scholarly journals The Application of Artificial Intelligence in Prostate Cancer Management—What Improvements Can Be Expected? A Systematic Review

2020 ◽  
Vol 10 (18) ◽  
pp. 6428
Author(s):  
Ronan Thenault ◽  
Kevin Kaulanjan ◽  
Thomas Darde ◽  
Nathalie Rioux-Leclercq ◽  
Karim Bensalah ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Artificial Intelligence ◽  
Systematic Review ◽  
Neural Networks ◽  
Language Processing ◽  
Daily Practice ◽  
Skills Training ◽  
Treatment Modalities ◽  
Cancer Management ◽  
Potential Applications

Artificial Intelligence (AI) is progressively remodeling our daily life. A large amount of information from “big data” now enables machines to perform predictions and improve our healthcare system. AI has the potential to reshape prostate cancer (PCa) management thanks to growing applications in the field. The purpose of this review is to provide a global overview of AI in PCa for urologists, pathologists, radiotherapists, and oncologists to consider future changes in their daily practice. A systematic review was performed, based on PubMed MEDLINE, Google Scholar, and DBLP databases for original studies published in English from January 2009 to January 2019 relevant to PCa, AI, Machine Learning, Artificial Neural Networks, Convolutional Neural Networks, and Natural-Language Processing. Only articles with full text accessible were considered. A total of 1008 articles were reviewed, and 48 articles were included. AI has potential applications in all fields of PCa management: analysis of genetic predispositions, diagnosis in imaging, and pathology to detect PCa or to differentiate between significant and non-significant PCa. AI also applies to PCa treatment, whether surgical intervention or radiotherapy, skills training, or assessment, to improve treatment modalities and outcome prediction. AI in PCa management has the potential to provide a useful role by predicting PCa more accurately, using a multiomic approach and risk-stratifying patients to provide personalized medicine.

scholarly journals Potential role of artificial intelligence in craniofacial surgery

2021 ◽  
Vol 22 (5) ◽  
pp. 223-231
Author(s):  
Jeong Yeop Ryu ◽  
Ho Yun Chung ◽  
Kang Young Choi
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Language Processing ◽  
Cosmetic Surgery ◽  
Craniofacial Surgery ◽  
Generative Adversarial Networks ◽  
Biosignal Analysis ◽  
Adversarial Networks ◽  
Potential Applications ◽  
Craniofacial Trauma

The field of artificial intelligence (AI) is rapidly advancing, and AI models are increasingly applied in the medical field, especially in medical imaging, pathology, natural language processing, and biosignal analysis. On the basis of these advances, telemedicine, which allows people to receive medical services outside of hospitals or clinics, is also developing in many countries. The mechanisms of deep learning used in medical AI include convolutional neural networks, residual neural networks, and generative adversarial networks. Herein, we investigate the possibility of using these AI methods in the field of craniofacial surgery, with potential applications including craniofacial trauma, congenital anomalies, and cosmetic surgery.

scholarly journals The Application and Development of Deep Learning in Radiotherapy: A Systematic Review

2021 ◽  
Vol 20 ◽  
pp. 153303382110163
Author(s):  
Danju Huang ◽  
Han Bai ◽  
Li Wang ◽  
Yu Hou ◽  
Lan Li ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Neural Networks ◽  
Decision Making ◽  
Deep Learning ◽  
Radiation Oncology ◽  
Clinical Work ◽  
Radiation Oncologists ◽  
Basic Concepts ◽  
Further Development

With the massive use of computers, the growth and explosion of data has greatly promoted the development of artificial intelligence (AI). The rise of deep learning (DL) algorithms, such as convolutional neural networks (CNN), has provided radiation oncologists with many promising tools that can simplify the complex radiotherapy process in the clinical work of radiation oncology, improve the accuracy and objectivity of diagnosis, and reduce the workload, thus enabling clinicians to spend more time on advanced decision-making tasks. As the development of DL gets closer to clinical practice, radiation oncologists will need to be more familiar with its principles to properly evaluate and use this powerful tool. In this paper, we explain the development and basic concepts of AI and discuss its application in radiation oncology based on different task categories of DL algorithms. This work clarifies the possibility of further development of DL in radiation oncology.

The Current Role of Immunotherapy in mCRPC: A Systematic Review

2021 ◽  
Vol 8 (7) ◽  
Author(s):  
Dalibey H ◽  
◽  
Hansen TF ◽  
Zedan AH ◽  
◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Systematic Review ◽  
Overall Survival ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Response Rate ◽  
Checkpoint Inhibitors ◽  
Specific Antigen ◽  
Treatment Modalities ◽  
Significant Difference

Background: The development of immunotherapy has shown promising results in several malignant diseases, including prostate cancer, calling for a systematic review of the current literature. This review aims to evaluate the present data and prospects of immune checkpoint inhibitors in metastatic Castration Resistant Prostate Cancer (mCRPC). Methods: Articles were identified via a systematic search of the electronic database Pubmed, in accordance with the PICO process and following the PRISMA guidelines. Articles in English studying immune checkpoint inhibitors in patients with mCRPC published between March 2010 and March 2020 were eligible for inclusion. Endpoints of interest were Overall Survival (OS), Progression-Free Survival (PFS), clinical Overall Response Rate (ORR), and Prostate-Specific Antigen (PSA) response rate. Results: Ten articles were identified as eligible for inclusion. The studies primarily explored the use of Ipilimumab, a CTLA-4 inhibitor, and Pembrolizumab, a PD-1 inhibitor. These drugs were both used either as monotherapy or in combination with other treatment modalities. The largest trial included in the review demonstrated no significant difference in overall survival between the intervention and placebo. However, two studies presented promising data combing immunotherapy and immune vaccines. Grade 3 and 4 adverse events ranging from 10.1% to 82.3%, whit diarrhea, rash, and fatigue were the most frequently reported. Forty relevant ongoing trials were identified exploring immunotherapy with or without a parallel treatment modality. Conclusion: Overall, the current data shows that the effect of immune checkpoint inhibitors as monotherapy may have limited impact on mCRPC, and the results from ongoing combinational trials are eagerly awaited.

scholarly journals Real World of Artificial Intelligence - A Review

2020 ◽  
Vol 11 (2) ◽  
pp. 41-47
Author(s):  
Amandeep Kaur ◽  
Madhu Dhiman ◽  
Mansi Tonk ◽  
Ramneet Kaur
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Face Recognition ◽  
Language Processing ◽  
Real World ◽  
Review Paper ◽  
General Concept ◽  
Research Trends ◽  
Human Intelligence

Artificial Intelligence is the combination of machine and human intelligence, which are in research trends from the last many years. Different Artificial Intelligence programs have become capable of challenging humans by providing Expert Systems, Neural Networks, Robotics, Natural Language Processing, Face Recognition and Speech Recognition. Artificial Intelligence brings a bright future for different technical inventions in various fields. This review paper shows the general concept of Artificial Intelligence and presents an impact of Artificial Intelligence in the present and future world.

scholarly journals A Systematic Review of Artificial Intelligence in Prostate Cancer

2021 ◽  
Vol Volume 13 ◽  
pp. 31-39 ◽  
Author(s):  
Derek J Van Booven ◽  
Manish Kuchakulla ◽  
Raghav Pai ◽  
Fabio S Frech ◽  
Reshna Ramasahayam ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Artificial Intelligence ◽  
Systematic Review

scholarly journals Human Gaze Assisted Artificial Intelligence: A Review

2020 ◽  
Author(s):  
Ruohan Zhang ◽  
Akanksha Saran ◽  
Bo Liu ◽  
Yifeng Zhu ◽  
Sihang Guo ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Cognitive State ◽  
Data Sets ◽  
Common Goal ◽  
Research Areas ◽  
Future Challenges ◽  
Potential Applications ◽  
High Level ◽  
And Robotics

Human gaze reveals a wealth of information about internal cognitive state. Thus, gaze-related research has significantly increased in computer vision, natural language processing, decision learning, and robotics in recent years. We provide a high-level overview of the research efforts in these fields, including collecting human gaze data sets, modeling gaze behaviors, and utilizing gaze information in various applications, with the goal of enhancing communication between these research areas. We discuss future challenges and potential applications that work towards a common goal of human-centered artificial intelligence.

A Matter of Perspective

2020 ◽  
pp. 182-202
Author(s):  
Katie Miller
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Human Rights ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Equal Opportunity ◽  
Facial Recognition ◽  
Phone Service

The challenge presented is an age when some decisions are made by humans, some are made by AI, and some are made by a combination of AI and humans. For the person refused housing, a phone service, or employment, the experience is the same, but the ability to understand what has happened and obtain a remedy may be very different if the discrimination is attributable to or contributed by an AI system. If we are to preserve the policy intentions of our discrimination, equal opportunity, and human rights laws, we need to understand how discrimination arises in AI systems; how design in AI systems can mitigate such discrimination; and whether our existing laws are adequate to address discrimination in AI. This chapter endeavours to provide this understanding. In doing so, it focuses on narrow but advanced forms of artificial intelligence, such as natural language processing, facial recognition, and cognitive neural networks.

Factors affecting health-related quality of life among prostate cancer patients: A systematic review

2020 ◽  
Vol 26 (8) ◽  
pp. 1997-2010
Author(s):  
Sharon Odeo ◽  
Amsalu Degu
Keyword(s):  
Quality Of Life ◽  
Prostate Cancer ◽  
Systematic Review ◽  
Sexual Function ◽  
Cancer Patients ◽  
Clinical Stage ◽  
Treatment Modalities ◽  
Functional Score ◽  
Incidence And Mortality

Introduction Prostate cancer is recognized as the leading cause of malignancy-related incidence and mortality in the male population. The treatment regimens have long-term effects detrimental to the patient's quality of life. Hence, this review was aimed to determine the overall HRQOL and its associated among prostate cancer patients. Methods The review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. The databases searched were PubMed, Embase, Google Scholar and Cumulative Index to the Nursing and Allied Literature (CINAHL), which provided articles that were critically examined, yielding 52 studies that met the inclusion criteria for the systematic review. Results Out of 52 studies, 30 studies reported poor overall HRQOL in various domains after prostate cancer treatment. Contrastingly, 15 studies reported good overall quality of life after treatment. Among the various domains, sexual function was the most grossly affected functional score by the treatment modalities of prostate cancer. Nonetheless, seven studies showed that the absence of a significant change in the overall quality of life after treatment. According to the studies, older age, comorbidities, higher clinical stage, higher Gleason score, greater cancer severity, African American race, impaired mental health, neoadjuvant hormonal therapy and lower level of education were the major poor predictors of HRQOL among prostate cancer patients. Conclusion The overall HRQOL in prostate cancer patients was generally poor in various functional domains after treatment. Among the various domains, sexual function was the most grossly affected functional score by the treatment modalities of prostate cancer.

scholarly journals Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review

2020 ◽  
Vol 78 (4) ◽  
pp. 1547-1574
Author(s):  
Sofia de la Fuente Garcia ◽  
Craig W. Ritchie ◽  
Saturnino Luz
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Language Processing ◽  
Future Research ◽  
Original Research ◽  
Speech And Language ◽  
Eligibility Criteria ◽  
Speech And Language Processing

Background: Language is a valuable source of clinical information in Alzheimer’s disease, as it declines concurrently with neurodegeneration. Consequently, speech and language data have been extensively studied in connection with its diagnosis. Objective: Firstly, to summarize the existing findings on the use of artificial intelligence, speech, and language processing to predict cognitive decline in the context of Alzheimer’s disease. Secondly, to detail current research procedures, highlight their limitations, and suggest strategies to address them. Methods: Systematic review of original research between 2000 and 2019, registered in PROSPERO (reference CRD42018116606). An interdisciplinary search covered six databases on engineering (ACM and IEEE), psychology (PsycINFO), medicine (PubMed and Embase), and Web of Science. Bibliographies of relevant papers were screened until December 2019. Results: From 3,654 search results, 51 articles were selected against the eligibility criteria. Four tables summarize their findings: study details (aim, population, interventions, comparisons, methods, and outcomes), data details (size, type, modalities, annotation, balance, availability, and language of study), methodology (pre-processing, feature generation, machine learning, evaluation, and results), and clinical applicability (research implications, clinical potential, risk of bias, and strengths/limitations). Conclusion: Promising results are reported across nearly all 51 studies, but very few have been implemented in clinical research or practice. The main limitations of the field are poor standardization, limited comparability of results, and a degree of disconnect between study aims and clinical applications. Active attempts to close these gaps will support translation of future research into clinical practice.

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