A neural network model for detection and classification of lumbar spinal stenosis on MRI.

OBJECTIVES: To develop a three-stage convolutional neural network (CNN) approach to segment anatomical structures, classify the presence of lumbar spinal stenosis (LSS) for all 3 stenosis types: central, lateral recess and foraminal and assess its severity on spine MRI and to demonstrate its efficacy as an accurate and consistent diagnostic tool. METHODS: The three-stage model was trained on 1635 annotated lumbar spine MRI studies consisting of T2-weighted sagittal and axial planes at each vertebral level. Accuracy of the model was evaluated on an external validation set of 150 MRI studies graded on a scale of absent, mild, moderate or severe by a panel of 7 radiologists. The reference standard for all types was determined by majority voting and in case of disagreement, adjudicated by an external radiologist. The radiologists' diagnoses were then compared to the diagnoses of the model. RESULTS: The model showed comparable performance to the radiologist average both in terms of the determination of presence/absence of LSS as well as severity classification, for all 3 stenosis types. In the case of central canal stenosis, the sensitivity, specificity and AUROC of the CNN were (0.971, 0.864, 0.963) for binary (presence/absence) classification compared to the radiologist average of (0.786, 0.899, 0.842). For lateral recess stenosis, the sensitivity, specificity and AUROC of the CNN were (0.853, 0.787, 0.907) compared to the radiologist average of (0.713, 0.898, 805). For foraminal stenosis, the sensitivity, specificity and AUROC of the CNN were (0.942, 0.844, 0.950) compared to the radiologist average of (0.879, 0.877, 0.878). Multi-class severity classifications showed similarly comparable statistics. CONCLUSIONS: The CNN showed comparable performance to radiologist subspecialists for the detection and classification of LSS. The integration of neural network models in the detection of LSS could bring higher accuracy, efficiency, consistency, and post-hoc interpretability in diagnostic practices.

How Can Pelvic MRI with Diffusion-Weighted Imaging Help My Pregnant Patient?

The purpose of this review is to explain how diffusion-weighted imaging (DWI) is used during magnetic resonance imaging (MRI) exams in pregnant patients for specific maternal indications, including evaluation of acute pelvic pain, adnexal masses, cancer diagnosis and staging, and morbidly adherent placenta. While ultrasound is often the appropriate initial imaging for evaluating a pregnant patient, MRI can be helpful when a pelvic ultrasound is indeterminate. MRI has advantages in that it does not use ionizing radiation and has shown no known deleterious effects to the fetus. The use of gadolinium-based contrast is controversial during pregnancy. DWI is a functional sequence performed during an MRI exam, which is valuable in the absence of gadolinium contrast, and can increase the visibility of inflammation, abscesses, and tumors. Case examples will be presented to demonstrate the utility and added value of DWI over conventional anatomic T1- and T2-weighted imaging in diagnosis of maternal disease in the pregnant patient's pelvis.