Arthrographic Anatomy of the Biceps Tendon Sheath: Potential Implications for Selective Injection

Accuracy of long head of the biceps tendon palpation by physical therapists; an ultrasonographic study

[Purpose] Examination and treatment of the long head of the biceps tendon (LHBT) requires accurate palpation. The purpose of this study was to determine physical therapists’ reliability and ability to accurately palpate the LHBT in two arm positions with ultrasound as the gold standard. [Participants and Methods] Examiners palpated the LHBT within the intertubercular groove (ITG) of the humerus on the bilateral shoulders of 32 asymptomatic (21 female; 24.3 ± 1.9 years) participants in 2 arm positions. The magnitude of distance between a marker and the border of the ITG was compared between 2 positions using an independent t-test. Percent accuracy was calculated. [Results] Inter-rater reliability was poor (position 1, k=1.04; position 2, k=0.016). Overall accuracy rate was 45.7% (117/256). Accuracy was 49.2% (63/128) and 42.2% (54/128) for testing position 1 and position 2 respectively. Mean distance palpated from the groove was M=2.58 mm (± 6.2 mm) for position 1 and M=3.77 mm (± 6.6 mm) for position 2. Inaccurate palpation occurred medially 72.3% (47/65) and 93.2% (69/74) in position 1 and position 2 respectively. [Conclusion] Results of this study did not support one arm position being more accurate over another for LHBT palpation.

A New Feature Ensemble with a Multistage Classification Scheme for Breast Cancer Diagnosis

A new and effective feature ensemble with a multistage classification is proposed to be implemented in a computer-aided diagnosis (CAD) system for breast cancer diagnosis. A publicly available mammogram image dataset collected during the Image Retrieval in Medical Applications (IRMA) project is utilized to verify the suggested feature ensemble and multistage classification. In achieving the CAD system, feature extraction is performed on the mammogram region of interest (ROI) images which are preprocessed by applying a histogram equalization followed by a nonlocal means filtering. The proposed feature ensemble is formed by concatenating the local configuration pattern-based, statistical, and frequency domain features. The classification process of these features is implemented in three cases: a one-stage study, a two-stage study, and a three-stage study. Eight well-known classifiers are used in all cases of this multistage classification scheme. Additionally, the results of the classifiers that provide the top three performances are combined via a majority voting technique to improve the recognition accuracy on both two- and three-stage studies. A maximum of 85.47%, 88.79%, and 93.52% classification accuracies are attained by the one-, two-, and three-stage studies, respectively. The proposed multistage classification scheme is more effective than the single-stage classification for breast cancer diagnosis.