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【2020 Solutions】 Datong World Science Uses Medical Imaging Recognition to Improve Breast Cancer Diagnosis Accuracy to 85%

The introduction of the 'AI Medical Imaging Identification System' assists radiologists to conveniently and quickly complete image identification tasks, reducing their workload.

Different Non-Invasive Options

Medical imaging recognition is an important task for radiologists, who must make professional judgments based on patient examination data. When a tumor is discovered, it is necessary to determine whether it is cancerous. The possible methods include non-invasive medical imaging and invasive biopsy. Although the invasive biopsy has a high accuracy rate, it also causes significant physical and psychological stress to the patient.

Currently, imaging recognition can only determine the presence of tumors, not yet able to detect the difference between benign and malignant tumors. To distinguish benign and malignant breast tumors, Datong World Science Company has assisted the Imaging Department of Changhua Christian Hospital, the first hospital in Taiwan to introduce the 'AI Medical Imaging Identification System'. This system has increased the accuracy rate of artificial intelligence mammography in distinguishing benign from malignant tumors to 85%, allowing for a shift from the original binary approach to a probability expression of BI-RADS grading.

AI Medical Imaging Identification System Enhances Breast Cancer Diagnosis Accuracy to 85%

The AI medical imaging recognition system can assist radiologists in making quick readings. Initially, it will target mammography. When a tumor is detected, determining whether it is cancerous requires a pathological biopsy or mammography. Pathological biopsy is invasive and, although more accurate, carries higher tangible and intangible costs.

Moreover, it helps improve the efficiency and accuracy of mammography readings. Furthermore, optimizing the mammography reading process will reduce the workload on radiologists and decrease the waiting time for patients for examination results. Additionally, with the aid of artificial intelligence, it helps reduce differences in radiologists' subjective judgments and prevent human errors, helping the institution to establish common standards and enhance collaborative efficiency among doctors from different specializations.

▲CNN (Convolutional Neural Network) Model

In addition to assisting doctors in making quick readings, here are summarized benefits of introducing the AI Medical Imaging Identification System:

1. Provides AI-assisted BI-RADS grading for mammography, helping radiologists in interpretation.

2. Optimizes medical imaging recognition processes, enhancing the degree of automation of existing procedures.

3. Uses local medical images to retrain models.

4. Adopts superior CNN models to improve accuracy and stability of the system.

5. Defines the relationship between BI-RADS grading and AI's readings of benign and malignant tumors; transitioning from a basic dichotomy to a probability representation in BI-RADS grading.

The prerequisite for deploying artificial intelligence in medical assistant decision-making is that the accuracy must exceed 85%, providing a valuable reference for radiologists. With the support of artificial intelligence, the time for radiologists to interpret a single x-ray mammography image and assign a BI-RADS grade has been reduced to 50% of the original time, from about 10 minutes to under 5 minutes, offering an efficient and accurate AI-assisted outcome.

▲Chairman Baiyan Shen of Datong World Technology Co., Ltd.

「Translated content is generated by ChatGPT and is for reference only. Translation date:2024-05-19」

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