scholarly journals Diagnostic performance study on the melanoma automated diagnosis software powered by artificial intelligence technologies

2020 ◽  
Vol 23 (5) ◽  
pp. 288-292
Author(s):  
Vasiliy Yu. Sergeev ◽  
Yu. Yu. Sergeev ◽  
O. B. Tamrazova ◽  
V. G. Nikitaev ◽  
A. N. Pronichev ◽  
...  

INTRODUCTION: The research evaluates a series of publications on the machine recognition efficacy of cutaneous melanoma dermatoscopic images. Some authors report high sensitivity and specificity of automated diagnostics of skin tumors. Significant differences in the published data can be attributed to the use of different algorithms and groups of skin neoplasms to calculate the accuracy rate. MATERIALS AND METHODS: The diagnostic performance of two automated artificial intelligence systems is compared. RESULTS: The convolutional neural network algorithm improves the overall diagnostic accuracy by 7% compared to the algorithm without deep learning, while the overall accuracy rate was 78%. An initial set of 100 dermatoscopic images used in the study is published online for the assessment of the applicability of the obtained data when introducing existing artificial intelligence systems. CONCLUSION: The main limitations and possible ways to further improve the automated diagnosis of skin tumors based on digital dermatoscopy are outlined.

2020 ◽  
pp. 76-78
Author(s):  
V. Yu. Sergeev ◽  
Yu. Yu. Sergeev ◽  
O. B. Tamrazova ◽  
V. G. Nikitaev ◽  
A. N. Pronichev

Despite the existence of many algorithms for automated diagnosis of melanoma and other skin cancers, these remain almost inaccessible to public health service. A small number of publications on the efficacy of existing artificial intelligence systems marks the problems of their implementation into current examination routines in dermatology and oncology. New algorithms and software solutions as well as studies demonstrating their diagnostic accuracy on compatible and verifiable clinical material are still in demand.


2021 ◽  
pp. 20201444
Author(s):  
Linda Watkins ◽  
Greg O'Neill ◽  
David Young ◽  
Claire McArthur

Objectives: To compare diagnostic performance of British Thyroid Association (BTA), American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS) and Artificial Intelligence TIRADS (AI-TIRADS) for thyroid nodule malignancy. To determine comparative unnecessary FNA rates. Methods: 218 thyroid nodules with definitive histology obtained during 2017 were included. Ultrasound (US) images were reviewed retrospectively in consensus by two subspecialist radiologists, blinded to histopathology, and nodules assigned a BTA, ACR-TIRADS and AI-TIRADS grade. Nodule laterality and size were recorded to allow accurate histopathological correlation and determine which nodules met criteria for fine needle aspiration (FNA). Results: 77 (35.3%) nodules were malignant. Deeming US Grade 4–5 as test-positive and 1–2 as test-negative, sensitivity and specificity for BTA was 98.28 and 42.55%, for ACR-TIRADS: 95.24 and 40.57% and for AI-TIRADS: 93.44 and 45.71%. FNA was indicated in 101 (71.6%), 67 (47.5%) and 65 (46.1%) benign nodules utilizing BTA, ACR-TIRADS and AI-TIRADS respectively. The unnecessary FNA rate was significantly higher with BTA (46.3%) compared to ACR-TIRADS (30.7%) and AI-TIRADS (29.8%) p < 0.001. Conclusion: BTA, ACR-TIRADS and AI-TIRADS had similar diagnostic performance for predicting thyroid nodule malignancy with sensitivity >93% for all systems when considering US Grade 4–5 as malignant and Grade 1–2 as benign. ACR-TIRADS and AI-TIRADS both had a significantly lower rate of recommended FNA in benign nodules compared to BTA. Advances in knowledge: BTA, ACR-TIRADS and AI-TIRADS have comparable diagnostic performance with high sensitivity but relatively low specificity for predicting thyroid nodule malignancy in this cohort using histology as gold standard. Using Grade 1–2 as benign and 4–5 as malignant there were more false negatives with TIRADS but this improved when taking other features into account while BTA had a significantly higher rate of unnecessary FNA. Comparison of British Thyroid Association, American College of Radiology TIRADS and Artificial Intelligence TIRADS Ultrasound Grading Systems with Histological Correlation: Diagnostic Performance for Predicting Thyroid Malignancy and Unnecessary Fine Needle Aspiration Biopsy Rate


Author(s):  
M. G. Koliada ◽  
T. I. Bugayova

The article discusses the history of the development of the problem of using artificial intelligence systems in education and pedagogic. Two directions of its development are shown: “Computational Pedagogic” and “Educational Data Mining”, in which poorly studied aspects of the internal mechanisms of functioning of artificial intelligence systems in this field of activity are revealed. The main task is a problem of interface of a kernel of the system with blocks of pedagogical and thematic databases, as well as with the blocks of pedagogical diagnostics of a student and a teacher. The role of the pedagogical diagnosis as evident reflection of the complex influence of factors and reasons is shown. It provides the intelligent system with operative and reliable information on how various reasons intertwine in the interaction, which of them are dangerous at present, where recession of characteristics of efficiency is planned. All components of the teaching and educational system are subject to diagnosis; without it, it is impossible to own any pedagogical situation optimum. The means in obtaining information about students, as well as the “mechanisms” of work of intelligent systems based on innovative ideas of advanced pedagogical experience in diagnostics of the professionalism of a teacher, are considered. Ways of realization of skill of the teacher on the basis of the ideas developed by the American scientists are shown. Among them, the approaches of researchers D. Rajonz and U. Bronfenbrenner who put at the forefront the teacher’s attitude towards students, their views, intellectual and emotional characteristics are allocated. An assessment of the teacher’s work according to N. Flanders’s system, in the form of the so-called “The Interaction Analysis”, through the mechanism of fixing such elements as: the verbal behavior of the teacher, events at the lesson and their sequence is also proposed. A system for assessing the professionalism of a teacher according to B. O. Smith and M. O. Meux is examined — through the study of the logic of teaching, using logical operations at the lesson. Samples of forms of external communication of the intellectual system with the learning environment are given. It is indicated that the conclusion of the found productive solutions can have the most acceptable and comfortable form both for students and for the teacher in the form of three approaches. The first shows that artificial intelligence in this area can be represented in the form of robotized being in the shape of a person; the second indicates that it is enough to confine oneself only to specially organized input-output systems for targeted transmission of effective methodological recommendations and instructions to both students and teachers; the third demonstrates that life will force one to come up with completely new hybrid forms of interaction between both sides in the form of interactive educational environments, to some extent resembling the educational spaces of virtual reality.


Author(s):  
Natalia V. Vysotskaya ◽  
T. V. Kyrbatskaya

The article is devoted to the consideration of the main directions of digital transformation of the transport industry in Russia. It is proposed in the process of digital transformation to integrate the community approach into the company's business model using blockchain technology and methods and results of data science; complement the new digital culture with a digital team and new communities that help management solve business problems; focus the attention of the company's management on its employees and develop those competencies in them that robots and artificial intelligence systems cannot implement: develop algorithmic, computable and non-linear thinking in all employees of the company.


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