Blood sample automatic analysis system based on artificial intelligence

By introducing dynamic adjustment mechanisms and adaptive learning algorithms into the blood sample automatic analysis system, combining transfer learning and Bayesian optimization, the problem that system accuracy depends on the quality and diversity of training data and the difficulty of explanation of results is solved, and high-accurate identification of rare diseases and interpretability of results is achieved, which enhances the trust of medical staff and the security of patient data.

CN119694529AInactive Publication Date: 2025-03-25KUNSHAN FIRST PEOPLES HOSPITAL
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Patent Information

Application Number
CN202411321256.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-09-23
Publication Date
2025-03-25
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

The accuracy of existing AI-based automatic blood sample analysis systems is highly dependent on the quality and diversity of training data, and the 'black box' nature of the system makes the results difficult to explain, and medical staff may not fully understand or trust the diagnostic results given by the system.

Method used

By introducing dynamic adjustment mechanisms and adaptive learning algorithms into the system, model parameters are adjusted in real time, transfer learning and Bayesian optimization are used to fine-tune the model and hyperparameter optimization, and heat maps are generated through Grad-CAM to provide visual interpreted images or reports to enhance interpretability.

Benefits of technology

It improves the accuracy of identification of rare diseases, enhances the generalization ability and interpretability of the model, enhances medical staff's trust in diagnostic results, and ensures the privacy and security of patient data.

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Abstract

The invention provides a blood sample automatic analysis system based on artificial intelligence. The blood sample automatic analysis system based on artificial intelligence comprises a sample collection module used for automatically collecting a blood sample of a patient, a data acquisition module comprising a sensor and an imaging device and used for acquiring an image of the blood sample or original data in other forms, and a data preprocessing module, and the noise filtering module is used for carrying out noise filtering, standardization and normalization processing on the original data. According to the blood sample automatic analysis system based on artificial intelligence, by introducing a dynamic adjustment mechanism and a self-adaptive learning algorithm, the system can adjust model parameters in real time, and especially under the condition that data is insufficient or a data set has deviation, the recognition accuracy of rare diseases is greatly improved. The dynamic model training method not only improves the generalization ability of the model, but also effectively relieves the limitation of relying on a large-scale annotation data set.
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