The development of an eye movement-based deep learning system for laparoscopic surgical skills assessment.

The development of valid, reliable, and objective methods of skills assessment is central to modern surgical training. Numerous rating scales have been developed and validated for quantifying surgical performance. However, many of these scoring systems are potentially flawed in their design in terms of reliability. Eye-tracking techniques, which provide a more objective investigation of the visual-cognitive aspects of the decision-making process, recently have been utilized in surgery domains for skill assessment and training, and their use has been focused on investigating differences between expert and novice surgeons to understand task performance, identify experienced surgeons, and establish training approaches. Ten graduate students at the National Taiwan University of Science and Technology with no prior laparoscopic surgical skills were recruited to perform the FLS peg transfer task. Then k-means clustering algorithm was used to split 500 trials into three dissimilar clusters, grouped as novice, intermediate, and expert levels, by an objective performance assessment parameter incorporating task duration with error score. Two types of data sets, namely, time series data extracted from coordinates of eye fixation and image data from videos, were used to implement and test our proposed skill level detection system with ensemble learning and a CNN algorithm. Results indicated that ensemble learning and the CNN were able to correctly classify skill levels with accuracies of 76.0% and 81.2%, respectively. Furthermore, the incorporation of coordinates of eye fixation and image data allowed the discrimination of skill levels with a classification accuracy of 82.5%. We examined more levels of training experience and further integrated an eye tracking technique and deep learning algorithms to develop a tool for objective assessment of laparoscopic surgical skill. With a relatively unbalanced sample, our results have demonstrated that the approach combining the features of visual fixation coordinates and images achieved a very promising level of performance for classifying skill levels of trainees.

Foreign objects detection using deep learning techniques for graphic card assembly line.

An assembly is a process in which operators and machines manufacture products from semi-finished components into finished goods. It is important to conduct quality control at the end of the assembly line and ensure that no foreign object is put on the conveyor. This study uses a case of foreign object detection in graphics card assembly line to create models which is capable of detecting and marking foreign objects using convolutional neural network (CNN) models. This study uses Inception Resnet v2 to conduct the foreign object classification and Attention Residual U-net++ for the foreign object segmentation. Both benchmark datasets and case study dataset are employed for model evaluation. The result shows that the proposed models can have more promising result than some existing models.

A medical cost estimation with fuzzy neural network of acute hepatitis patients in emergency room.

Taiwan is an area where chronic hepatitis is endemic. Liver cancer is so common that it has been ranked first among cancer mortality rates since the early 1980s in Taiwan. Besides, liver cirrhosis and chronic liver diseases are the sixth or seventh in the causes of death. Therefore, as shown by the active research on hepatitis, it is not only a health threat, but also a huge medical cost for the government. The estimated total number of hepatitis B carriers in the general population aged more than 20 years old is 3,067,307. Thus, a case record review was conducted from all patients with diagnosis of acute hepatitis admitted to the Emergency Department (ED) of a well-known teaching-oriented hospital in Taipei. The cost of medical resource utilization is defined as the total medical fee. In this study, a fuzzy neural network is employed to develop the cost forecasting model. A total of 110 patients met the inclusion criteria. The computational results indicate that the FNN model can provide more accurate forecasts than the support vector regression (SVR) or artificial neural network (ANN). In addition, unlike SVR and ANN, FNN can also provide fuzzy IF-THEN rules for interpretation.

Fluke (Spirorchiidae) infections in sea turtles stranded on Taiwan: prevalence and pathology.

The prevalence of spirorchiid fluke infections of marine turtles is high and may cause the death of the hosts throughout their ranges. Virtually nothing has been reported regarding the infective status of sea turtles stranded on Taiwan. Between 2007 and 2010, 30 green turtles (Chelonia mydas) and 2 loggerhead turtles ( Caretta caretta ), stranded and dead, were examined for spirorchiid flukes and their eggs. Twenty-four of the green turtles were juveniles, and the stranded loggerhead turtles were subadults. Adult spirorchiid flukes were found in 13 green turtles but not in the loggerheads. Four species of flukes were identified, namely, Leardius learedi , Hapalotrema postorchis , H. mehrai , and Carettacola hawaiiensis . The main infection sites were the major arteries and heart. Seventy percent of the green turtles harbored spirorchiid eggs, but no eggs were found in loggerheads. The largest eggs with bipolar spines, type I eggs, were found in every case. Although more than half of the stranded turtles were infected, parasite infections were not the main cause of death in the green turtles. Fishery by-catch is probably responsible for the mortality of these stranded turtles.

An intelligent sales forecasting system through integration of artificial neural networks and fuzzy neural networks with fuzzy weight elimination.

Sales forecasting plays a very prominent role in business strategy. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average (ARMA). However, sales forecasting is very complicated owing to influence by internal and external environments. Recently, artificial neural networks (ANNs) have also been applied in sales forecasting since their promising performances in the areas of control and pattern recognition. However, further improvement is still necessary since unique circumstances, e.g. promotion, cause a sudden change in the sales pattern. Thus, this study utilizes a proposed fuzzy neural network (FNN), which is able to eliminate the unimportant weights, for the sake of learning fuzzy IF-THEN rules obtained from the marketing experts with respect to promotion. The result from FNN is further integrated with the time series data through an ANN. Both the simulated and real-world problem results show that FNN with weight elimination can have lower training error compared with the regular FNN. Besides, real-world problem results also indicate that the proposed estimation system outperforms the conventional statistical method and single ANN in accuracy.

Multi-sensor integration for on-line tool wear estimation through radial basis function networks and fuzzy neural network.

On-line tool wear estimation plays a very critical role in industry automation for higher productivity and product quality. In addition, appropriate and timely decision for tool change is significantly required in the machining systems. Thus, this paper is dedicated to develop an estimation system through integration of two promising technologies, artificial neural networks (ANN) and fuzzy logic. An on-line estimation system consisting of five components: (1) data collection; (2) feature extraction; (3) pattern recognition; (4) multi-sensor integration; and (5) tool/work distance compensation for tool flank wear, is proposed herein. For each sensor, a radial basis function (RBF) network is employed to recognize the extracted features. Thereafter, the decisions from multiple sensors are integrated through a proposed fuzzy neural network (FNN) model. Such a model is self-organizing and self-adjusting, and is able to learn from the experience. Physical experiments for the metal cutting process are implemented to evaluate the proposed system. The results show that the proposed system can significantly increase the accuracy of the product profile.

Epidural morphine for post-hemorrhoidectomy analgesia.

Sixty-six patients were operated upon for third- or fourth-degree hemorrhoids during the period of January 1982 through November 1983. A single injection of epidural morphine was given for relief of postoperative pain. Eighty-three point three per cent of the patients needed no other analgesics and 16.7 per cent required only mild analgesics. There were no complications observed after surgery.