Rare Median Nerve and Digital Nerve Tumor Resection and Distal Nerve Transfers: A Report of 2 Cases.

CASES: We present 2 cases of median nerve reconstruction using distal nerve transfers after resection of unusual benign median nerve tumors. Critical sensation was restored in case 1 by transferring the fourth common digital nerve to first web digital nerves. Thumb opposition was regained by transferring the abductor digiti minimi ulnar motor nerve branch to the recurrent median motor nerve branch. Critical sensation was restored in case 2 by transferring the long finger ulnar digital nerve to the index finger radial digital nerve. CONCLUSION: Distal nerve transfers, even with short grafts, are reliable median nerve deficit treatments, sparing the need for larger autologous nerve grafts and late tendon opponensplasties.

Automatic detection and diagnosis of sacroiliitis in CT scans as incidental findings.

Early diagnosis of sacroiliitis may lead to preventive treatment which can significantly improve the patient's quality of life in the long run. Oftentimes, a CT scan of the lower back or abdomen is acquired for suspected back pain. However, since the differences between a healthy and an inflamed sacroiliac joint in the early stages are subtle, the condition may be missed. We have developed a new automatic algorithm for the diagnosis and grading of sacroiliitis CT scans as incidental findings, for patients who underwent CT scanning as part of their lower back pain workout. The method is based on supervised machine and deep learning techniques. The input is a CT scan that includes the patient's pelvis. The output is a diagnosis for each sacroiliac joint. The algorithm consists of four steps: (1) computation of an initial region of interest (ROI) that includes the pelvic joints region using heuristics and a U-Net classifier; (2) refinement of the ROI to detect both sacroiliiac joints using a four-tree random forest; (3) individual sacroiliitis grading of each sacroiliiac joint in each CT slice with a custom slice CNN classifier, and; (4) sacroiliitis diagnosis and grading by combining the individual slice grades using a random forest. Experimental results on 484 sacroiliiac joints yield a binary and a 3-class case classification accuracy of 91.9% and 86%, a sensitivity of 95% and 82%, and an Area-Under-the-Curve of 0.97 and 0.57, respectively. Automatic computer-based analysis of CT scans has the potential of being a useful method for the diagnosis and grading of sacroiliitis as an incidental finding.