Artificial intelligence assisted prostate tumor early diagnosis method based on surface enhanced Raman spectroscopy
A surface-enhanced Raman and prostate tumor technology, applied in the medical field, can solve the problems of unsatisfactory early diagnosis of prostate tumors, excessive screening of benign diseases, and insufficient diagnosis of malignant diseases.
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
Embodiment 1
[0056] Such as Figure 1-2 As shown, the establishment of the serum SERS spectrum database of prostate tumor patients
[0057] (1) Inclusion criteria:
[0058] 1) Patients with clear pathology who have undergone prostate biopsy or prostate surgery in the hospital;
[0059] 2) Through the evaluation of the patient's physical strength (ECOG 0-2), blood routine, liver and kidney function, heart function and tumor burden;
[0060] 3) After being fully informed of the purpose and possible risks of the study, the patient agreed to participate in the test and signed the "Informed Consent Form for the Use of Clinical Samples".
[0061] (2) Exclusion criteria:
[0062] 1) Those with previous history of other tumors;
[0063] 2) Previously received allogeneic hematopoietic stem cell transplantation or solid organ transplantation;
[0064] 3) Those who have a history of psychotropic drug abuse and cannot quit or have a history of mental disorders;
[0065] 4) Unable to cooperate or...
Embodiment 2
[0082] Such as Figure 1-2 As shown, the construction of an early artificial intelligence diagnosis system for prostate tumors
[0083] 1.2.1 Construct and train CNN network based on SERS data, construct classifier
[0084] (1) Use the Python script to call the Keras framework API to complete CNN construction, training and testing. The framework uses TensorFlow as the underlying driver. The CNN structure designed in this project consists of 6 layers: convolutional layer 1-pooling layer 1-convolutional layer 2-pooling layer 2-full connection layer 1-full connection layer 2 (output layer).
[0085] 1) The input data of the input layer is the preprocessed one-dimensional SERS spectral data.
[0086] 2) The first convolutional layer includes 60 convolutional kernels, each convolutional kernel has a size of 1×12, border padding, and the activation function is set to “relu”, and convolution operation is performed on the input data to extract data features.
[0087] 3) The second...
PUM
Abstract
Description
Claims
Application Information
- R&D Engineer
- R&D Manager
- IP Professional
- Industry Leading Data Capabilities
- Powerful AI technology
- Patent DNA Extraction
Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.
© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com