scholarly journals An Automatic Bleeding-Rank System for Transurethral Resection of the Prostate Surgery Videos Using Machine Learning

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1767
Author(s):  
Jian-Wen Chen ◽  
Wan-Ju Lin ◽  
Chun-Yuan Lin ◽  
Che-Lun Hung ◽  
Chen-Pang Hou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Transurethral Resection ◽  
Main Idea ◽  
Invasive Procedure ◽  
Blood Levels ◽  
Ranking Algorithms ◽  
Urinary Tract Symptoms ◽  
Rank System ◽  
Surgical Field ◽  
Human Eyes

Benign prostatic hyperplasia (BPH) is the main cause of lower urinary tract symptoms (LUTS) in aging males. Transurethral resection of the prostate (TURP) surgery is performed by a cystoscope passing through the urethra and scraping off the prostrate piece by piece through a cutting loop. Although TURP is a minimally invasive procedure, bleeding is still the most common complication. Therefore, the evaluation, monitoring, and prevention of interop bleeding during TURP are very important issues. The main idea of this study is to rank bleeding levels during TURP surgery from videos. Generally, to judge bleeding level by human eyes from surgery videos is a difficult task, which requires sufficient experienced urologists. In this study, machine learning-based ranking algorithms are proposed to efficiently evaluate the ranking of blood levels. Based on the visual clarity of the surgical field, the four ranking of blood levels, including score 0: excellent; score 1: acceptable; score 2: slightly bad; and 3: bad, were identified by urologists who have sufficient experience in TURP surgery. The results of extensive experiments show that the revised accuracy can achieve 90, 89, 90, and 91%, respectively. Particularly, the results reveal that the proposed methods were capable of classifying the ranking of bleeding level accurately and efficiently reducing the burden of urologists.

scholarly journals Development of detection method for automatic hemostasis using machine learning with abdominal cavity irrigation

2020 ◽  
Vol 7 (7) ◽  
pp. 2103
Author(s):  
Yoshihisa Matsunaga ◽  
Ryoichi Nakamura
Keyword(s):  
Machine Learning ◽  
Minimally Invasive Surgery ◽  
Minimally Invasive ◽  
Real Time ◽  
Invasive Surgery ◽  
Abdominal Cavity ◽  
Training Dataset ◽  
Support Vector ◽  
Processing Cost ◽  
Surgical Field

Background: Abdominal cavity irrigation is a more minimally invasive surgery than that using a gas. Minimally invasive surgery improves the quality of life of patients; however, it demands higher skills from the doctors. Therefore, the study aimed to reduce the burden by assisting and automating the hemostatic procedure a highly frequent procedure by taking advantage of the clearness of the endoscopic images and continuous bleeding point observations in the liquid. We aimed to construct a method for detecting organs, bleeding sites, and hemostasis regions.Methods: We developed a method to perform real-time detection based on machine learning using laparoscopic videos. Our training dataset was prepared from three experiments in pigs. Linear support vector machine was applied using new color feature descriptors. In the verification of the accuracy of the classifier, we performed five-part cross-validation. Classification processing time was measured to verify the real-time property. Furthermore, we visualized the time series class change of the surgical field during the hemostatic procedure.Results: The accuracy of our classifier was 98.3% and the processing cost to perform real-time was enough. Furthermore, it was conceivable to quantitatively indicate the completion of the hemostatic procedure based on the changes in the bleeding region by ablation and the hemostasis regions by tissue coagulation.Conclusions: The organs, bleeding sites, and hemostasis regions classification was useful for assisting and automating the hemostatic procedure in the liquid. Our method can be adapted to more hemostatic procedures. 

scholarly journals Prostatic Abscess on Xanthogranulomatous Prostatitis: Uncommon Complication of an Uncommon Disease

2018 ◽  
Vol 2018 ◽  
pp. 1-5 ◽  
Author(s):  
Youness Jabbour ◽  
Hamza Lamchahab ◽  
Sumba Harrison ◽  
Hafsa El Ouazzani ◽  
Tarik Karmouni ◽  
...  
Keyword(s):  
Transurethral Resection ◽  
Needle Biopsy ◽  
Inflammatory Process ◽  
Histopathological Examination ◽  
Incidental Finding ◽  
English Literature ◽  
Prostatic Abscess ◽  
Urinary Tract Symptoms ◽  
Uncommon Disease ◽  
Uncommon Complication

Xanthogranulomatous prostatitis is a rare benign inflammatory process of the prostate. Only few cases have been reported in the English literature. Xanthogranulomatous prostatitis is usually an incidental finding after needle biopsy or transurethral resection of the prostate in patients suffering from low urinary tract symptoms. We report the case of a 59-years-old patient diagnosed with prostatic abscess managed by transurethral resection of the prostate. Histopathological examination of resected prostatic tissue revealed abscessed xanthogranulomatous prostatitis with no evidence of malignancy. Xanthogranulomatous prostatitis presenting as a prostatic abscess is a rare finding. To the best of our knowledge our case represents the fourth case of xanthogranulomatous prostatitis presenting as prostatic abscess reported in the English literature so far.

Enabling continuous connectivity services for ambrosus blockchain application by incorporating 5G-multilevel machine learning orchestrations

2022 ◽  
pp. 1-16
Author(s):  
Nagaraj Varatharaj ◽  
Sumithira Thulasimani Ramalingam
Keyword(s):  
Machine Learning ◽  
Wireless Networks ◽  
High Speed ◽  
Dynamic Condition ◽  
Main Idea ◽  
Base Station ◽  
Performance Comparison ◽  
Wide Range ◽  
Unit Layer ◽  
Block Chain

Most revolutionary applications extending far beyond smartphones and high configured mobile device use to the future generation wireless networks’ are high potential capabilities in recent days. One of the advanced wireless networks and mobile technology is 5G, where it provides high speed, better reliability, and amended capacity. 5 G offers complete coverage, which is accommodates any IoT device, connectivity, and intelligent edge algorithms. So that 5 G has a high demand in a wide range of commercial applications. Ambrosus is a commercial company that integrates block-chain security, IoT network, and supply chain management for medical and food enterprises. This paper proposed a novel framework that integrates 5 G technology, Machine Learning (ML) algorithms, and block-chain security. The main idea of this work is to incorporate the 5 G technology into Machine learning architectures for the Ambrosus application. 5 G technology provides continuous connection among the network user/nodes, where choosing the right user, base station, and the controller is obtained by using for ML architecture. The proposed framework comprises 5 G technology incorporate, a novel network orchestration, Radio Access Network, and a centralized distributor, and a radio unit layer. The radio unit layer is used for integrating all the components of the framework. The ML algorithm is evaluated the dynamic condition of the base station, like as IoT nodes, Ambrosus users, channels, and the route to enhance the efficiency of the communication. The performance of the proposed framework is evaluated in terms of prediction by simulating the model in MATLAB software. From the performance comparison, it is noticed that the proposed unified architecture obtained 98.6% of accuracy which is higher than the accuracy of the existing decision tree algorithm 97.1% .

Single cell flow cytometry and machine learning stratifies patients undergoing transurethral resection of the bladder tumor (TURBT)

2019 ◽  
Vol 18 (2) ◽  
pp. e2406-e2407
Author(s):  
A. Koladiya ◽  
K. Otavová ◽  
V. Adamcová ◽  
J. Stejskal ◽  
B. Ogan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
Single Cell ◽  
Transurethral Resection ◽  
Bladder Tumor ◽  
Cell Flow Cytometry

Ranking with Genetics

2020 ◽  
Vol 10 (3) ◽  
pp. 20-34
Author(s):  
Lawrence Master
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Question Answering ◽  
Learning To Rank ◽  
Approaches To Learning ◽  
Genetic Optimization ◽  
Regularization Technique ◽  
The Past ◽  
Ranking Algorithms ◽  
Ranking Model

There are many applications for ranking, including page searching, question answering, recommender systems, sentiment analysis, and collaborative filtering, to name a few. In the past several years, machine learning and information retrieval techniques have been used to develop ranking algorithms and several list wise approaches to learning to rank have been developed. We propose a new method, which we call GeneticListMLE++ and GeneticListNet++, which build on the original ListMLE and ListNet algorithms. Our method substantially improves on the original ListMLE and ListNet ranking approaches by incorporating genetic optimization of hyperparameters, a nonlinear neural network ranking model, and a regularization technique.

Hiding Images Using Dynamic Bit-Replacement and Human Visual System

2002 ◽  
pp. 190-205
Author(s):  
Chin-Chen Chang ◽  
Kuo-Feng Hwang
Keyword(s):  
Visual System ◽  
Human Visual System ◽  
Main Idea ◽  
Cover Image ◽  
Two Dimensional ◽  
Hiding Capacity ◽  
Partial Encryption ◽  
Secret Image ◽  
Threshold Mechanism ◽  
Human Eyes

A simple image hiding scheme in spatial domain is proposed in this chapter. The main idea is to utilize a threshold mechanism to embed as much information of the secret image into the cover image as possible. The changing of the cover image is hard to be discovered by the human eyes because the threshold mechanism is setup especially to fit the human visual system. The experimental results show that the human visual system has improved the quality in terms of perceptibility. On the hiding capacity issue, the proposed method has capability to embed two times the size of the secret image of previous work. A partial encryption strategy is used for the security of the secret image. In addition, a two-dimensional permutation function, torus automorphism, is also introduced in this chapter.

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