Lee-Ren Yeh1, Yang Zhang2, Jeon-Hor Chen3,4, Yan-Lin Liu2, An-Chi Wang5, Jie-Yu Yang5, Wei-Cheng Yeh6, Chiu-Shih Cheng1, Li-Kuang Chen2, Min-Ying Su2,7. 1. Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung, Taiwan. 2. Department of Radiological Sciences, University of California, 164 Irvine Hall, Irvine, CA, 92697-5020, USA. 3. Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung, Taiwan. jeonhc@hs.uci.edu. 4. Department of Radiological Sciences, University of California, 164 Irvine Hall, Irvine, CA, 92697-5020, USA. jeonhc@hs.uci.edu. 5. Department of Radiology, Chi-Mei Medical Center, Tainan, Taiwan. 6. Department of Radiology, E-Da Cancer Hospital, Kaohsiung, Taiwan. 7. Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan.
Abstract
PURPOSE: To improve the performance of less experienced clinicians in the diagnosis of benign and malignant spinal fracture on MRI, we applied the ResNet50 algorithm to develop a decision support system. METHODS: A total of 190 patients, 50 with malignant and 140 with benign fractures, were studied. The visual diagnosis was made by one senior MSK radiologist, one fourth-year resident, and one first-year resident. The MSK radiologist also gave the binary score for 15 qualitative imaging features. Deep learning was implemented using ResNet50, using one abnormal spinal segment selected from each patient as input. The T1W and T2W images of the lesion slice and its two neighboring slices were considered. The diagnostic performance was evaluated using tenfold cross-validation. RESULTS: The overall reading accuracy was 98, 96, and 66% for the senior MSK radiologist, fourth-year resident, and first-year resident, respectively. Of the 15 imaging features, 10 showed a significant difference between benign and malignant groups with p < = 0.001. The accuracy achieved by using the ResNet50 deep learning model for the identified abnormal vertebral segment was 92%. Compared to the first-year resident's reading, the model improved the sensitivity from 78 to 94% (p < 0.001) and the specificity from 61 to 91% (p < 0.001). CONCLUSION: Our deep learning-based model may provide information to assist less experienced clinicians in the diagnosis of spinal fractures on MRI. Other findings away from the vertebral body need to be considered to improve the model, and further investigation is required to generalize our findings to real-world settings.
PURPOSE: To improve the performance of less experienced clinicians in the diagnosis of benign and malignant spinal fracture on MRI, we applied the ResNet50 algorithm to develop a decision support system. METHODS: A total of 190 patients, 50 with malignant and 140 with benign fractures, were studied. The visual diagnosis was made by one senior MSK radiologist, one fourth-year resident, and one first-year resident. The MSK radiologist also gave the binary score for 15 qualitative imaging features. Deep learning was implemented using ResNet50, using one abnormal spinal segment selected from each patient as input. The T1W and T2W images of the lesion slice and its two neighboring slices were considered. The diagnostic performance was evaluated using tenfold cross-validation. RESULTS: The overall reading accuracy was 98, 96, and 66% for the senior MSK radiologist, fourth-year resident, and first-year resident, respectively. Of the 15 imaging features, 10 showed a significant difference between benign and malignant groups with p < = 0.001. The accuracy achieved by using the ResNet50 deep learning model for the identified abnormal vertebral segment was 92%. Compared to the first-year resident's reading, the model improved the sensitivity from 78 to 94% (p < 0.001) and the specificity from 61 to 91% (p < 0.001). CONCLUSION: Our deep learning-based model may provide information to assist less experienced clinicians in the diagnosis of spinal fractures on MRI. Other findings away from the vertebral body need to be considered to improve the model, and further investigation is required to generalize our findings to real-world settings.
Authors: Benedikt J Schwaiger; Alexandra S Gersing; Thomas Baum; Christian R Krestan; Jan S Kirschke Journal: Semin Musculoskelet Radiol Date: 2016-11-14 Impact factor: 1.777
Authors: Anthony M Avellino; Fred A Mann; M Sean Grady; Jens R Chapman; Richard G Ellenbogen; Tord D Alden; Sohail K Mirza Journal: Childs Nerv Syst Date: 2004-12-18 Impact factor: 1.475