scholarly journals Current Trends of Artificial Intelligence for Colorectal Cancer Pathology Image Analysis: A Systematic Review

Cancers ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1884 ◽  
Author(s):  
Nishant Thakur ◽  
Hongjun Yoon ◽  
Yosep Chong
Keyword(s):  
Artificial Intelligence ◽  
Colorectal Cancer ◽  
Systematic Review ◽  
Image Analysis ◽  
State Of The Art ◽  
The State ◽  
Cochrane Library ◽  
Routine Practice ◽  
Cancer Pathology ◽  
Pathology Image

Colorectal cancer (CRC) is one of the most common cancers requiring early pathologic diagnosis using colonoscopy biopsy samples. Recently, artificial intelligence (AI) has made significant progress and shown promising results in the field of medicine despite several limitations. We performed a systematic review of AI use in CRC pathology image analysis to visualize the state-of-the-art. Studies published between January 2000 and January 2020 were searched in major online databases including MEDLINE (PubMed, Cochrane Library, and EMBASE). Query terms included “colorectal neoplasm,” “histology,” and “artificial intelligence.” Of 9000 identified studies, only 30 studies consisting of 40 models were selected for review. The algorithm features of the models were gland segmentation (n = 25, 62%), tumor classification (n = 8, 20%), tumor microenvironment characterization (n = 4, 10%), and prognosis prediction (n = 3, 8%). Only 20 gland segmentation models met the criteria for quantitative analysis, and the model proposed by Ding et al. (2019) performed the best. Studies with other features were in the elementary stage, although most showed impressive results. Overall, the state-of-the-art is promising for CRC pathological analysis. However, datasets in most studies had relatively limited scale and quality for clinical application of this technique. Future studies with larger datasets and high-quality annotations are required for routine practice-level validation.

Current practices of management of congenital Cytomegalovirus infection during pregnancy and childhood: systematic review protocol

2020 ◽  
Author(s):  
Claire Perillaud Dubois ◽  
Drifa Belhadi ◽  
Cédric Laouénan ◽  
Laurent Mandelbrot ◽  
Christelle Vauloup-Fellous ◽  
...  
Keyword(s):  
Systematic Review ◽  
Pregnant Women ◽  
State Of The Art ◽  
Grey Literature ◽  
The State ◽  
Cochrane Library ◽  
Cmv Infection ◽  
Congenital Cmv Infection ◽  
Congenital Cmv ◽  
Current Practices

Abstract Background: Congenital CMV infection is the first worldwide cause of congenital viral infection and a major cause of sensorineural hearing loss and mental retardation. As systematic screening of pregnant women and newborns is still debated in many countries, this systematic review aims to provide the state of the art on current practices concerning management of congenital CMV infection.Methods: We will perform electronically searches on MEDLINE, EMBASE, Cochrane Library (CENTRAL), ClinicalTrials.gov, Web of Science and hand searches in grey literature. Interventions regarding biological, imaging, and therapeutic management of infected pregnant women, fetuses and neonates/children (from birth to 6 years old) will be studied in this systematic review. Study screening will be performed in duplicate by two independent reviewers and risk of bias will be evaluated with the ROBINS-I tool. Discussion: This review will provide the state of the art of current management of congenital CMV infection in pregnant women, fetuses, neonates and children until 6 years old, in order to have an overview of current practices of congenital CMV infection.Systematic review registration: PROSPERO CRD42019124342

scholarly journals Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sergei Bedrikovetski ◽  
Nagendra N. Dudi-Venkata ◽  
Hidde M. Kroon ◽  
Warren Seow ◽  
Ryash Vather ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Colorectal Cancer ◽  
Systematic Review ◽  
Deep Learning ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Meta Analysis ◽  
Cochrane Library ◽  
Learning Models ◽  
Node Metastasis

Abstract Background Artificial intelligence (AI) is increasingly being used in medical imaging analysis. We aimed to evaluate the diagnostic accuracy of AI models used for detection of lymph node metastasis on pre-operative staging imaging for colorectal cancer. Methods A systematic review was conducted according to PRISMA guidelines using a literature search of PubMed (MEDLINE), EMBASE, IEEE Xplore and the Cochrane Library for studies published from January 2010 to October 2020. Studies reporting on the accuracy of radiomics models and/or deep learning for the detection of lymph node metastasis in colorectal cancer by CT/MRI were included. Conference abstracts and studies reporting accuracy of image segmentation rather than nodal classification were excluded. The quality of the studies was assessed using a modified questionnaire of the QUADAS-2 criteria. Characteristics and diagnostic measures from each study were extracted. Pooling of area under the receiver operating characteristic curve (AUROC) was calculated in a meta-analysis. Results Seventeen eligible studies were identified for inclusion in the systematic review, of which 12 used radiomics models and five used deep learning models. High risk of bias was found in two studies and there was significant heterogeneity among radiomics papers (73.0%). In rectal cancer, there was a per-patient AUROC of 0.808 (0.739–0.876) and 0.917 (0.882–0.952) for radiomics and deep learning models, respectively. Both models performed better than the radiologists who had an AUROC of 0.688 (0.603 to 0.772). Similarly in colorectal cancer, radiomics models with a per-patient AUROC of 0.727 (0.633–0.821) outperformed the radiologist who had an AUROC of 0.676 (0.627–0.725). Conclusion AI models have the potential to predict lymph node metastasis more accurately in rectal and colorectal cancer, however, radiomics studies are heterogeneous and deep learning studies are scarce. Trial registration PROSPERO CRD42020218004.

scholarly journals Artificial Intelligence in Lung Cancer Pathology Image Analysis

Cancers ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1673 ◽  
Author(s):  
Shidan Wang ◽  
Donghan M. Yang ◽  
Ruichen Rong ◽  
Xiaowei Zhan ◽  
Junya Fujimoto ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Image Analysis ◽  
Deep Learning ◽  
Digital Pathology ◽  
Diagnosis And Prognosis ◽  
Model Interpretation ◽  
Lung Cancer Diagnosis ◽  
Cancer Pathology ◽  
Pathology Image

Objective: Accurate diagnosis and prognosis are essential in lung cancer treatment selection and planning. With the rapid advance of medical imaging technology, whole slide imaging (WSI) in pathology is becoming a routine clinical procedure. An interplay of needs and challenges exists for computer-aided diagnosis based on accurate and efficient analysis of pathology images. Recently, artificial intelligence, especially deep learning, has shown great potential in pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection. Materials and Methods: In this review, we aim to provide an overview of current and potential applications for AI methods in pathology image analysis, with an emphasis on lung cancer. Results: We outlined the current challenges and opportunities in lung cancer pathology image analysis, discussed the recent deep learning developments that could potentially impact digital pathology in lung cancer, and summarized the existing applications of deep learning algorithms in lung cancer diagnosis and prognosis. Discussion and Conclusion: With the advance of technology, digital pathology could have great potential impacts in lung cancer patient care. We point out some promising future directions for lung cancer pathology image analysis, including multi-task learning, transfer learning, and model interpretation.

Proteomics of Pancreatic Neuroendocrine Tumors: A Systematic Review

2020 ◽  
Vol 27 (12) ◽  
pp. 1276-1287
Author(s):  
Brigida Anna Maiorano ◽  
Giovanni Schinzari ◽  
Sabrina Chiloiro ◽  
Felicia Visconti ◽  
Domenico Milardi ◽  
...  
Keyword(s):  
Systematic Review ◽  
Neuroendocrine Tumors ◽  
Prognostic Markers ◽  
State Of The Art ◽  
Therapeutic Targets ◽  
The State ◽  
Pancreatic Neuroendocrine Tumors ◽  
Rare Tumors ◽  
Personalized Approach ◽  
Future Management

Pancreatic neuroendocrine tumors (PanNETs) are rare tumors having usually an indolent behavior, but sometimes with unpredictable aggressiveness. PanNETs are more often non-functioning (NF), unable to produce functioning hormones, while 10-30% present as functioning (F) - PanNETs, such as insulinomas , gastrinomas , and other rare tumors. Diagnostic and prognostic markers, but also new therapeutic targets, are still lacking. Proteomics techniques represent therefore promising approaches for the future management of PanNETs. We conducted a systematic review to summarize the state of the art of proteomics in PanNETs. A total of 9 studies were included, focusing both on NF- and F-PanNETs. Indeed, proteomics is useful for the diagnosis, the prognosis and the detection of therapeutic targets. However, further studies are required. It is also warranted to standardize the analysis methods and the collection techniques, in order to validate proteins with a relevance in the personalized approach to PanNETs management.

scholarly journals Communication in decision aids for stage I–III colorectal cancer patients: a systematic review

BMJ Open ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. e044472
Author(s):  
Saar Hommes ◽  
Ruben Vromans ◽  
Felix Clouth ◽  
Xander Verbeek ◽  
Ignace de Hingh ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Systematic Review ◽  
Decision Making ◽  
Systematic Reviews ◽  
Decision Aids ◽  
Specific Treatment ◽  
Cochrane Library ◽  
Plain Language ◽  
Patient Decision

ObjectivesTo assess the communicative quality of colorectal cancer patient decision aids (DAs) about treatment options, the current systematic review was conducted.DesignSystematic review.Data sourcesDAs (published between 2006 and 2019) were identified through academic literature (MEDLINE, Embase, CINAHL, Cochrane Library and PsycINFO) and online sources.Eligibility criteriaDAs were only included if they supported the decision-making process of patients with colon, rectal or colorectal cancer in stages I–III.Data extraction and synthesisAfter the search strategy was adapted from similar systematic reviews and checked by a colorectal cancer surgeon, two independent reviewers screened and selected the articles. After initial screening, disagreements were resolved with a third reviewer. The review was conducted in concordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. DAs were assessed using the International Patient Decision Aid Standards (IPDAS) and Communicative Aspects (CA) checklist.ResultsIn total, 18 DAs were selected. Both the IPDAS and CA checklist revealed that there was a lot of variation in the (communicative) quality of DAs. The findings highlight that (1) personalisation of treatment information in DAs is lacking, (2) outcome probability information is mostly communicated verbally and (3) information in DAs is generally biased towards a specific treatment. Additionally, (4) DAs about colorectal cancer are lengthy and (5) many DAs are not written in plain language.ConclusionsBoth instruments (IPDAS and CA) revealed great variation in the (communicative) quality of colorectal cancer DAs. Developers of patient DAs should focus on personalisation techniques and could use both the IPDAS and CA checklist in the developmental process to ensure personalised health communication and facilitate shared decision making in clinical practice.

scholarly journals A Systematic Review of Recommender Systems and Their Applications in Cybersecurity

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5248
Author(s):  
Aleksandra Pawlicka ◽  
Marek Pawlicki ◽  
Rafał Kozik ◽  
Ryszard S. Choraś
Keyword(s):  
Systematic Review ◽  
Recommender Systems ◽  
Recommender System ◽  
State Of The Art ◽  
The State ◽  
Advantages And Disadvantages ◽  
Comprehensive Survey ◽  
Security Concerns ◽  
Valuable Role

This paper discusses the valuable role recommender systems may play in cybersecurity. First, a comprehensive presentation of recommender system types is presented, as well as their advantages and disadvantages, possible applications and security concerns. Then, the paper collects and presents the state of the art concerning the use of recommender systems in cybersecurity; both the existing solutions and future ideas are presented. The contribution of this paper is two-fold: to date, to the best of our knowledge, there has been no work collecting the applications of recommenders for cybersecurity. Moreover, this paper attempts to complete a comprehensive survey of recommender types, after noticing that other works usually mention two–three types at once and neglect the others.

scholarly journals Artificial intelligence and machine learning in design of mechanical materials

2021 ◽  
Author(s):  
Kai Guo ◽  
Zhenze Yang ◽  
Chi-Hua Yu ◽  
Markus J. Buehler
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
State Of The Art ◽  
The State

This review revisits the state of the art of research efforts on the design of mechanical materials using machine learning.

scholarly journals What you always wanted to know about the deterministic part of the International Planning Competition (IPC) 2014 (but were too afraid to ask)

2018 ◽  
Vol 33 ◽  
Author(s):  
Mauro Vallati ◽  
Lukáš Chrpa ◽  
Thomas L. Mccluskey
Keyword(s):  
Artificial Intelligence ◽  
State Of The Art ◽  
The State ◽  
Strong Impact ◽  
Significant Progress ◽  
Planning Techniques ◽  
Artificial Intelligence Planning ◽  
The Future ◽  
International Planning ◽  
Planning Approaches

AbstractThe International Planning Competition (IPC) is a prominent event of the artificial intelligence planning community that has been organized since 1998; it aims at fostering the development and comparison of planning approaches, assessing the state-of-the-art in planning and identifying new challenging benchmarks. IPC has a strong impact also outside the planning community, by providing a large number of ready-to-use planning engines and testing pioneering applications of planning techniques.This paper focusses on the deterministic part of IPC 2014, and describes format, participants, benchmarks as well as a thorough analysis of the results. Generally, results of the competition indicates some significant progress, but they also highlight issues and challenges that the planning community will have to face in the future.

The State of the Art in Biological Image Analysis

Cell Biology ◽  
2006 ◽  
pp. 201-206
Author(s):  
F FEDERICI ◽  
S SCAGLIONE ◽  
A DIASPRO
Keyword(s):  
Image Analysis ◽  
State Of The Art ◽  
The State ◽  
Biological Image

scholarly journals Análisis crítico de los NeuroDerechos Humanos al libre albedrío y al acceso equitativo a tecnologías de mejora.

2020 ◽  
Vol 6 (2) ◽  
pp. 135-161
Author(s):  
Diego Alejandro Borbón Rodríguez ◽  
◽  
Luisa Fernanda Borbón Rodríguez ◽  
Jeniffer Laverde Pinzón
Keyword(s):  
Artificial Intelligence ◽  
Human Rights ◽  
Free Will ◽  
New Technologies ◽  
State Of The Art ◽  
The State ◽  
Columbia University ◽  
Equitable Access ◽  
Ethical Implications ◽  
Legal Limits

Advances in neurotechnologies and artificial intelligence have led to an innovative proposal to establish ethical and legal limits to the development of technologies: Human NeuroRights. In this sense, the article addresses, first, some advances in neurotechnologies and artificial intelligence, as well as their ethical implications. Second, the state of the art on the innovative proposal of Human NeuroRights is exposed, specifically, the proposal of the NeuroRights Initiative of Columbia University. Third, the proposal for the rights of free will and equitable access to augmentation technologies is critically analyzed to conclude that, although it is necessary to propose new regulations for neurotechnologies and artificial intelligence, the debate is still very premature as if to try to incorporate a new category of human rights that may be inconvenient or unnecessary. Finally, some considerations on how to regulate new technologies are explained and the conclusions of the work are presented.

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