Artificial Intelligence: Is It Armageddon for Breast Radiologists?

Preliminary data on artificial intelligence tool in magnetic resonance imaging assessment of degenerative pathologies of lumbar spine

PURPOSE

To evaluate the ability of an artificial intelligence (AI) tool in magnetic resonance imaging (MRI) assessment of degenerative pathologies of lumbar spine using radiologist evaluation as a gold standard.

METHODS

Patients with degenerative pathologies of lumbar spine, evaluated with MRI study, were enrolled in a retrospective study approved by local ethical committee. A comprehensive software solution (CoLumbo; SmartSoft Ltd., Varna, Bulgaria) designed to label the segments of the lumbar spine and to detect a broad spectrum of degenerative pathologies based on a convolutional neural network (CNN) was employed, utilizing an automatic segmentation. The AI tool efficacy was compared to data obtained by a senior neuroradiologist that employed a semiquantitative score. Chi-square test was used to assess the differences among groups, and Spearman's rank correlation coefficient was calculated between the grading assigned by radiologist and the grading obtained by software. Moreover, agreement was assessed between the value assigned by radiologist and software.

RESULTS

Ninety patients (58 men; 32 women) affected with degenerative pathologies of lumbar spine and aged from 60 to 81 years (mean 66 years) were analyzed. Significant correlations were observed between grading assigned by radiologist and the grading obtained by software for each localization. However, only when the localization was L2-L3, there was a good correlation with a coefficient value of 0.72. The best agreements were obtained in case of L1-L2 and L2-L3 localizations and were, respectively, of 81.1% and 72.2%. The lowest agreement of 51.1% was detected in case of L4-L5 locations. With regard canal stenosis and compression, the highest agreement was obtained for identification of in L5-S1 localization.

CONCLUSIONS

AI solution represents an efficacy and useful toll degenerative pathologies of lumbar spine to improve radiologist workflow.

Separating Hope from Hype: Artificial Intelligence Pitfalls and Challenges in Radiology

Although recent scientific studies suggest that artificial intelligence (AI) could provide value in many radiology applications, much of the hard engineering work required to consistently realize this value in practice remains to be done. In this article, we summarize the various ways in which AI can benefit radiology practice, identify key challenges that must be overcome for those benefits to be delivered, and discuss promising avenues by which these challenges can be addressed.