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The use of musculoskeletal ultrasonography (MSUS) in guiding subdeltoid injection has been shown to improve outcome up to 6 weeks in a few small studies. A recent meta-analysis identified the need for further studies with longer-term outcome and larger sample size. This randomized prospective study assessed whether clinic-based MSUS can significantly improve diagnostic accuracy in shoulder pain and whether MSUS-guided shoulder injection results in improved long-term outcomes. One hundred consecutive patients with 125 painful shoulders were recruited. Patients were randomized to receive either sonographic assessment with consequent palpation-guided injection (Group 1, n = 66) or sonographic assessment with a MSUS-guided injection of 40 mg of methylprednisolone acetate (Group 2, n = 59). A blinded rheumatologist (ADF) performed clinical assessments at baseline, 6 and 12 weeks including shoulder function tests (SFTs) (Hawkins-Kennedy test, supraspinatus tendon tenderness), physician global assessment (PGA) and patient visual analogue scores (VAS) for pain (0-10). Eighty patients with 90 symptomatic shoulders completed 12-week follow-up. Twenty patients, 11 (20 shoulders) from the palpation-guided group and 9 (15 shoulders) from the MSUS-guided group, were excluded at 6 weeks either due to requirement for repeat injection or due to surgical referral. Mean age for patients was 57.7 years, and 65 % patients were female; mean shoulder pain duration was 18 weeks (range 14-22 weeks). SFTs, patient VAS and PGA scores for pain improved significantly from baseline in both groups with significantly greater improvements in the MSUS-guided group (44 shoulders) compared to the palpation-guided group (46 shoulders) in all parameters at 6 (p < 0.01) and 12 weeks (p < 0.05). The use of MSUS in guiding subdeltoid injection has been shown to improve outcome up to 6 weeks in a few small studies. A recent meta-analysis identified the need for further studies with longer-term outcome and larger sample size.

This paper presents an intelligent approach for the detection of Melanoma—a deadly skin cancer. The first step in this direction includes the extraction of the textural features of the skin lesion along with the color features. The extracted features are used to train the Multilayer Feed-Forward Artificial Neural Networks. We evaluate the trained networks for the classification of test samples. This work entails three sets of experiments including 50 % , 70 % and 90 % of the data used for training, while the remaining 50 % , 30 % , and 10 % constitute the test sets. Haralick’s statistical parameters are computed for the extraction of textural features from the lesion. Such parameters are based on the Gray Level Co-occurrence Matrices (GLCM) with an offset of 2 , 4 , 8 , 12 , 16 , 20 , 24 and 28, each with an angle of 0 , 45 , 90 and 135 degrees, respectively. In order to distill color features, we have calculated the mean, median and standard deviation of the three color planes of the region of interest. These features are fed to an Artificial Neural Network (ANN) for the detection of skin cancer. The combination of Haralick’s parameters and color features have proven better than considering the features alone. Experimentation based on another set of features such as Asymmetry, Border irregularity, Color and Diameter (ABCD) features usually observed by dermatologists has also been demonstrated. The ‘D’ feature is however modified and named Oblongness. This feature captures the ratio between the length and the width. Furthermore, the use of modified standard deviation coupled with ABCD features improves the detection of Melanoma by an accuracy of 93.7 %

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