An Overview of Current Trends, Techniques, Prospects, and Pitfalls of Artificial Intelligence in Breast Imaging

Abstract Background The classification of Breast Imaging Reporting and Data System 4A (BI-RADS 4A) lesions is mostly based on the personal experience of doctors and lacks specific and clear classification standards. The development of artificial intelligence (AI) provides a new method for BI-RADS categorisation. We analysed the ultrasonic morphological and texture characteristics of BI-RADS 4A benign and malignant lesions using AI, and these ultrasonic characteristics of BI-RADS 4A benign and malignant lesions were compared to examine the value of AI in the differential diagnosis of BI-RADS 4A benign and malignant lesions. Methods A total of 206 lesions of BI-RADS 4A examined using ultrasonography were analysed retrospectively, including 174 benign lesions and 32 malignant lesions. All of the lesions were contoured manually, and the ultrasonic morphological and texture features of the lesions, such as circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, margin lobulation, energy, entropy, grey mean, internal calcification and angle between the long axis of the lesion and skin, were calculated using grey level gradient co-occurrence matrix analysis. Differences between benign and malignant lesions of BI-RADS 4A were analysed. Results Significant differences in margin lobulation, entropy, internal calcification and ALS were noted between the benign group and malignant group (P = 0.013, 0.045, 0.045, and 0.002, respectively). The malignant group had more margin lobulations and lower entropy compared with the benign group, and the benign group had more internal calcifications and a greater angle between the long axis of the lesion and skin compared with the malignant group. No significant differences in circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, energy, and grey mean were noted between benign and malignant lesions. Conclusions Compared with the naked eye, AI can reveal more subtle differences between benign and malignant BI-RADS 4A lesions. These results remind us carefully observation of the margin and the internal echo is of great significance. With the help of morphological and texture information provided by AI, doctors can make a more accurate judgment on such atypical benign and malignant lesions.

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Government by Algorithm: Artificial Intelligence in Federal Administrative Agencies, a Case of USA

European Journal of Technology ◽

10.47672/ejt.641 ◽

2021 ◽

Vol 5 (1) ◽

pp. 1-15

Author(s):

Rubina Shaheen ◽

Mir Kasi

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Thematic Analysis ◽

Image Data ◽

Workplace Health ◽

Current Trends ◽

Safety And Health ◽

Administrative Agencies ◽

Conventional Machine ◽

The Government

The report gives a presents use of artificial intelligence in few administrative agencies. In-depth thematic analysis of some institution, have been conducted to review the current trends. In thematic analysis, 12 institutions have been selected and described the details of the institutions using artificial intelligence in different departments. These analyses yielded five major findings. First, the government has a wide application of Artificial Intelligence toolkit traversing the federal administrative and state. Almost half of the federal agencies evaluated (45%) has used AI and associated machine learning (ML) tools. Also, AI tools are already enhancing agency strategies in the full span of governance responsibilities, such as keeping regulatory assignments bordering on market efficiency, safety in workplace, health care, and protection of the environmental, protecting the privileges and benefits of the government ranging from intellectual properties to disability, accessing, verifying and analyzing all risks to public safety and health, Extracting essential data from the data stream of government including complaints by consumer and the communicating with citizens on their rights, welfare, asylum seeking and business ownership. AI toolkit owned by government span the complete scope of Artificial Intelligence techniques, ranging from conventional machine learning to deep learning including natural language and image data. Irrespective of huge acceptance of AI, much still has to be done in this area by the government. Recommendations also discussed at the end.

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Factors Influencing Trainees’ Interest in Breast Imaging

Canadian Association of Radiologists Journal ◽

10.1177/08465371211049553 ◽

2021 ◽

pp. 084653712110495

Author(s):

Tong Wu ◽

Wyanne Law ◽

Nayaar Islam ◽

Charlotte J. Yong-Hing ◽

Supriya Kulkarni ◽

...

Keyword(s):

Artificial Intelligence ◽

Medical Students ◽

Breast Imaging ◽

Current Level ◽

Common Reason ◽

Training Level ◽

Chi Square ◽

Factors Influencing ◽

Radiology Residents ◽

The Impact

Purpose: To gauge the level of interest in breast imaging (BI) and determine factors impacting trainees’ decision to pursue this subspecialty. Methods: Canadian radiology residents and medical students were surveyed from November 2020 to February 2021. Training level, actual vs preferred timing of breast rotations, fellowship choices, perceptions of BI, and how artificial intelligence (AI) will impact BI were collected. Chi-square, Fisher’s exact tests and univariate logistic regression were performed to determine the impact of trainees’ perceptions on interest in pursuing BI/women’s imaging (WI) fellowships. Results: 157 responses from 80 radiology residents and 77 medical students were collected. The top 3 fellowship subspecialties desired by residents were BI/WI (36%), abdominal imaging (35%), and interventional radiology (25%). Twenty-five percent of the medical students were unsure due to lack of exposure. The most common reason that trainees found BI unappealing was repetitiveness (20%), which was associated with lack of interest in BI/WI fellowships (OR = 3.9, 95% CI: 1.6-9.5, P = .002). The most common reason residents found BI appealing was procedures (59%), which was associated with interest in BI/WI fellowships (OR, 3.2, 95% CI, 1.2-8.6, P = .02). Forty percent of residents reported an earlier start of their first breast rotation (PGY1-2) would affect their fellowship choice. Conclusion: This study assessed the current level of Canadian trainees’ interest in BI and identified factors that influenced their decisions to pursue BI. Solutions for increased interest include earlier exposure to breast radiology and addressing inadequacies in residency training.

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A Semantic Web Pragmatic Approach to Develop Clinical Ontologies, and thus Semantic Interoperability, based in HL7 v2.xml Messaging

Information Systems and Technologies for Enhancing Health and Social Care ◽

10.4018/978-1-4666-3667-5.ch014 ◽

2013 ◽

pp. 205-214 ◽

Cited By ~ 2

Author(s):

David Mendes ◽

Irene Pimenta Rodrigues

Keyword(s):

Artificial Intelligence ◽

Semantic Web ◽

Knowledge Representation ◽

Ontology Mapping ◽

Knowledge Representation And Reasoning ◽

Healthcare Applications ◽

Current Trends ◽

Web Tools ◽

Ontology Population ◽

Tools And Techniques

The ISO/HL7 27931:2009 standard intends to establish a global interoperability framework for healthcare applications. However, being a messaging related protocol, it lacks a semantic foundation for interoperability at a machine treatable level intended through the Semantic Web. There is no alignment between the HL7 V2.xml message payloads and a meaning service like a suitable ontology. Careful application of Semantic Web tools and concepts can ease the path to the fundamental concept of Shared Semantics. In this chapter, the Semantic Web and Artificial Intelligence tools and techniques that allow aligned ontology population are presented and their applicability discussed. The authors present the coverage of HL7 RIM inadequacy for ontology mapping and how to circumvent it, NLP techniques for semi-automated ontology population, and the current trends about knowledge representation and reasoning that concur to the proposed achievement.

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What is happening to laboratory medicine in Denmark?

Clinical Chemistry and Laboratory Medicine (CCLM) ◽

10.1515/cclm-2018-0590 ◽

2018 ◽

Vol 0 (0) ◽

Author(s):

Per E. Jørgensen

Keyword(s):

Artificial Intelligence ◽

Health Care ◽

Digital Health ◽

Laboratory Medicine ◽

Positive Development ◽

Scandinavian Country ◽

Health Records ◽

Current Trends ◽

Technological Developments ◽

Almost All

Abstract A number of current trends will affect and probably change laboratory medicine, as we know it. Scientific and technological developments, digital health with big data and artificial intelligence, and centralization will change the interfaces among the specialties of laboratory medicine. They might even challenge the identity of some specialties. Other trends such as demographic changes, increased complexity of health care, digital health with electronic health records, and more demanding and well-informed patients will change the way laboratory medicine specialties deliver their services. This paper discusses the possible changes of laboratory medicine in Denmark – a Scandinavian country where almost all hospitals are public. If Danish laboratories grasp the new possibilities instead of trying to avoid them, laboratory medicine is likely to prosper. Such a positive development will call upon good leadership and a genuine willingness among laboratory specialist to adapt to a future where their own specialty might be very different from today.

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Exploring the Current Trends of Artificial Intelligence in Stem Cell Therapy: A Systematic Review

Cureus ◽

10.7759/cureus.20083 ◽

2021 ◽

Author(s):

Mirra Srinivasan ◽

Santhosh Raja Thangaraj ◽

Krishnamurthy Ramasubramanian ◽

Padma Pradha Thangaraj ◽

Krishna Vyas Ramasubramanian

Keyword(s):

Artificial Intelligence ◽

Systematic Review ◽

Stem Cell ◽

Cell Therapy ◽

Stem Cell Therapy ◽

Current Trends

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Artificial intelligence in services: current trends, benefits and challenges

Service Industries Journal ◽

10.1080/02642069.2021.1989177 ◽

2021 ◽

Vol 41 (13-14) ◽

pp. 853-859

Author(s):

Carlos Flavián ◽

Luis V. Casaló

Keyword(s):

Artificial Intelligence ◽

Current Trends

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Deep learning, past present and future: An odyssey

10.31224/osf.io/vrmk4 ◽

2021 ◽

Author(s):

Anwaar Ulhaq

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Deep Neural Networks ◽

Review Paper ◽

Complex Problems ◽

Current Trends

Machine learning has grown in popularity and effectiveness over the last decade. It has become possible to solve complex problems, especially in artificial intelligence, due to the effectiveness of deep neural networks. While numerous books and countless papers have been written on deep learning, new researchers want to understand the field's history, current trends and envision future possibilities. This review paper will summarise the recorded work that resulted in such success and address patterns and prospects.

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