Diagnostic accuracy of an iPhone DICOM viewer for the interpretation of magnetic resonance imaging of the knee.

ABSTRACT

OBJECTIVE: To evaluate the diagnostic performance of viewing magnetic resonance (MR) images on a handheld mobile device compared with a conventional radiology workstation for the diagnosis of intra-articular knee pathology. DESIGN: Prospective comparison study. SETTING: Tertiary care center. PATIENTS Fifty consecutive subjects who had MR imaging of the knee followed by knee arthroscopy were prospectively evaluated. INTERVENTIONS: Two musculoskeletal radiologists independently reviewed each MR study using 2 different viewers the OsiriX DICOM viewer software on an Apple iPhone 3GS device and eFilm Workstation software on a conventional picture archiving and communications system workstation. MAIN OUTCOME MEASURES: Sensitivity and specificity of the iPhone and workstation interpretations was performed using knee arthroscopy as the reference standard. Intraobserver concordance and agreement between the iPhone and workstation interpretations were determined. RESULTS: There was no statistically significant difference between the 2 devices for each paired comparison of diagnostic performance. For the iPhone interpretations, sensitivity ranged from 77% (13 of 17) for the lateral meniscus to 100% (17 of 17) for the anterior cruciate ligament. Specificity ranged from 74% (14 of 19) for cartilage to 100% (50 of 50) for the posterior cruciate ligament. There was a very high level of interobserver and intraobserver agreement between devices and readers. The iPhone reads took longer than the corresponding workstation reads, with a significant mean difference between the iPhone and workstation reads of 3.98 minutes (P < 0.001). CONCLUSIONS: The diagnostic performance of interpreting MR images on a handheld mobile device for the assessment of intra-articular knee pathology is similar to that of a conventional radiology workstation, however, requires a longer viewing time. CLINICAL RELEVANCE Timely and accurate interpretation of complex medical images using mobile device solutions could result in new workflow efficiencies and ultimately improve patient care.