ABSTRACT Objective: To explore the diagnostic value of the TUIAS (SW_TH01/II) computer-aided diagnosis (CAD) software system for the ultrasound Thyroid Imaging Reporting and Data System (TI-RADS) features in thyroid nodules. Materials and methods: This retrospective study enrolled patients with thyroid nodules in Shanghai East Hospital between January 2017 and October 2021. The novel CAD software (SW_TH01/II) and three sonographers performed a qualitative analysis of the ultrasound TI-RADS features in aspect ratio, margin irregularity, margin smoothness, calcification, and echogenicity of the thyroid nodules. Results: A total of 225 patients were enrolled. The accuracy, sensitivity, and specificity of the CAD software in “aspect ratio” were 95.6%, 96.2%, and 95.4%, in “margin irregularity” were 90.7%, 90.5%, and 90.9%, in “margin smoothness” were 85.8%, 88.5%, and 83.0%, in “calcification” were 83.6%, 81.7%, and 82.0%, in “homogeneity” were 88.9%, 90.6%, and 82.2%, in “major echo” were 85.3%, 88.0%, and 85.4%, and in “contains very hypoechoic echo” were 92.0%, 90.0%, and 92.4%. The analysis time of the CAD software was significantly shorter than for the sonographers (2.7 ± 1.6 vs. 29.7 ± 12.7 s, P < 0.001). Conclusion: The CAD system achieved high accuracy in describing thyroid nodule features. It might assist in clinical thyroid nodule analysis. Objective SW_TH01/II SWTH01II SWTHII SW TH01 II TH (SW_TH01/II computeraided computer aided (CAD TIRADS TI RADS (TI-RADS methods 201 2021 ratio irregularity smoothness calcification Results 22 sensitivity 956 95 6 95.6% 962 96 2 96.2% 954 4 95.4% 907 90 7 90.7% 905 5 90.5% 909 9 90.9% 858 85 8 85.8% 885 88 88.5% 830 83 0 83.0% “calcification 836 83.6% 817 81 81.7% 820 82 82.0% homogeneity “homogeneity 889 88.9% 906 90.6% 822 82.2% major echo 853 3 85.3% 880 88.0% 854 85.4% contains 920 92 92.0% 900 90.0% 924 92.4% 2.7 27 (2. 16 1 1. vs 297 29 29. 127 12 12. s 0.001. 0001 0.001 . 001 0.001) Conclusion SWTH TH0 20 202 95.6 96.2 95.4 90.7 90.5 90.9 85.8 88.5 83.0 83.6 81.7 82.0 88.9 90.6 82.2 85.3 88.0 85.4 92.0 90.0 92.4 2. (2 000 0.00 00 95. 96. 90. 85. 88. 83. 81. 82. 92. ( 0.0 0.

O objetivo deste artigo é apresentar um novo modelo de relações topológicas fuzzy para objetos espaciais simples em Sistemas de Informações Geográficas (SIG). Aplica-se o conceito de espaço topológico fuzzy a objetos fuzzy simples para resolver as relações topológicas de modo mais eficiente e acurado. Inicialmente é proposta uma nova definição para segmentos de linha fuzzy e para regiões fuzzy com base na topologia computacional. Em seguida propõe-se um novo modelo para calcular as relações topológicas fuzzy entre os objetos espaciais. A análise do novo modelo aponta: (1) relações topológicas de dois objetos "crisp"; (2) relações topológicas entre um objeto "crisp" e um objeto fuzzy; (3) relações topológicas entre dois objetos "crisp". Finalmente, discutem-se alguns exemplos para demonstrar a validade do modelo, por meio de um experimento e comparações entre modelos existentes. É possível demonstrar que o método proposto pode realizar distinções mais precisas e á mais expressivo do que os modelos fuzzy existentes.

The goal of this paper is to present a new model of fuzzy topological relations for simple spatial objects in Geographic Information Sciences (GIS). The concept of computational fuzzy topological space is applied to simple fuzzy objects to efficiently and more accurately solve fuzzy topological relations, extending and improving upon previous research in this area. Firstly, we propose a new definition for simple fuzzy line segments and simple fuzzy regions based on computational fuzzy topology. And then, we also propose a new model to compute fuzzy topological relations between simple spatial objects, an analysis of the new model exposes:(1) the topological relations of two simple crisp objects; (2) the topological relations between one simple crisp object and one simple fuzzy object; (3) the topological relations between two simple fuzzy objects. In the end, we have discussed some examples to demonstrate the validity of the new model, through an experiment and comparisons of existing models, we showed that the proposed method can make finer distinctions, as it is more expressive than the existing fuzzy models.