Machine learning algorithms can more efficiently predict biochemical recurrence after robot‐assisted radical prostatectomy

The Prostate ◽  
2021 ◽  
Author(s):  
Mithat Ekşi ◽  
İsmail Evren ◽  
Fatih Akkaş ◽  
Yusuf Arıkan ◽  
Osman Özdemir ◽  
...  
Keyword(s):  
Machine Learning ◽  
Radical Prostatectomy ◽  
Biochemical Recurrence ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Robot Assisted

scholarly journals A Machine Learning Approach to Predict an Early Biochemical Recurrence after a Radical Prostatectomy

2020 ◽  
Vol 10 (11) ◽  
pp. 3854
Author(s):  
Seongkeun Park ◽  
Jieun Byun ◽  
Ji young Woo
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Radical Prostatectomy ◽  
Prediction Model ◽  
Cancer Patients ◽  
Biochemical Recurrence ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Operating Characteristics ◽  
Prediction Ability

Background: Approximately 20–50% of prostate cancer patients experience biochemical recurrence (BCR) after radical prostatectomy (RP). Among them, cancer recurrence occurs in about 20–30%. Thus, we aim to reveal the utility of machine learning algorithms for the prediction of early BCR after RP. Methods: A total of 104 prostate cancer patients who underwent magnetic resonance imaging and RP were evaluated. Four well-known machine learning algorithms (i.e., k-nearest neighbors (KNN), multilayer perceptron (MLP), decision tree (DT), and auto-encoder) were applied to build a prediction model for early BCR using preoperative clinical and imaging and postoperative pathologic data. The sensitivity, specificity, and accuracy for detection of early BCR of each algorithm were evaluated. Area under the receiver operating characteristics (AUROC) analyses were conducted. Results: A prediction model using an auto-encoder showed the highest prediction ability of early BCR after RP using all data as input (AUC = 0.638) and only preoperative clinical and imaging data (AUC = 0.656), followed by MLP (AUC = 0.607 and 0.598), KNN (AUC = 0.596 and 0.571), and DT (AUC = 0.534 and 0.495). Conclusion: The auto-encoder-based prediction system has the potential for accurate detection of early BCR and could be useful for long-term follow-up planning in prostate cancer patients after RP.

scholarly journals Surgical Hand Gesture Recognition Utilizing Electroencephalogram as Input to the Machine Learning and Network Neuroscience Algorithms

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1733
Author(s):  
Somayeh B. Shafiei ◽  
Mohammad Durrani ◽  
Zhe Jing ◽  
Michael Mostowy ◽  
Philippa Doherty ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Skill Assessment ◽  
Hand Gesture ◽  
Dominant Hand ◽  
Robot Assisted ◽  
Functional Brain Network ◽  
Eeg Data ◽  
Network Neuroscience

Surgical gestures detection can provide targeted, automated surgical skill assessment and feedback during surgical training for robot-assisted surgery (RAS). Several sources including surgical videos, robot tool kinematics, and an electromyogram (EMG) have been proposed to reach this goal. We aimed to extract features from electroencephalogram (EEG) data and use them in machine learning algorithms to classify robot-assisted surgical gestures. EEG was collected from five RAS surgeons with varying experience while performing 34 robot-assisted radical prostatectomies over the course of three years. Eight dominant hand and six non-dominant hand gesture types were extracted and synchronized with associated EEG data. Network neuroscience algorithms were utilized to extract functional brain network and power spectral density features. Sixty extracted features were used as input to machine learning algorithms to classify gesture types. The analysis of variance (ANOVA) F-value statistical method was used for feature selection and 10-fold cross-validation was used to validate the proposed method. The proposed feature set used in the extra trees (ET) algorithm classified eight gesture types performed by the dominant hand of five RAS surgeons with an accuracy of 90%, precision: 90%, sensitivity: 88%, and also classified six gesture types performed by the non-dominant hand with an accuracy of 93%, precision: 94%, sensitivity: 94%.

Classification of the Emotional State of a Subject Using Machine Learning Algorithms for RehabRoby

2015 ◽  
pp. 53-80
Author(s):  
Duygun Erol Barkana ◽  
Engin Masazade
Keyword(s):  
Machine Learning ◽  
Emotional State ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Difficulty Level ◽  
Robot Assisted ◽  
Actual Performance ◽  
Sensory Data ◽  
Machine Learning Methods

Robot-assisted rehabilitation systems have shown to be helpful in neuromotor rehabilitation because it is possible to deliver interactive and repeatable sensorimotor exercise and monitor the actual performance continuously. Note that it is also essential to distinguish if subject finds the rehabilitation task difficult or easy, since the difficulty level of a task can yield different emotional state, such as excited, bored, over-stressed, etc., at each subject. It is important to adjust the difficulty level of the task to encourage the non-motivated subjects during the therapy. The physiological measurements, which can be obtained from the biofeedback sensors, can be used to estimate the subject's emotional state during the execution of the rehabilitation task. Machine learning methods can be used to classify the emotional state using the features of the biofeedback sensory data. This is explored in this chapter.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

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