scholarly journals Human, All Too Human? An All-Around Appraisal of the “Artificial Intelligence Revolution” in Medical Imaging

2021 ◽  
Vol 12 ◽  
Author(s):  
Francesca Coppola ◽  
Lorenzo Faggioni ◽  
Michela Gabelloni ◽  
Fabrizio De Vietro ◽  
Vincenzo Mendola ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Medical Imaging ◽  
Treatment Strategies ◽  
Specific Treatment ◽  
Computer Application ◽  
Special Focus ◽  
Daily Lives ◽  
Fast Development ◽  
Considerable Benefit ◽  
Dramatic Growth

Artificial intelligence (AI) has seen dramatic growth over the past decade, evolving from a niche super specialty computer application into a powerful tool which has revolutionized many areas of our professional and daily lives, and the potential of which seems to be still largely untapped. The field of medicine and medical imaging, as one of its various specialties, has gained considerable benefit from AI, including improved diagnostic accuracy and the possibility of predicting individual patient outcomes and options of more personalized treatment. It should be noted that this process can actively support the ongoing development of advanced, highly specific treatment strategies (e.g., target therapies for cancer patients) while enabling faster workflow and more efficient use of healthcare resources. The potential advantages of AI over conventional methods have made it attractive for physicians and other healthcare stakeholders, raising much interest in both the research and the industry communities. However, the fast development of AI has unveiled its potential for disrupting the work of healthcare professionals, spawning concerns among radiologists that, in the future, AI may outperform them, thus damaging their reputations or putting their jobs at risk. Furthermore, this development has raised relevant psychological, ethical, and medico-legal issues which need to be addressed for AI to be considered fully capable of patient management. The aim of this review is to provide a brief, hopefully exhaustive, overview of the state of the art of AI systems regarding medical imaging, with a special focus on how AI and the entire healthcare environment should be prepared to accomplish the goal of a more advanced human-centered world.

scholarly journals Machine learning in dental, oral and craniofacial imaging: a review of recent progress

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11451
Author(s):  
Ruiyang Ren ◽  
Haozhe Luo ◽  
Chongying Su ◽  
Yang Yao ◽  
Wen Liao
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Medical Imaging ◽  
Medical Diagnosis ◽  
Recent Progress ◽  
Medical Science ◽  
Research Progress ◽  
Daily Lives ◽  
Major Application ◽  
Machine Learning Models

Artificial intelligence has been emerging as an increasingly important aspect of our daily lives and is widely applied in medical science. One major application of artificial intelligence in medical science is medical imaging. As a major component of artificial intelligence, many machine learning models are applied in medical diagnosis and treatment with the advancement of technology and medical imaging facilities. The popularity of convolutional neural network in dental, oral and craniofacial imaging is heightening, as it has been continually applied to a broader spectrum of scientific studies. Our manuscript reviews the fundamental principles and rationales behind machine learning, and summarizes its research progress and its recent applications specifically in dental, oral and craniofacial imaging. It also reviews the problems that remain to be resolved and evaluates the prospect of the future development of this field of scientific study.

scholarly journals Artificial Intelligence in Medical Imaging of the Breast

2021 ◽  
Vol 11 ◽  
Author(s):  
Yu-Meng Lei ◽  
Miao Yin ◽  
Mei-Hui Yu ◽  
Jing Yu ◽  
Shu-E Zeng ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Medical Imaging ◽  
Malignant Tumors ◽  
Cancer Risk Assessment ◽  
Physical And Mental Health ◽  
Early Screening ◽  
Daily Lives ◽  
Breast Cancer Risk Assessment

Artificial intelligence (AI) has invaded our daily lives, and in the last decade, there have been very promising applications of AI in the field of medicine, including medical imaging, in vitro diagnosis, intelligent rehabilitation, and prognosis. Breast cancer is one of the common malignant tumors in women and seriously threatens women’s physical and mental health. Early screening for breast cancer via mammography, ultrasound and magnetic resonance imaging (MRI) can significantly improve the prognosis of patients. AI has shown excellent performance in image recognition tasks and has been widely studied in breast cancer screening. This paper introduces the background of AI and its application in breast medical imaging (mammography, ultrasound and MRI), such as in the identification, segmentation and classification of lesions; breast density assessment; and breast cancer risk assessment. In addition, we also discuss the challenges and future perspectives of the application of AI in medical imaging of the breast.

Current concepts of the interventional treatment of proximal supraaortic vessel stenosis

VASA ◽  
2012 ◽  
Vol 41 (5) ◽  
pp. 313-318 ◽  
Author(s):  
Ernemann ◽  
Bender ◽  
Melms ◽  
Brechtel ◽  
Kobba ◽  
...  
Keyword(s):  
Treatment Strategies ◽  
Carotid Bifurcation ◽  
Current Treatment ◽  
Long Term Results ◽  
Clinical Indication ◽  
Interventional Treatment ◽  
Special Focus ◽  
Brachiocephalic Artery ◽  
Treatment Concepts ◽  
Angioplasty And Stenting

Interventional therapies using angioplasty and stenting of symptomatic stenosis of the proximal supraaortic vessels have evolved as safe and effective treatment strategies. The aim of this paper is to summarize the current treatment concepts for stenosis in the subclavian and brachiocephalic artery with regard to clinical indication, interventional technique including selection of the appropriate vascular approach and type of stent, angiographic and clinical short-term and long-term results and follow-up. The role of hybrid interventions for tandem stenoses of the carotid bifurcation and brachiocephalic artery is analysed. A systematic review of data for angioplasty and stenting of symptomatic extracranial vertebral artery stenosis is discussed with a special focus on restenosis rate.

Top 100 most-cited articles on medical imaging applications of artificial intelligence: a bibliometric analysis (Preprint)

2020 ◽  
Author(s):  
Ying Liu ◽  
Ziyan Yu ◽  
Shuolan Jing ◽  
Honghu Jiang ◽  
Chunxia Wang
Keyword(s):  
Artificial Intelligence ◽  
Medical Imaging ◽  
Bibliometric Analysis ◽  
Clinical Medicine ◽  
Medical Physics ◽  
Medical Image Analysis ◽  
The United States ◽  
Independent Learning ◽  
Learning Ability ◽  
Core Technology

BACKGROUND Artificial intelligence (AI) has penetrated into almost every aspect of our lives and is rapidly changing our way of life. Recently, the new generation of AI taking machine learning and particularly deep convolutional neural network theories as the core technology, has stronger learning ability and independent learning evolution ability, combined with a large amount of learning data, breaks through the bottleneck limit of model accuracy, and makes the model efficient use. OBJECTIVE To identify the 100 most cited papers in artificial intelligence in medical imaging, we performed a comprehensive bibliometric analysis basing on the literature search on Web of Science Core Collection (WoSCC). METHODS The 100 top-cited articles published in “AI, Medical imaging” journals were identified using the Science Citation Index Database. The articles were further reviewed, and basic information was collected, including the number of citations, journals, authors, publication year, and field of study. RESULTS The highly cited articles in AI were cited between 72 and 1,554 times. The majority of them were published in three major journals: IEEE Transactions on Medical Imaging, Medical Image Analysis and Medical Physics. The publication year ranged from 2002 to 2019, with 66% published in a three-year period (2016 to 2018). Publications from the United States (56%) were the most heavily cited, followed by those from China (15%) and Netherlands (10%). Radboud University Nijmegen from Netherlands, Harvard Medical School in USA, and The Chinese University of Hong Kong in China produced the highest number of publications (n=6). Computer science (42%), clinical medicine (35%), and engineering (8%) were the most common fields of study. CONCLUSIONS Citation analysis in the field of artificial intelligence in medical imaging reveals interesting information about the topics and trends negotiated by researchers and elucidates which characteristics are required for a paper to attain a “classic” status. Clinical science articles published in highimpact specialized journals are most likely to be cited in the field of artificial intelligence in medical imaging.

scholarly journals Immune Sensing and Potential Immunotherapeutic Approaches to Control Chromoblastomycosis

2020 ◽  
Vol 7 (1) ◽  
pp. 3
Author(s):  
Leandro C. D. Breda ◽  
Isabela G. Menezes ◽  
Larissa N. M. Paulo ◽  
Sandro Rogério de Almeida
Keyword(s):  
Social Stigma ◽  
Treatment Strategies ◽  
Specific Treatment ◽  
Treatment Interruption ◽  
Lower Limbs ◽  
Disease Treatment ◽  
Immune Sensing ◽  
Skin Immunology ◽  
Anti Fungal ◽  
Made In

Chromoblastomycosis (CBM) is a neglected, chronic, and progressive subcutaneous mycosis caused by different species of fungi from the Herpotrichiellaceae family. CBM disease is usually associated with agricultural activities, and its infection is characterized by verrucous, erythematous papules, and atrophic lesions on the upper and lower limbs, leading to social stigma and impacts on patients’ welfare. The economic aspect of disease treatment is another relevant issue. There is no specific treatment for CBM, and different anti-fungal drug associations are used to treat the patients. However, the long period of the disease and the high cost of the treatment lead to treatment interruption and, consequently, relapse of the disease. In previous years, great progress had been made in the comprehension of the CBM pathophysiology. In this review, we discuss the differences in the cell wall composition of conidia, hyphae, and muriform cells, with a particular focus on the activation of the host immune response. We also highlight the importance of studies about the host skin immunology in CBM. Finally, we explore different immunotherapeutic studies, highlighting the importance of these approaches for future treatment strategies for CBM.

scholarly journals Molecular Subtypes and Precision Oncology in Intrahepatic Cholangiocarcinoma

2021 ◽  
Vol 10 (13) ◽  
pp. 2803
Author(s):  
Carolin Czauderna ◽  
Martha M. Kirstein ◽  
Hauke C. Tews ◽  
Arndt Vogel ◽  
Jens U. Marquardt
Keyword(s):  
Clinical Care ◽  
Treatment Options ◽  
Treatment Strategies ◽  
Specific Treatment ◽  
Growth Factor Receptor ◽  
Treatment Modalities ◽  
Precision Oncology ◽  
Recurrence Rates ◽  
Isocitrate Dehydrogenase 1 ◽  
Liver Cancers

Cholangiocarcinomas (CCAs) are the second-most common primary liver cancers. CCAs represent a group of highly heterogeneous tumors classified based on anatomical localization into intra- (iCCA) and extrahepatic CCA (eCCA). In contrast to eCCA, the incidence of iCCA is increasing worldwide. Curative treatment strategies for all CCAs involve oncological resection followed by adjuvant chemotherapy in early stages, whereas chemotherapy is administered at advanced stages of disease. Due to late diagnosis, high recurrence rates, and limited treatment options, the prognosis of patients remains poor. Comprehensive molecular characterization has further revealed considerable heterogeneity and distinct prognostic and therapeutic traits for iCCA and eCCA, indicating that specific treatment modalities are required for different subclasses. Several druggable alterations and oncogenic drivers such as fibroblast growth factor receptor 2 gene fusions and hotspot mutations in isocitrate dehydrogenase 1 and 2 mutations have been identified. Specific inhibitors have demonstrated striking antitumor activity in affected subgroups of patients in phase II and III clinical trials. Thus, improved understanding of the molecular complexity has paved the way for precision oncological approaches. Here, we outline current advances in targeted treatments and immunotherapeutic approaches. In addition, we delineate future perspectives for different molecular subclasses that will improve the clinical care of iCCA patients.

scholarly journals SARS-CoV-2 diagnosis using medical imaging techniques and artificial intelligence: A review

2021 ◽  
Vol 76 ◽  
pp. 6-14
Author(s):  
Narjes Benameur ◽  
Ramzi Mahmoudi ◽  
Soraya Zaid ◽  
Younes Arous ◽  
Badii Hmida ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Medical Imaging ◽  
Imaging Techniques

scholarly journals Molecular subtype-specific efficacy of anti-EGFR therapy in colorectal cancer is dependent on the chemotherapy backbone

2021 ◽  
Author(s):  
Sanne ten Hoorn ◽  
Dirkje W. Sommeijer ◽  
Faye Elliott ◽  
David Fisher ◽  
Tim R. de Back ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Molecular Subtype ◽  
Primary Tumour ◽  
Treatment Strategies ◽  
Specific Treatment ◽  
Progression Free Survival ◽  
First Line ◽  
Free Survival ◽  
Tumour Location ◽  
Selection For

Abstract Background Patient selection for addition of anti-EGFR therapy to chemotherapy for patients with RAS and BRAF wildtype metastatic colorectal cancer can still be optimised. Here we investigate the effect of anti-EGFR therapy on survival in different consensus molecular subtypes (CMSs) and stratified by primary tumour location. Methods Retrospective analyses, using the immunohistochemistry-based CMS classifier, were performed in the COIN (first-line oxaliplatin backbone with or without cetuximab) and PICCOLO trial (second-line irinotecan with or without panitumumab). Tumour tissue was available for 323 patients (20%) and 349 (41%), respectively. Results When using an irinotecan backbone, anti-EGFR therapy is effective in both CMS2/3 and CMS4 in left-sided primary tumours (progression-free survival (PFS): HR 0.44, 95% CI 0.26–0.75, P = 0.003 and HR 0.12, 95% CI 0.04–0.36, P < 0.001, respectively) and in CMS4 right-sided tumours (PFS HR 0.17, 95% CI 0.04–0.71, P = 0.02). Efficacy using an oxaliplatin backbone was restricted to left-sided CMS2/3 tumours (HR 0.57, 95% CI 0.36–0.96, P = 0.034). Conclusions The subtype-specific efficacy of anti-EGFR therapy is dependent on the chemotherapy backbone. This may provide the possibility of subtype-specific treatment strategies for a more optimal use of anti-EGFR therapy.

scholarly journals Single cell-derived clonal analysis of human glioblastoma links functional and genomic heterogeneity

2015 ◽  
Vol 112 (3) ◽  
pp. 851-856 ◽  
Author(s):  
Mona Meyer ◽  
Jüri Reimand ◽  
Xiaoyang Lan ◽  
Renee Head ◽  
Xueming Zhu ◽  
...  
Keyword(s):  
Functional Significance ◽  
Treatment Strategies ◽  
Specific Treatment ◽  
Clonal Analysis ◽  
Specific Drug ◽  
Drug Resistant ◽  
Naive Patient ◽  
Functional Behavior ◽  
Proliferation And Differentiation ◽  
Genomic Analyses

Glioblastoma (GBM) is a cancer comprised of morphologically, genetically, and phenotypically diverse cells. However, an understanding of the functional significance of intratumoral heterogeneity is lacking. We devised a method to isolate and functionally profile tumorigenic clones from patient glioblastoma samples. Individual clones demonstrated unique proliferation and differentiation abilities. Importantly, naïve patient tumors included clones that were temozolomide resistant, indicating that resistance to conventional GBM therapy can preexist in untreated tumors at a clonal level. Further, candidate therapies for resistant clones were detected with clone-specific drug screening. Genomic analyses revealed genes and pathways that associate with specific functional behavior of single clones. Our results suggest that functional clonal profiling used to identify tumorigenic and drug-resistant tumor clones will lead to the discovery of new GBM clone-specific treatment strategies.

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