Evaluation of an Artificial Intelligence–Augmented Digital System for Histologic Classification of Colorectal Polyps

Abstract Objectives This study evaluated the usefulness of artificial intelligence (AI) algorithms as tools in improving the accuracy of histologic classification of breast tissue. Methods Overall, 100 microscopic photographs (test A) and 152 regions of interest in whole-slide images (test B) of breast tissue were classified into 4 classes: normal, benign, carcinoma in situ (CIS), and invasive carcinoma. The accuracy of 4 pathologists and 3 pathology residents were evaluated without and with the assistance of algorithms. Results In test A, algorithm A had accuracy of 0.87, with the lowest accuracy in the benign class (0.72). The observers had average accuracy of 0.80, and most clinically relevant discordances occurred in distinguishing benign from CIS (7.1% of classifications). With the assistance of algorithm A, the observers significantly increased their average accuracy to 0.88. In test B, algorithm B had accuracy of 0.49, with the lowest accuracy in the CIS class (0.06). The observers had average accuracy of 0.86, and most clinically relevant discordances occurred in distinguishing benign from CIS (6.3% of classifications). With the assistance of algorithm B, the observers maintained their average accuracy. Conclusions AI tools can increase the classification accuracy of pathologists in the setting of breast lesions.

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Automated classification of colorectal adenoma-dysplasia-carcinoma in tissue biopsies using artificial intelligence based image evaluation

Zeitschrift für Gastroenterologie ◽

10.1055/s-0032-1312443 ◽

2012 ◽

Vol 50 (05) ◽

Author(s):

B Wichmann ◽

P Ittzés ◽

G Valcz ◽

D Szabó ◽

B Barták ◽

...

Keyword(s):

Artificial Intelligence ◽

Colorectal Adenoma ◽

Automated Classification ◽

Image Evaluation

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Classification of artificial intelligence technologies to determine the civil liability

Journal of Physics Conference Series ◽

10.1088/1742-6596/1794/1/012001 ◽

2021 ◽

Vol 1794 (1) ◽

pp. 012001

Author(s):

A Alekseev ◽

O Erakhtina ◽

K Kondratyeva ◽

T Nikitin

Keyword(s):

Artificial Intelligence ◽

Civil Liability

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Artificial Intelligence, 3D Documentation, and Rock Art—Approaching and Reflecting on the Automation of Identification and Classification of Rock Art Images

Journal of Archaeological Method and Theory ◽

10.1007/s10816-021-09518-6 ◽

2021 ◽

Author(s):

Christian Horn ◽

Oscar Ivarsson ◽

Cecilia Lindhé ◽

Rich Potter ◽

Ashely Green ◽

...

Keyword(s):

Artificial Intelligence ◽

Bronze Age ◽

Data Augmentation ◽

Rock Art ◽

Training Data ◽

Future Research ◽

Rock Surface ◽

Southern Scandinavia ◽

Bounding Boxes

AbstractRock art carvings, which are best described as petroglyphs, were produced by removing parts of the rock surface to create a negative relief. This tradition was particularly strong during the Nordic Bronze Age (1700–550 BC) in southern Scandinavia with over 20,000 boats and thousands of humans, animals, wagons, etc. This vivid and highly engaging material provides quantitative data of high potential to understand Bronze Age social structures and ideologies. The ability to provide the technically best possible documentation and to automate identification and classification of images would help to take full advantage of the research potential of petroglyphs in southern Scandinavia and elsewhere. We, therefore, attempted to train a model that locates and classifies image objects using faster region-based convolutional neural network (Faster-RCNN) based on data produced by a novel method to improve visualizing the content of 3D documentations. A newly created layer of 3D rock art documentation provides the best data currently available and has reduced inscribed bias compared to older methods. Several models were trained based on input images annotated with bounding boxes produced with different parameters to find the best solution. The data included 4305 individual images in 408 scans of rock art sites. To enhance the models and enrich the training data, we used data augmentation and transfer learning. The successful models perform exceptionally well on boats and circles, as well as with human figures and wheels. This work was an interdisciplinary undertaking which led to important reflections about archaeology, digital humanities, and artificial intelligence. The reflections and the success represented by the trained models open novel avenues for future research on rock art.

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Convolutional Neural Network-Based Artificial Intelligence for Classification of Protein Localization Patterns

Biomolecules ◽

10.3390/biom11020264 ◽

2021 ◽

Vol 11 (2) ◽

pp. 264

Author(s):

Kaisa Liimatainen ◽

Riku Huttunen ◽

Leena Latonen ◽

Pekka Ruusuvuori

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Convolutional Neural Network ◽

High Throughput ◽

Protein Function ◽

Protein Localization ◽

Convolutional Network ◽

High Throughput Analysis ◽

Fully Convolutional Network

Identifying localization of proteins and their specific subpopulations associated with certain cellular compartments is crucial for understanding protein function and interactions with other macromolecules. Fluorescence microscopy is a powerful method to assess protein localizations, with increasing demand of automated high throughput analysis methods to supplement the technical advancements in high throughput imaging. Here, we study the applicability of deep neural network-based artificial intelligence in classification of protein localization in 13 cellular subcompartments. We use deep learning-based on convolutional neural network and fully convolutional network with similar architectures for the classification task, aiming at achieving accurate classification, but importantly, also comparison of the networks. Our results show that both types of convolutional neural networks perform well in protein localization classification tasks for major cellular organelles. Yet, in this study, the fully convolutional network outperforms the convolutional neural network in classification of images with multiple simultaneous protein localizations. We find that the fully convolutional network, using output visualizing the identified localizations, is a very useful tool for systematic protein localization assessment.

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Application of Artificial Intelligence in Food Industry—a Guideline

Food Engineering Reviews ◽

10.1007/s12393-021-09290-z ◽

2021 ◽

Author(s):

Nidhi Rajesh Mavani ◽

Jarinah Mohd Ali ◽

Suhaili Othman ◽

M. A. Hussain ◽

Haslaniza Hashim ◽

...

Keyword(s):

Artificial Intelligence ◽

Intelligent Systems ◽

Food Industry ◽

Near Infrared ◽

Vision System ◽

Electronic Tongue ◽

World Population ◽

Computer Vision System ◽

The Past

AbstractArtificial intelligence (AI) has embodied the recent technology in the food industry over the past few decades due to the rising of food demands in line with the increasing of the world population. The capability of the said intelligent systems in various tasks such as food quality determination, control tools, classification of food, and prediction purposes has intensified their demand in the food industry. Therefore, this paper reviews those diverse applications in comparing their advantages, limitations, and formulations as a guideline for selecting the most appropriate methods in enhancing future AI- and food industry–related developments. Furthermore, the integration of this system with other devices such as electronic nose, electronic tongue, computer vision system, and near infrared spectroscopy (NIR) is also emphasized, all of which will benefit both the industry players and consumers.

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PATH-46. AUTOMATED HISTOPATHOLOGIC CLASSIFICATION OF BRAIN TUMORS USING ARTIFICIAL INTELLIGENCE

Neuro-Oncology ◽

10.1093/neuonc/nox168.736 ◽

2017 ◽

Vol 19 (suppl_6) ◽

pp. vi181-vi181

Author(s):

Quin Xie ◽

Dominick Han ◽

Kevin Faust ◽

Kenneth Aldape ◽

Gelareh Zadeh ◽

...

Keyword(s):

Artificial Intelligence ◽

Brain Tumors

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