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Background/Objectives: During gastroscopy, accurately identifying the anatomical locations of the gastrointestinal tract is crucial for developing diagnostic aids, such as lesion localization and blind spot alerts. Methods: This study utilized a dataset of 31,403 still images from 1000 patients with normal findings to annotate the anatomical locations within the images and develop a classification model. The model was then applied to videos of 20 esophagogastroduodenoscopy procedures, where it was validated for real-time location prediction. To address instability of predictions caused by independent frame-by-frame assessment, we implemented a hard-voting-based post-processing algorithm that aggregates results from seven consecutive frames, improving the overall accuracy. Results: Among the tested models, InceptionV3 demonstrated superior performance for still images, achieving an F1 score of 79.79%, precision of 80.57%, and recall of 80.08%. For video data, the InceptionResNetV2 model performed best, achieving an F1 score of 61.37%, precision of 73.08%, and recall of 57.21%. These results indicate that the deep learning models not only achieved high accuracy in position recognition for still images but also performed well on video data. Additionally, the post-processing algorithm effectively stabilized the predictions, highlighting its potential for real-time endoscopic applications. Conclusions: This study demonstrates the feasibility of predicting the gastrointestinal tract locations during gastroscopy and suggests a promising path for the development of advanced diagnostic aids to assist clinicians. Furthermore, the location information generated by this model can be leveraged in future technologies, such as automated report generation and supporting follow-up examinations for patients. [ABSTRACT FROM AUTHOR]

The primary goal during cancer removal surgery is to completely excise the malignant tumor. Because the color of the tumor and surrounding tissues is very similar, it is difficult to observe with the naked eye, posing a risk of damaging surrounding blood vessels during the tumor removal process. Therefore, fluorescence emission is induced using a fluorescent contrast agent, and color classification is monitored through camera imaging. LEDs must be irradiated to generate the fluorescent emission electromotive force. However, the power and beam width of the LED are insufficient to generate this force effectively, so the beam width and intensity must be increased to irradiate the entire lesion. Additionally, there should be no shaded areas in the beam irradiation range. This paper proposes a method to enhance the beam width and intensity while eliminating shadow areas. A total reflection beam mirror was used to increase beam width and intensity. However, when the beam width increased, a shadow area appeared at the edge, limiting irradiation of the entire lesion. To compensate for this shadow area, a concave lens was combined with the beam mirror, resulting in an increase in beam width and intensity by more than 1.42 times and 18.6 times, respectively. Consequently, the beam width reached 111.8°, and the beam power was 13.6 mW. The proposed method is expected to be useful for observing tumors through the induction of fluorescence emission during cancer removal surgery or for pathological examination in the pathology department. [ABSTRACT FROM AUTHOR]

Cancer surgery is aimed at complete tumor resection and accurate lymph node detection. However, numerous blood vessels are distributed within the tumor, and the colors of the tumor, blood vessels, and lymph nodes are similar, making observations with the naked eye difficult. Therefore, tumors, blood vessels, and lymph nodes can be monitored via color classification using an operating microscope to induce fluorescence emission. However, as the beam width of the LED required to induce fluorescence emission is narrow and the power loss of the beam is significant at a certain working distance, there are limitations to inducing fluorescence emission, and light reflection occurs in the observation image, obstructing the view of the observation area. Therefore, the removal of reflected light is essential to avoid missing the diagnosis of the lesion under observation. This paper proposes the use of a beam mirror and polarizing filter to increase the beam width and beam intensity. The refraction and reflection effects of the beam were utilized using the beam mirror, and the rotation angle of the polarizing filter was adjusted to remove light reflection. Consequently, the minimum beam power using the beam mirror was 10.9 mW, the beam width was doubled to 40.2°, and more than 98% of light reflection was removed at 90° and 270°. With light reflection effectively eliminated, clear observation of lesions is possible. This method is expected to be used effectively in surgical, procedural, and diagnostic departments. [ABSTRACT FROM AUTHOR]

Motivation: A fluorescence emission-guided microscope used to monitor the outcome of cancer removal surgery is highly effective when employing a manipulator to motorize and switch the observation direction. It is necessary to minimize the alignment of looper tension between the stands for pull/push to change the direction of the manipulator and reduce the error rate caused by tension differences. This paper presents a method to minimize the error rate of looper tension between the stands. Methods: \The looper is inserted between the stands of the manipulator to minimize the difference in tension and make the stress on the pull and push of the looper constant. The constant stress allows the manipulator to move stably in left/right, up/down, and left/right movements, which will be effective for full-camera observation and close-up shots of the end effector. Results: Reducing the tolerance for differences in the manipulator's looper tension (angle and tension) is crucial. When the input value of the looper tension angle is 50°, the output should closely match 50°. Consequently, the measured response has a tolerance of ±49.98%, resulting in an error rate of.02% (1/50th level). Conclusion: A method is proposed to minimize the error rate of the manipulator's looper tension in a robot-based fluorescence emission-guided microscope used to observe the status of cancer surgery. As a result, a stable manipulator with a minimal error rate can achieve a 3.986x magnification for close-up observation by switching between high and low orientations. [ABSTRACT FROM AUTHOR]

Depending on the degree of fracture, pelvic fracture can be accompanied by vascular damage, and in severe cases, it may progress to hemorrhagic shock. Pelvic radiography can quickly diagnose pelvic fractures, and the Association for Osteosynthesis Foundation and Orthopedic Trauma Association (AO/OTA) classification system is useful for evaluating pelvic fracture instability. This study aimed to develop a radiomics-based machine-learning algorithm to quickly diagnose fractures on pelvic X-ray and classify their instability. data used were pelvic anteroposterior radiographs of 990 adults over 18 years of age diagnosed with pelvic fractures, and 200 normal subjects. A total of 93 features were extracted based on radiomics:18 first-order, 24 GLCM, 16 GLRLM, 16 GLSZM, 5 NGTDM, and 14 GLDM features. To improve the performance of machine learning, the feature selection methods RFE, SFS, LASSO, and Ridge were used, and the machine learning models used LR, SVM, RF, XGB, MLP, KNN, and LGBM. Performance measurement was evaluated by area under the curve (AUC) by analyzing the receiver operating characteristic curve. The machine learning model was trained based on the selected features using four feature-selection methods. When the RFE feature selection method was used, the average AUC was higher than that of the other methods. Among them, the combination with the machine learning model SVM showed the best performance, with an average AUC of 0.75±0.06. By obtaining a feature-importance graph for the combination of RFE and SVM, it is possible to identify features with high importance. The AO/OTA classification of normal pelvic rings and pelvic fractures on pelvic AP radiographs using a radiomics-based machine learning model showed the highest AUC when using the SVM classification combination. Further research on the radiomic features of each part of the pelvic bone constituting the pelvic ring is needed. [ABSTRACT FROM AUTHOR]