Dana Lin1, Michael Schwier2, Bernhard Geiger2, Esther Raithel3, and Michael Recht1
1NYU Grossman School of Medicine, New York, NY, United States, 2Siemens Medical Solutions USA, Princeton, NJ, United States, 3Siemens Healthcare GmbH, Erlangen, Germany
1NYU Grossman School of Medicine, New York, NY, United States, 2Siemens Medical Solutions USA, Princeton, NJ, United States, 3Siemens Healthcare GmbH, Erlangen, Germany
We demonstrated that automated deep learning–based rotator cuff measurements are feasible. Further research is needed to improve algorithm performance and clarify the clinical significance of the length and location differences of the measurements.
Figure 1. Example mediolateral measurements of full-thickness supraspinatus tendon tears made by the algorithm (red) and the reference annotator (green). (a) and (b) demonstrate a case where the algorithm vastly undermeasured the tear due to slice offset and segmentation failure. (c) and (d) demonstrate a different case where the measurements were concordant with similar measurements taken on the same slice.
Figure 3. Histogram of measurement location distances for ML and AP measurements. Measurement location distances are computed (and recorded) as the two distinct distances between corresponding endpoints of algorithm and reference measurements.