Trapezius Rotational Flap for Cervico-thoracic Wound Breakdown in Post-radiotherapy Necrosis : A Case Report

The management of post-radiation wound breakdown over the posterior cervico thoracic region can be a challenging task for a surgeon. The aim of the treatment is to produce a well vascularized and a low tensile flap which will close a large defect. We describe the use of the lower trapezius flap to reconstruct the wound breakdown and to obtain stable tissue coverage in a patient with postradiation necrosis. This flap minimizes the disruption of the scapula-thoracic function while preserving the range of movement over the shoulder. From the literature review, it was noted that the dorsal scapular artery (DSA) and transverse cervical artery (TCA) aid in the blood supply to the trapezius muscle and prevent local necrosis during rotation of the flap. The trapezius flap is widely accepted because of the minor donor site morbidity, large arc of rotation and adequate blood supply

A comparison between average and max-pooling in convolutional neural network for scoliosis classification

The present study carried out a comparison between average and max-pooling in Convolutional Neural Network for scoliosis classification. In the past, around 2 to 4 per cent of adolescence has been reported to suffer with scoliosis. Currently, radiographic is the clinical approach used in identifying the Cobb angle to determine the suitable treatment for this category of patients. However, over exposure to radiographic have been seen to what is leading to the risk of cancer. As such, the present study proposed the used of photogrammetric approach to overcome the radiographic side effect. The photogrammetric of human’s back is acquired to classify the scoliosis into Lenke Type 1 or Non-Type 1. Due to limited dataset, rotation, x-transition and y-transition of data augmentation was carried out. These data are classified using convolutional neural network. The convolutional neural network (CNN) consist of convolve layer, pooling layer, fully connected layer and softmax layer. Selection of the best pooling layer is important to increase the accuracy of classification. As mentioned earlier, the present study compares between average and max-pooling layer to classify the Lenke classification system. The result shows that the use of max-pooling can achieve a higher accuracy which is 84.6% compared to average pooling. Future studies are encouraged to collect more data to further prove the effectiveness of max-pooling layer