Heuron Co., Ltd.

Hl Klemove Corp.

Hwan Heo

Eung Yeop Kim

Soo Hwa Song

Dong Hoon Shin

Ilki Mun

Minyoung Lee

Jungwook Seo

Young Hee Sung

Jinseok Bae

Disclosed herein is a sensor assembly capable of detecting moisture introduced into a rack housing. The sensor assembly includes a housing, an angle sensor provided in the housing, and a moisture sensor provided in the housing. The moisture sensor includes a ground electrode, a first input electrode, a second input electrode, and a control unit electrically connected to the ground electrode, the first input electrode, and the second input electrode to detect moisture. The ground electrode, the first input electrode, and the second input electrode extend downward from the housing. A lower end of the ground electrode, a lower end of the first input electrode, and a lower end of the second input electrode are exposed to the outside of the housing. Further, the first input electrode extends downward to a different level compared to the second input electrode.

Sensor assembly and apparatus

The present invention relates to an artificial intelligence-based Parkinson's disease diagnosing apparatus and method. An artificial intelligence-based Parkinson's disease diagnosing apparatus which is an aspect of the present disclosure includes an image acquiring unit which acquires a first image related to a multi-echo magnitude and phase from an MRI obtained by capturing a brain of a patient; an image processing unit which post-processes the acquired first image so as to observe a substantia nigra and a nigrosome-1 region used as imaging biomarkers of the Parkinson's disease; an image analyzing unit which analyzes the post-processed first image to classify a second image including the nigrosome-1 region and detects the nigrosome-1 region from the classified second image; and a diagnosing unit which diagnoses whether the patient has the Parkinson's disease by analyzing whether the detected nigrosome-1 region is normal, in which the image processing unit generates a susceptibility map weighted imaging image by applying a quantitative susceptibility map mask to the first image based on a quantitative susceptibility mapping algorithm to perform the post-processing and the image processing unit further performs at least one operation of angle adjustment, image enlargement, and reslice, on the generated susceptibility map weighted imaging image.

Artificial intelligence (AI) based parkinson's disease diagnosing apparatus and method

Disclosed are Parkinson's disease diagnosing apparatus and method and a configuration which includes an image acquiring unit which acquires a multi-echo magnitude and a phase image from MRI obtained by capturing a brain of a patient, an image processing unit which post-processes only substantia nigra and a nigrosome-1 region proposed as an imaging biomarker of the Parkinson's disease from the acquired image to be observed; an image analyzing unit which classifies images including the nigrosome-1 region by analyzing the processed images and detects the nigrosome-1 region from the classified image, and a diagnosing unit which determines whether the nigrosome-1 region is normal in the classified image to diagnose the Parkinson's disease is provided so that only the image which includes the nigrosome-1 region is classified in the MRI and the nigrosome-1 region is analyzed from the classified image to diagnose the Parkinson's disease.

Parkinson's disease diagnosing apparatus and method

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Seongnam-si, South Korea

Hwan Heo