Tiegs-Heiden, Christin A

Purpose To develop a machine learning model capable of identifying subscapularis tears before surgery based on imaging and physical examination findings. Methods Between 2010 and 2020, 202 consecutive shoulders underwent arthroscopic rotator cuff repair by a single surgeon. Patient demographics, physical examination findings (including range of motion, weakness with internal rotation, lift/push‐off test, belly press test, and bear hug test), and imaging (including direct and indirect signs of tearing, biceps status, fatty atrophy, cystic changes, and other similar findings) were included for model creation. Results Sixty percent of the shoulders had partial or full thickness tears of the subscapularis verified during surgery (83% of these were upper third). Using only preoperative imaging‐related parameters, the XGBoost model demonstrated excellent performance at predicting subscapularis tears (c‐statistic, 0.84; accuracy, 0.85; F1 score, 0.87). The top 5 features included direct signs related to the presence of tearing as evidenced on magnetic resonance imaging (MRI) (changes in tendon morphology and signal), as well as the quality of the MRI and biceps pathology. Conclusions In this study, machine learning was successful in predicting subscapularis tears by MRI alone in 85% of patients, and this accuracy did not decrease by isolating the model to the top features. The top five features included direct signs related to the presence of tearing as evidenced on MRI (changes in tendon morphology and signal), as well as the quality of the MRI and biceps pathology. Last, in advanced modeling, the addition of physical examination or patient characteristics did not make a significant difference in the predictive ability of this model. Level of Evidence Level III, diagnostic case‐control study.

Background: Magnetic resonance imaging (MRI) is considered to be the gold standard for imaging of osteochondritis dissecans (OCD). Purpose/Hypothesis: The purpose was to determine the additional value of a preoperative computed tomography (CT) scan in adolescent patients with capitellar OCD of the elbow. Consistent with the fact that OCD is a lesion involving the subchondral bone, the hypothesis was that CT would be superior to MRI for imaging OCD of the capitellum. Study Design: Cohort study (diagnosis); Level of evidence, 3. Methods: All patients being treated surgically for an OCD of the capitellum between 2006 and 2016 at one institution were reviewed for preoperative imaging. A total of 28 patients met the inclusion criteria. Corresponding MRI and CT scans were compared retrospectively. Multiple parameters were recorded, with special emphasis on OCD lesion size, fragmentation, and tilt as well as joint surface integrity, loose bodies, and osteophytes. Results: The OCD lesions were best seen on CT scans, whereas MRI T1-weighted images overestimated and T2-weighted images underestimated the size of defects. A subchondral fracture nonunion was found on CT scans in 18 patients, whereas this was seen on MRI T1-weighted images in only 2 patients ( P < .001) and MRI T2-weighted images in 4 patients ( P < .001). Fragmentation of the OCD fragment was found on CT scans in 17 patients but on MRI scans in only 9 patients ( P = .05). Osteophytes as a sign of secondary degenerative changes were seen on CT scans in 24 patients and were seen on MRI scans in 15 patients ( P = .02). Altogether, only 51 of 89 secondary changes including loose bodies, effects on the radial head and ulnohumeral joint, and osteophytes that were seen on CT scans were also seen on MRI scans ( P = .002). Conclusion: OCD fragmentation and secondary changes were more often diagnosed on CT. These factors indicate OCD instability or advanced OCD stages, which are indications for surgery. In an adolescent who is considered at risk for OCD (baseball, gymnastics, weightlifting, tennis) and who has lateral elbow joint pain with axial or valgus load bearing, CT is our imaging modality of choice for diagnosing and staging OCD of the capitellum.

AbstractBackgroundDual‐energy CT with virtual noncalcium (VNCa) images allows the evaluation of focal intramedullary bone marrow involvement in patients with multiple myeloma. However, current commercial VNCa techniques suffer from excessive image noise and artifacts due to material decomposition used in synthesizing VNCa images.ObjectivesIn this work, we aim to improve VNCa image quality for the assessment of focal multiple myeloma, using an Artificial intelligence based Generalizable Algorithm for mulTi‐Energy CT (AGATE) method.Materials and methodsAGATE method used a custom dual‐task convolutional neural network (CNN) that concurrently carries out material classification and quantification. The material classification task provided an auxiliary regularization to the material quantification task. CNN parameters were optimized using custom loss functions that involved cross‐entropy, physics‐informed constraints, structural redundancy in spectral and material images, and texture information in spectral images. For training data, CT phantoms (diameters 30 to 45 cm) with tissue‐mimicking inserts were scanned on a third generation dual‐source CT system. Scans were performed at routine dose and half of the routine dose. Small image patches (i.e., 40 × 40 pixels) of tissue‐mimicking inserts with known basis material densities were extracted for training samples. Numerically simulated insert materials with various shapes increased diversity of training samples. Generalizability of AGATE was evaluated using CT images from phantoms and patients. In phantoms, material decomposition accuracy was estimated using mean‐absolute‐percent‐error (MAPE), using physical inserts that were not used during the training. Noise power spectrum (NPS) and modulation transfer function (MTF) were compared across phantom sizes and radiation dose levels. Five patients with multiple myeloma underwent dual‐energy CT, with VNCa images generated using a commercial method and AGATE. Two fellowship‐trained musculoskeletal radiologists reviewed the VNCa images (commercial and AGATE) side‐by‐side using a dual‐monitor display, blinded to VNCa type, rating the image quality for focal multiple myeloma lesion visualization using a 5‐level Likert comparison scale (−2 = worse visualization and diagnostic confidence, −1 = worse visualization but equivalent diagnostic confidence, 0 = equivalent visualization and diagnostic confidence, 1 = improved visualization but equivalent diagnostic confidence, 2 = improved visualization and diagnostic confidence). A post hoc assignment of comparison ratings was performed to rank AGATE images in comparison to commercial ones.ResultsAGATE demonstrated consistent material quantification accuracy across phantom sizes and radiation dose levels, with MAPE ranging from 0.7% to 4.4% across all testing materials. Compared to commercial VNCa images, the AGATE‐synthesized VNCa images yielded considerably lower image noise (50–77% noise reduction) without compromising noise texture or spatial resolution across different phantom sizes and two radiation doses. AGATE VNCa images had markedly reduced area under NPS curves and maintained NPS peak frequency (0.7 lp/cm to 1.0 lp/cm), with similar MTF curves (50% MTF at 3.0 lp/cm). In patients, AGATE demonstrated reduced image noise and artifacts with improved delineation of focal multiple myeloma lesions (all readers comparison scores indicating improved overall diagnostic image quality [scores 1 or 2]).ConclusionsAGATE demonstrated reduced noise and artifacts in VNCa images and ability to improve visualization of bone marrow lesions for assessing multiple myeloma.