Clinical StudyDetecting ossification of the posterior longitudinal ligament on plain radiographs using a deep convolutional neural network: a pilot study
Introduction
Ossification of the posterior longitudinal ligament (OPLL) is a disease of cervical origin causing neurological impairments manifesting as sensorimotor symptoms [1]. Plain radiographs are initially used to diagnose OPLL, including functional radiographs. However, this method alone may be insufficient to achieve at a definitive diagnosis due to subtle radiological changes on plain cervical spine radiographs [2]. Thus, it is not rare to take computed tomography (CT) and magnetic resonance imaging (MRI) images to confirm the radiological diagnosis. However, delays in diagnosis of OPLL are often made by non-spine physicians when only relying on plain radiographic images. Even spine experts experience difficulties in arriving at an accurate diagnosis when using only plain radiographs. Therefore, increasing the diagnostic accuracy of plain radiographs will reduce delays in the diagnosis of OPLL. Additionally, improving the diagnostic accuracy of OPLL may reduce the risk of deterioration of the neurologic symptoms of patients with OPLL due to minor trauma, such as falls. Furthermore, increasing the diagnostic accuracy of radiographic images will lead to a decreased usage of CT or MRI, leading to reduced time, cost, and radiation exposure. Thus, an improved accuracy of the diagnosis by plain radiographs will be beneficial to institutions that have limited medical resources.
Convolutional neural networks (CNNs) are a method of machine learning recently applied in the medical field [3,4], including research using MRI images in spinal surgery field [5], [6], [7]. CNNs demonstrate a high capability for the diagnostic image analysis of plain radiographs, and can be applied in various medical fields. In patients with lung cancer, a 20% increase in accuracy was found when screening with plain chest radiographs using machine learning as compared to that with the current diagnostic standard [8]. Additionally, improvements in the diagnostic accuracy of upper and lower extremity fractures have been observed when machine learning was used [9,10].
Despite its advantages, research regarding the application of CNNs in the diagnosis of cervical spinal diseases is under explored. Therefore, this study aimed to evaluate the diagnostic performance of the CNN model based on plain radiographic images and compare it with that of general orthopedic surgeons and spine experts.
Section snippets
Study participants and input image
We retrospectively examined the medical records of patients who underwent surgical treatment of the cervical or lumbar spine at our institution between January 2016 and December 2017. This study included the records of 50 patients with symptomatic cervical OPLL who underwent surgery with thickness of ossification more than 3 mm, and 50 control patients who underwent surgery for lumbar spine diseases that manifested asymptomatic cervical spondylotic changes. Patients with the following
Patient demographics
The patient characteristics are shown in Table 1A and B. The mean age was 67.1 years (standard deviation, 10.0 years) and 61% of the patients were men.
Performance evaluation for the diagnosis by the CNN model and surgeons
The percentage of correct diagnoses made by the general orthopedic surgeons, spine specialists, and the CNN model based on neutral/flexion/extension radiographs for the 20 cases (10 OPLL and 10 control) in the test dataset are shown in Table 2. Fig. 2 shows the ROC curve of the CNN model, which had an AUC of 0.924. The CNN model had the highest
Discussion
This study aimed to evaluate the diagnostic accuracy of plain radiographs with machine learning in the diagnosis of OPLL, particularly in comparison to general orthopedic and spine surgeons. We found an excellent AUC of the diagnostic accuracy of plain radiographs, and its superior accuracy compared to that of general orthopedic and spine surgeons.
An increasing trend has been recently observed in the application of image recognition with machine learning [3,4]. These technologies have been
Declaration of competing interests
Support for this study was provided by Japan Agency for Medical Research and Development (JP21ek1126633).
Acknowledgment
Support for this study was provided by Japan Agency for Medical Research and Development (JP21ek1126633).
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Cited by (4)
Radiomics in Spine Surgery
2023, International Journal of Spine Surgery
FDA device/drug status: Not applicable.
Author disclosures: TO: Nothing to disclose. TY: Grant: Japan Agency for Medical Research and Development (C, Paid directly to institution). JO: Nothing to disclose. NS: Nothing to disclose. TA: Nothing to disclose. TS: Nothing to disclose. MH: Nothing to disclose. SM: Nothing to disclose. TT: Nothing to disclose. TM: Nothing to disclose. TO: Nothing to disclose. TY: Nothing to disclose. HO: Nothing to disclose. TH: Nothing to disclose. HI: Nothing to disclose. YN: Nothing to disclose. AO: Grant: Japan Agency for Medical Research and Development (C, Paid directly to institution).
Ethical review committee statement: The study protocol was approved by the institutional review boards of Tokyo Medical and Dental University. (M2020-065)
A statement of the location where the work was performed: All research work has been performed in Tokyo Medical and Dental University.