Prospective Evaluation of Innovative Force Assessing Firmware in Simulation to Improve the Technical Competence of Surgical Trainees.

INTRODUCTION: Suturing is an integral part of all surgeries. In minimal access surgery, the force exerted is based only on visual perception (tautness of the thread and degree of tissue deformation). An unbalanced suture force can cause tissue rupture or cut-through resulting in avoidable morbidity and mortality. There is a need to find ways of improving surgical dexterity and finesse without adversely affecting patient outcomes. AIM: We aimed to calculate the knot-tying force in minimal access pancreatic surgery (MAPS) performed by experienced surgeons (ES) and use this information to develop a surgical suturing model to train the surgical trainees. We have developed a firmware for force sensor calibration and post-data analysis, using which we aimed to compare the differences in forces applied by a trainee as compared to ES. RESULTS: Our technology showed that, as compared to the ES, the trainee's (TS) knot was unbalanced with significant differences in force applied per knot for each of the knots (P < 0.01). The shape of the Force curve for each suture was also different for the TS as compared to the ES. After using the training tool, the forces applied by the TS and the Force curve for the whole suture were similar to those of the ES. CONCLUSION: Our firmware promises to be an excellent training tool for organ anastomosis. Considering the complexity and likely complications of MAPS, it is a sine qua non that the surgeon be highly experienced and skilled. Surgical simulation is attractive because it avoids the use of patients for skills practice and provides relevant technical training for trainees before they can safely operate on humans.

Automatic classification of focal lesions in ultrasound liver images using principal component analysis and neural networks.

Ultrasound Medical Imaging is currently the most popular modality for diagnostic application. This imaging technique has been used for the detecting abnormalities associated with abdominal organs like liver, kidney, uterus etc. In this paper, the possibilities of automatic classification of the ultrasound liver images into four classes-Normal, Cyst, Benign and Malignant masses, using texture features are explored. These texture features are extracted using the various statistical and spectral methods. The optimal feature selection process is carried out manually to pick the best discriminating features from the extracted texture parameters. Also, the method of principal component analysis is used to extract the principal features or directions of maximum information from the data set there by automatically selecting the optimal features. Using these optimal features, a final combined feature set is formed and is employed for classification of the liver lesions into respective classes. K-means clustering and neural network based automatic classifiers are employed in this process. The classifier design and its performance are studied. This paper summarizes the various statistical and spectral texture parameter extraction processes, optimal feature selection techniques and automated classification procedures involved in our work.