AI-POCUS assessment of bladder volume
Image Analysis With Artificial Intelligence to Increase Precision in Breast Cancer Screening - the ScreenTrust MRI Substudy: a Prospective Trial of AI to Select Women for Supplemental Screening MRI
Trial ID: NCT04832594
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-04-01
Completion date: 2025-07-01
Sponsor: Karolinska University Hospital
Collaborators: KTH Royal Institute of Technology; Region Stockholm; Bröstcancerförbundet, Sweden
Conditions: Breast Cancer
Interventions: AI selection for supplemental breast MRI
Locations: Karolinska University Hospital
Countries: Sweden
Enrollment: 2500
Age range: 40 Years - 74 Years
Sex: FEMALE

Summary:
This is a prospective clinical trial aiming to determine the ability of an AI pipeline to identify women who would benefit from supplemental MRI in terms of decreasing the number of cancers having a significantly delayed detection

Eligibility criteria:
Inclusion Criteria:

* Complete four-view screening mammography examination

Exclusion Criteria:

* Women in surveillance program referred from the hereditary cancer unit
* Breast implants
* Prior breast cancer
* Breast feeding
* MRI contraindication requiring radiologist assessment
* AI Tool unable to process mammograms due to technical reason

Outcomes:
Significantly Delayed Breast Cancer Detection per 1000 women