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Artificial Intelligence clinical trials

View clinical trials related to Artificial Intelligence.

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NCT ID: NCT05682105 Active, not recruiting - Clinical trials for Artificial Intelligence

Detection of Jaundice From Ocular Images Via Deep Learning

Start date: December 1, 2018
Phase:
Study type: Observational

Our study presents a detection model predicting a diagnosis of jaundice (clinical jaundice and occult jaundice) trained on prospective cohort data from slit-lamp photos and smartphone photos, demonstrating the model's validity and assisting clinical workers in identifying patient underlying hepatobiliary diseases.

NCT ID: NCT05041777 Active, not recruiting - Clinical trials for Basal Cell Carcinoma

Optical-Coherence Tomography for the Non-invasive Diagnosis and Subtyping of Basal Cell Carcinoma

OCT-BCC
Start date: February 15, 2017
Phase:
Study type: Observational

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

NCT ID: NCT04314180 Active, not recruiting - Clinical trials for Artificial Intelligence

Artificial Intelligence System for Assessing Image Quality of Slit-Lamp Images and Its Effects on Diagnosis

Start date: February 1, 2020
Phase:
Study type: Observational

Slit-lamp images are widely used in ophthalmology for the detection of cataract, keratopathy and other anterior segment disorders. In real-world practice, the quality of slit-lamp images can be unacceptable, which can undermine diagnostic accuracy and efficiency. Here, the researchers established and validated an artificial intelligence system to achieve automatic quality assessment of slit-lamp images upon capture. This system can also provide guidance to photographers according to the reasons for low quality.

NCT ID: NCT04289064 Active, not recruiting - Clinical trials for Artificial Intelligence

Artificial Intelligence System for Assessing Image Quality of Fundus Images and Its Effects on Diagnosis

Start date: February 1, 2020
Phase:
Study type: Observational

Fundus images are widely used in ophthalmology for the detection of diabetic retinopathy, glaucoma and other diseases. In real-world practice, the quality of fundus images can be unacceptable, which can undermine diagnostic accuracy and efficiency. Here, the researchers established and validated an artificial intelligence system to achieve automatic quality assessment of fundus images upon capture. This system can also provide guidance to photographers according to the reasons for low quality.

NCT ID: NCT04232462 Active, not recruiting - Clinical trials for Artificial Intelligence

A Multicentric Validation Study on the Accuracy of Artificial Intelligence Assisted System in Clinical Application of Digestive Endoscopy

Start date: January 1, 2020
Phase:
Study type: Observational

This is an artificial intelligence-based optical artificial intelligence assisted system that can assist endoscopists in improving the quality of endoscopy.