Use of Artificial Intelligence and Machine Learning Algorithms with Gene Expression Profiling to Predict Recurrent Nonmuscle Invasive Urothelial Carcinoma of the Bladder

Bartsch, Georg; Mitra, Anirban P.; Mitra, Sheetal A.; Almal, Arpit A.; Steven, Kenneth; Skinner, Donald G.; Fry, David W.; Lenehan, Peter F.; Worzel, William P.; Côté, Richard J.

doi:10.1016/j.juro.2015.09.090

Cited by 51 publications

(28 citation statements)

References 27 publications

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“…Urothelial cancer is a serious urological malignancy with high rate of recurrence. AI algorithms have been developed to predict cancer recurrence and patient survival (Table ). Using clinicopathological data, Lam et al.…”

Section: Application Of Ai In Urologymentioning

confidence: 99%

“…Urothelial cancer is a serious urological malignancy with high rate of recurrence. AI algorithms have been developed to predict cancer recurrence and patient survival [30][31][32][33] ( Table 2). Using clinicopathological data, Lam et al [30] and Wang et al [31] developed and validated an array of AI algorithms to predict 5-year survival rate after radical cystectomy.…”

Section: Urothelial Cancermentioning

confidence: 99%

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Current status of artificial intelligence applications in urology and their potential to influence clinical practice

et al. 2019

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Objective To investigate the applications of artificial intelligence (AI) in diagnosis, treatment and outcome predictionin urologic diseases and evaluate its advantages over traditional models and methods. Materials and methods A literature search was performed after PROSPERO registration (CRD42018103701) and in compliance with Preferred Reported Items for Systematic Reviews and Meta‐Analyses (PRISMA) methods. Articles between 1994 and 2018 using the search terms “urology”, “artificial intelligence”, “machine learning” were included and categorized by the application of AI in urology. Review articles, editorial comments, articles with no full‐text access, and nonurologic studies were excluded. Results Initial search yielded 231 articles, but after excluding duplicates and following full‐text review and examination of article references, only 111 articles were included in the final analysis. AI applications in urology include: utilizing radiomic imaging or ultrasonic echo data to improve or automate cancer detection or outcome prediction, utilizing digitized tissue specimen images to automate detection of cancer on pathology slides, and combining patient clinical data, biomarkers, or gene expression to assist disease diagnosis or outcome prediction. Some studies employed AI to plan brachytherapy and radiation treatments while others used video based or robotic automated performance metrics to objectively evaluate surgical skill. Compared to conventional statistical analysis, 71.8% of studies concluded that AI is superior in diagnosis and outcome prediction. Conclusion AI has been widely adopted in urology. Compared to conventional statistics AI approaches are more accurate in prediction and more explorative for analyzing large data cohorts. With an increasing library of patient data accessible to clinicians, AI may help facilitate evidence‐based and individualized patient care.

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Section: Application Of Ai In Urologymentioning

confidence: 99%

Section: Urothelial Cancermentioning

confidence: 99%

Current status of artificial intelligence applications in urology and their potential to influence clinical practice

et al. 2019

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“…Virtual reality is a computer-generated simulation of a 3D environment that can be interacted with in a seemingly real manner. Artificial intelligence will be used in augmented reality for future remote eHealth [21,22]. It is currently used in customer service and support and as a smart home manager [23,[49][50][51][52].…”

Section: Virtual/augmented Realitymentioning

confidence: 99%

Brain Intelligence: Go beyond Artificial Intelligence

et al. 2017

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Artificial intelligence (AI) is an important technology that supports daily social life and economic activities. It contributes greatly to the sustainable growth of Japan's economy and solves various social problems. In recent years, AI has attracted attention as a key for growth in developed countries such as Europe and the United States and developing countries such as China and India. The attention has been focused mainly on developing new artificial intelligence information communication technology (ICT) and robot technology (RT). Although recently developed AI technology certainly excels in extracting certain patterns, there are many limitations. Most ICT models are overly dependent on big data, lack a self-idea function, and are complicated. In this paper, rather than merely developing nextgeneration artificial intelligence technology, we aim to develop a new concept of general-purpose intelligence cognition technology called "Beyond AI". Specifically, we plan to develop an intelligent learning model called "Brain Intelligence (BI)" that generates new ideas about events without having experienced them by using artificial life with an imagine function. We will also conduct demonstrations of the developed BI intelligence learning model on automatic driving, precision medical care, and industrial robots.

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“…Optimization problems exist in scientific research and engineering areas [1][2][3], such as statistical physics [4,5], computer science [6], artificial intelligence [7], and pattern recognition [8].…”

Section: Introductionmentioning

confidence: 99%

The Improved Antlion Optimizer and Artificial Neural Network for Chinese Influenza Prediction

Bai

et al. 2019

Complexity

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The antlion optimizer (ALO) is a new swarm-based metaheuristic algorithm for optimization, which mimics the hunting mechanism of antlions in nature. Aiming at the shortcoming that ALO has unbalanced exploration and development capability for some complex optimization problems, inspired by the particle swarm optimization (PSO), the updated position of antlions in elitism operator of ALO is improved, and thus the improved ALO (IALO) is obtained. The proposed IALO is compared against sine cosine algorithm (SCA), PSO, Moth-flame optimization algorithm (MFO), multi-verse optimizer (MVO), and ALO by performing on 23 classic benchmark functions. The experimental results show that the proposed IALO outperforms SCA, PSO, MFO, MVO, and ALO according to the average values and the convergence speeds. And the proposed IALO is tested to optimize the parameters of BP neural network for predicting the Chinese influenza and the predicted model is built, written as IALO-BPNN, which is against the models: BPNN, SCA-BPNN, PSO-BPNN, MFO-BPNN, MVO-BPNN, and ALO-BPNN. It is shown that the predicted model IALO-BPNN has smaller errors than other six predicted models, which illustrates that the IALO has potentiality to optimize the weights and basis of BP neural network for predicting the Chinese influenza effectively. Therefore, the proposed IALO is an effective and efficient algorithm suitable for optimization problems.

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Use of Artificial Intelligence and Machine Learning Algorithms with Gene Expression Profiling to Predict Recurrent Nonmuscle Invasive Urothelial Carcinoma of the Bladder

Abstract: Using primary nonmuscle invasive urothelial carcinoma from initial occurrences genetic programming identified transcripts in reproducible fashion, which were predictive of recurrence. These findings could potentially impact nonmuscle invasive urothelial carcinoma management.

Cited by 51 publications

References 27 publications

Current status of artificial intelligence applications in urology and their potential to influence clinical practice

Current status of artificial intelligence applications in urology and their potential to influence clinical practice

Brain Intelligence: Go beyond Artificial Intelligence

The Improved Antlion Optimizer and Artificial Neural Network for Chinese Influenza Prediction

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