Method for obtaining pathological diagnosis according to color Doppler ultrasound image of thyroid nodule by applying artificial intelligence technology

Abstract

The invention relates to a method for obtaining pathological diagnosis according to a color ultrasound image of a thyroid nodule by applying an artificial intelligence technology. The thyroid nodule color Doppler ultrasound image-pathological diagnosis system has the functions of image acquisition, image preprocessing, feature extraction, image recognition and the like, and can recognize, extract and memorize the color Doppler ultrasound image features of thyroid nodules and the internal features of abnormal cells in pathological tissue staining pictures of the thyroid nodules. Color Doppler ultrasound image data of thyroid nodules directly correspond to pathological picture data, and the data can be interpreted. A color Doppler ultrasound image database and a pathological picture database of thyroid nodules are established, large-scale machine learning is carried out with sufficient data, and functions of the machine learning are improved. The system is verified and clinically tested to ensure the accuracy of the system, and pathological diagnosis is displayed. The method has the advantages that the pathological diagnosis can be obtained when nodules are seen on the thyroid color ultrasound, and the purpose of accurate treatment can be achieved. Finally, clinical diagnosis is based on pathological results under a microscope.

Description

Technical Field

[0001] This invention belongs to the field of medical technology and relates to the application of artificial intelligence in the fields of medical imaging and pathological diagnosis. Its essence is to invent a method for deriving pathological diagnosis based on high-resolution color Doppler (color ultrasound) images of thyroid nodules. Background Technology

[0002] Thyroid nodules are lesions of abnormal proliferation within the thyroid gland. They can be single or multiple, and are usually formed by abnormal cell proliferation or changes in the structure of the thyroid gland. Most nodules are benign, such as cysts and adenomas. However, about 10% of nodules are malignant, such as thyroid cancer, with papillary carcinoma being the most common.

[0003] Thyroid nodules are often discovered during physical examinations and ultrasound scans. The morphological characteristics of thyroid nodules on color Doppler ultrasound are important indicators for assessing benignity or malignancy, including shape, borders, margins, internal structure, presence of calcification, echogenicity, aspect ratio, and internal blood flow. Currently, thyroid nodules can be classified into TI-RADS risk levels based on color Doppler ultrasound. TI-RADS 2 and TI-RADS 3 nodules are highly likely to be benign; while TI-RADS 4 and TI-RADS 5 nodules are highly likely to be malignant. This experience-based classification does not achieve a definitive diagnosis unless a thyroid nodule biopsy is performed, which is invasive. In some cases, surgery may be performed too early or when intervention is unnecessary, leading to overtreatment. In other cases, the surgical approach is determined intraoperatively based on frozen section pathology, which is cumbersome and time-consuming. In some cases, discrepancies in pathological classification or subtype between the surgical frozen section and the final pathology report may only be discovered several days after surgery. This can necessitate a second surgery or additional adjuvant therapy, impacting treatment outcomes, increasing patient suffering, and wasting medical resources. Therefore, it is crucial to utilize the ultrasound imaging characteristics of thyroid nodules to determine whether they are benign or malignant, and to decide on the necessary intervention. This necessitates the development of a new diagnostic solution for thyroid nodules based on ultrasound imaging and pathology.

[0004] Artificial intelligence technology is already quite mature. Image recognition technology is formed by processing, analyzing, and understanding images using computer vision technology. By applying this technology to build models and create algorithms, and through steps such as image acquisition, image preprocessing, feature extraction, and image recognition, a medical image artificial intelligence analysis system has been established and applied to AI-powered color Doppler ultrasound image diagnosis. The AI-powered color Doppler ultrasound thyroid nodule diagnostic system shows improvements in both accuracy and sensitivity compared to simple color Doppler ultrasound.

[0005] Traditionally, postoperative tissue specimens are examined by the naked eye using a microscope to observe stained sections and make pathological diagnoses. However, with advancements in computer vision technology, computational pathology has emerged. Studies show that by analyzing digitized images of stained pathological tissues and performing machine learning on large-scale data, artificial intelligence-based pathological diagnostic systems have achieved extremely high accuracy and reliability in diagnosing cancerous tissue specimens.

[0006] However, currently, medical AI imaging diagnostic systems and postoperative pathological diagnosis exist independently, and it is not yet possible to determine the pathological diagnosis simply by seeing a lesion on an imaging image. This is precisely the direction of future development. Therefore, this invention connects an AI-powered color Doppler ultrasound imaging diagnostic system for diagnosing thyroid nodules with postoperative pathological diagnosis of thyroid nodules and related AI-powered pathological diagnostic systems. This allows for the matching of ultrasound images of thyroid nodules with corresponding pathological results, leading to a clear diagnosis and ultimately achieving the goal of precise treatment. Summary of the Invention

[0007] To address the existing problems, this invention provides a solution: applying artificial intelligence technology to create a method for deriving pathological diagnoses from ultrasound images of thyroid nodules, meaning that the presence of a thyroid nodule immediately indicates its pathological diagnosis. The invention includes: 1. Designing a software diagnostic system for thyroid nodule ultrasound images and pathology, solving comprehensive algorithm problems, and enabling deep learning; 2. This diagnostic system can identify the ultrasound image features of thyroid nodules and the abnormal cell features in their pathological tissue staining images, and establish a correlation between the two; 3. This diagnostic system has the function of forward and reverse interpretation of thyroid nodule ultrasound image data and pathological tissue image data, as well as a memory function; 4. Establishing a database of thyroid nodule ultrasound images and a database of pathological tissue staining images, with sufficient data for large-scale machine learning; 5. This diagnostic system has the ability to derive a pathological diagnosis upon the presence of a thyroid nodule on ultrasound; 6. The system's pathological diagnosis will be displayed in graphical or textual form.

[0008] Compared with existing technologies, the present invention has the following advantages: This diagnostic system is a new diagnostic technology invented by applying artificial intelligence technology to perform deep learning on a large amount of color Doppler ultrasound image data and pathological tissue image data of thyroid nodules; it can achieve the goal of obtaining pathological diagnosis results directly based on the color Doppler ultrasound images of thyroid nodules; since the pathological process of thyroid nodules is clearly understood, appropriate intervention methods can be adopted to carry out precise treatment, which can prevent both missed diagnosis and overtreatment.

[0009] The technical solution is as follows: 1. Establish an artificial intelligence-based ultrasound imaging-pathology diagnostic system for thyroid nodules. This system utilizes machine learning, natural language processing, and computer vision algorithms to construct an algorithm model that associates the ultrasound imaging features of thyroid nodules with the abnormal cell features in their pathological images, thus forming the ultrasound imaging-pathology diagnostic system for thyroid nodules. 2. This diagnostic system has functions such as image acquisition, image preprocessing, feature extraction, and image recognition and association. It can identify, extract, and memorize the ultrasound imaging features of thyroid nodules and the internal features of abnormal cells in their pathological tissue staining images. It can also perform forward and reverse interpretation of ultrasound imaging data and pathological image data of thyroid nodules. 3. Different morphological features of thyroid nodules on ultrasound images will be associated with benign or malignant nodules on pathology. The pathological results will be displayed simultaneously after the ultrasound imaging data of each patient's thyroid nodules is input. 4. Establish a database of 100,000 surgically operated thyroid nodule patients' ultrasound images and the corresponding thyroid nodules. The system includes: 1) A database of 100,000 pathological tissue images. For each thyroid nodule ultrasound image, the patient's pathological image data is input simultaneously. The ultrasound image features of the thyroid nodule are correlated with the internal features of abnormal cells in the stained pathological tissue images, and deep learning is used to refine its functionality. 2) Validation of the database to test the performance of the diagnostic system: This requires 10,000 thyroid nodule ultrasound images and corresponding clinical pathology reports from patients who have undergone surgery. The system inputs ultrasound image data of a patient's thyroid nodule and verifies whether the displayed pathological diagnosis is consistent with the clinical pathology results, ensuring accuracy even with unseen data. 3) A test database also requires 10,000 patients. Based on the patient's thyroid nodule ultrasound images, the system predicts the pathological diagnosis before surgery and compares it with the postoperative clinical pathology report to evaluate the accuracy of the diagnostic system. 4) The diagnostic results made by the AI-powered thyroid nodule ultrasound image-pathology diagnostic system are presented in graphical or textual form.

[0010] Of course, the final clinical diagnosis is based on the pathological results under a microscope.

[0011] Example Description: When a thyroid nodule is found on a patient's color Doppler ultrasound at a hospital or health checkup center, the ultrasound image data of the thyroid nodule is automatically input into the artificial intelligence thyroid nodule color Doppler ultrasound image-pathology diagnosis system. Based on the color Doppler ultrasound image characteristics of the thyroid nodule, the diagnostic system will find the corresponding pathological features, make a pathological diagnosis, and display the results.

Claims

1. A method for deriving pathological diagnosis from high-resolution color Doppler ultrasound images of thyroid nodules using artificial intelligence technology, characterized by: With the support of artificial intelligence technology, a system can make a pathological diagnosis as soon as a thyroid nodule is seen on a color Doppler ultrasound.

2. The diagnostic system according to claim 1 is a thyroid nodule color Doppler ultrasound imaging-pathology diagnostic system formed by constructing a large-scale artificial intelligence model using artificial intelligence machine learning algorithms, natural language processing algorithms, computer vision algorithms, and DeepSeek technologies. This diagnostic system has functions such as image acquisition, image preprocessing, feature extraction, image recognition, and correlation between color Doppler ultrasound images and pathological images.

3. The diagnostic system according to claims 1 and 2 can identify, extract, and memorize the internal characteristics of abnormal cells in the color Doppler ultrasound images and pathological tissue staining images of thyroid nodules. It can also perform forward and reverse interpretation of the color Doppler ultrasound image data and pathological image data of thyroid nodules. A sufficient database will be established for large-scale machine learning, and validation and testing will be conducted.

4. The diagnostic system according to the preceding claims will display the pathological diagnosis in graphical or textual form.