Ophthalmological ultrasonic automatic screening method and system based on deep learning

A deep learning and automatic screening technology, applied in the field of medical technology assistance, can solve the problems of inconsistent ultrasound training standards and inability to achieve training, and achieve the effect of reducing workload and improving timely diagnosis rate

Pending Publication Date: 2020-11-24
WUHAN UNIV
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Problems solved by technology

However, ultrasound training standards are not uniform across countries
I

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  • Ophthalmological ultrasonic automatic screening method and system based on deep learning
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  • Ophthalmological ultrasonic automatic screening method and system based on deep learning

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[0047] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0048] Such as figure 1 As shown, the deep learning-based ophthalmic ultrasound automatic screening method of the embodiment of the present invention comprises the following steps:

[0049] Model training phase:

[0050] Collect historical ophthalmic ultrasound pictures and videos, and train them to obtain segmentation models and classification models;

[0051] Detection phase:

[0052] S1. Obtain an ophthalmic ultrasound image to be detected;

[0053] S2. Use the trained segmentation model to perform artificial intelligence network segmentation on the ophthalmic ultrasound image to be detected...

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Abstract

The invention discloses an ophthalmological ultrasonic automatic screening method and system based on deep learning. The method comprises a model training stage comprising the steps of collecting historical ophthalmological ultrasonic pictures and videos and training the historical ophthalmological ultrasonic pictures and videos to obtain a segmentation model and a classification model, and a detection stage comprising the following steps of: S1, acquiring a to-be-detected ophthalmologic ultrasonic image; S2, performing artificial intelligence network segmentation on the to-be-detected ophthalmologic ultrasonic image by using the trained segmentation model, identifying the segmented ophthalmologic ultrasonic image by using the classification model to obtain a lesion category, and promptinga focus position; S3, evaluating a corresponding risk level according to the identified lesion; and S4, performing next diagnosis and treatment prompt according to the risk level. According to the invention, the workload of doctors can be reduced, the timely diagnosis rate of diseases can be improved, and meanwhile, specific lesion positions can be displayed, so that auxiliary training can be carried out on novice doctors.

Description

technical field [0001] The invention relates to the field of medical technology assistance, in particular to a method and system for automatic ophthalmic ultrasound screening based on deep learning. Background technique [0002] Ultrasonic diagnosis is an effective means of using the physical characteristics of ultrasound, that is, the reflection characteristics of sound waves, to apply to pathological changes in human organ tissues. It evaluates the detection site by imaging human organ tissues. , Intuitive advantages. Ultrasound examination in ophthalmology has almost become the only diagnosis and treatment method that can show intraocular diseases after refractive media opacity, and plays an irreplaceable important role in ophthalmology clinical auxiliary examination. Early diagnosis of blinding conditions such as cataracts can help reduce preventable vision loss. There is still a huge screening burden internationally and in places where medical resources are scarce, su...

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Application Information

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IPC IPC(8): G06T7/11G06T7/00G06T5/00G06N3/04G06K9/62
CPCG06T7/11G06T5/002G06T7/0012G06T2207/20028G06T2207/30041G06N3/045G06F18/214
Inventor 杨燕宁陈弟胡珊周奕文于薏
Owner WUHAN UNIV
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