Machine learning enables further and more accurate analysis of orthopedic images

Implementing machine learning (ML) in medical image analysis is not new. Radiologists actively utilize automated tools to significantly improve every step of the medical imaging pathway. This includes image acquisition and reconstruction for analysis and interpretation.

The results of image analysis based on machine learning are crucial for addressing key challenges in multiple medical fields such as heart disease, lung disease, and ophthalmology, such as diagnosis and treatment planning. Orthopedics is no exception. From bone reconstruction to joint segmentation and then to cancer recognition, machine learning can help orthopedic doctors accelerate the transition to value based care.

3D technology helps align bones

According to the World Health Organization, up to 25% of patients suffer from surgical complications. In addition, one million people died during or after surgery. To alleviate this thorny problem, doctors should strive to improve the accuracy of image analysis, thereby enhancing the accuracy of surgical planning. Machine learning can intervene and assist them.

In plastic surgery, creating a 3D model of the patient's anatomical site is crucial for guiding surgeons during the surgical process. However, reconstructing surfaces from sparse point sets can be challenging. For example, when a patient's long bone or lower limb fractures.

In this case, it is necessary to perform initial alignment on the skeletal parts. Moreover, systems driven by computer vision can eliminate the need for manual operation by surgeons, thus avoiding situations of small misalignment. The generated virtual model will provide key guidance for surgery or, if necessary, implant design by indicating the exact bone position and direction.

Accurate detection of bone cancer

Machine learning is an effective technical tool in oncology and can also be used to identify very common bone tumors and osteosarcoma. Although not as common as other types of cancer, metastatic bone malignancies may occur after breast cancer or prostate cancer. Early detection of these skeletal metastases is significantly helpful in determining prognosis and personalized treatment.

Early cancer recognition began with computer vision driven bone segmentation, which was separated from surrounding anatomical parts in 2D format. Then, the continuous 2D images are automatically stitched onto the 3D surface of bones and other bone related structures. All of these make it easier for machine learning to locate abnormal areas near cartilage and within bones, and identify bone areas with high fracture risk.

Further classification of detected metastatic lesions was achieved through an algorithm based on Support Vector Machine (SVM), which was previously trained on a set of manually classified normal and abnormal lesions. Afterwards, doctors can immediately undergo treatment to improve the survival rate and quality of life of cancer patients.

Automatic bone and joint segmentation

From the previous section, we can understand that segmentation plays an important role in medical image analysis. Organ measurement, isolation between organs and tissues, cell counting - Artificial intelligence can automate these tasks and other critical tasks. Machine learning driven segmentation is used in plastic surgery for precise bone and joint examinations, knee and hip replacement plans, lesion detection, shoulder surgery preparation, and other medical procedures.

Of course, to achieve fair results, we can use some ready-made solutions, but tailor-made machine learning analytics will help address the most challenging challenges. One of them is image degradation caused by metal artifacts. Previously trained on synthetic data generated through simulation based analysis, systems with machine learning capabilities can improve the results of real-time orthopedic image processing.

In addition to severely degraded images, machine learning automation can also effectively analyze images with osteophytes, cartilage loss, or merged bones by focusing on anatomical positions that are more prone to algorithmic errors. To ensure the accuracy of bone segmentation at the pixel level, complex classical algorithms can be used to perform post-processing.

Quick response

The image analysis technology driven by machine learning technology is a leap in the field of plastic surgery, aimed at improving diagnosis, achieving ultra personalized treatment, improving the survival rate of terminally ill patients, and accelerating recovery time.

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