A Mechatronic Platform for Computer Aided Detection of Nodules in Anatomopathological Analyses via Stiffness and Ultrasound Measurements

Massari, Luca; Bulletti, Andrea; Prasanna, Sahana; Mazzoni, Marina; Frosini, Francesco; Vicari, Elena; Pantano, Marcello; Staderini, Fabio; Ciuti, Gastone; Cianchi, Fabio; Messerini, Luca; Capineri, Lorenzo; Menciassi, Arianna; Oddo, Calogero Maria

doi:10.3390/s19112512

Cited by 4 publications

(10 citation statements)

References 37 publications

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“…Furthermore, we used agar inclusions to mimic cancer lesions. Since the ultrasonic Correlation Index Amplitude (CIA) already tested on agar-based phantoms with spherical inclusions of higher agar density correctly identified only 56% of inclusions [18], we found this parameter alone insufficient to give a good statistical classification of agar nodules. Then we introduced a second correlation index, Correlation Index Shape (CIS), for the evaluation of the shape modifications of signals also.…”

Section: Introductionmentioning

confidence: 84%

“…The probe is part of an automated system reported in [18] and shown in Figure 1. The ultrasound probe (Sonomed, mod.…”

Section: Experimental Set-upmentioning

confidence: 99%

“…Preliminary tests were performed on an agar block-shaped phantom already used in the previous experimentation, which was realized to mimic both the mechanical and the acoustic properties of diseased human tissues [18]. This was done in order to validate the new ultrasonic analysis approach on phantoms before using the procedure on samples of liver tissue.…”

Section: Design and Manufacture Of Agar-phantoms With Agar-spherical mentioning

confidence: 99%

“…This was one goal of the IMEROS (Integrated MEdical-RObotic Solutions) project which focused on the study of new integrated robotic technologies for intraoperative tele-assisted diagnosis and for the surgical treatment of tumors. In this project, we proposed US techniques associated with tactile sensing to analyze stiffness and accurately localize tumor nodules [18]. Since the US methods for ex-vivo analyses had to operate with dry coupling in order to avoid contamination for the successive pathological analysis, we evaluated US imaging with commercial arrays that were not suitable.…”

Section: Introductionmentioning

confidence: 99%

“…At first for the validation of this new strategy, we used a previously fabricated agar phantom with spherical inclusions of different diameters [18] to mimic cancer nodules. The fixed position of inclusions inside the matrix and their designed dimensions allowed the evaluation of the CIA and CIS maps finalized to the individuation of nodules by the localization of the regions of maximum attenuation, and, possibly, in the determination of the nodule's lateral extension.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Strategy for Detection and Localization of Nodules in Liver Tissues by a 16 MHz Needle Ultrasonic Probe Mounted on a Robotic Platform

Bulletti

Mazzoni

Prasanna

et al. 2020

Sensors

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This study presents an improved strategy for the detection and localization of small size nodules (down to few mm) of agar in excised pork liver tissues via pulse-echo ultrasound measurements performed with a 16 MHz needle probe. This work contributes to the development of a new generation of medical instruments to support robotic surgery decision processes that need information about cancerous tissues in a short time (minutes). The developed ultrasonic probe is part of a scanning platform designed for the automation of surgery-associated histological analyses. It was coupled with a force sensor to control the indentation of tissue samples placed on a steel plate. For the detection of nodules, we took advantage of the property of nodules of altering not only the acoustical properties of tissues producing ultrasound attenuation, but also of developing patterns at their boundary that can modify the shape and the amplitude of the received echo signals from the steel plate supporting the tissues. Besides the Correlation Index Amplitude (CIA), which is linked to the overall amplitude changes of the ultrasonic signals, we introduced the Correlation Index Shape (CIS) linked to their shape changes. Furthermore, we applied AND-OR logical operators to these correlation indices. The results were found particularly helpful in the localization of the irregular masses of agar we inserted into some excised liver tissues, and in the individuation of the regions of major interest over which perform the vertical dissections of tissues in an automated analysis finalized to histopathology. We correctly identified up to 89% of inclusions, with an improvement of about 14% with respect to the result obtained (78%) from the analysis performed with the CIA parameter only.

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Section: Introductionmentioning

confidence: 84%

“…The probe is part of an automated system reported in [18] and shown in Figure 1. The ultrasound probe (Sonomed, mod.…”

Section: Experimental Set-upmentioning

confidence: 99%

Section: Design and Manufacture Of Agar-phantoms With Agar-spherical mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Improved Strategy for Detection and Localization of Nodules in Liver Tissues by a 16 MHz Needle Ultrasonic Probe Mounted on a Robotic Platform

Bulletti

Mazzoni

Prasanna

et al. 2020

Sensors

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Using Artificial Neural Network to Detect Fetal Alcohol Spectrum Disorder in Children

et al. 2021

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Fetal alcohol spectrum disorder (FASD) is an umbrella term for children’s conditions due to their mother having consumed alcohol during pregnancy. These conditions can be mild to severe, affecting the subject’s quality of life. An earlier diagnosis of FASD is crucial for an improved quality of life of children by allowing a better inclusion in the educational system. New trends in computer-based diagnosis to detect FASD include using Machine Learning (ML) tools to detect this syndrome. However, most of these studies rely on children’s images that can be invasive and costly. Therefore, this paper presents a study that focuses on evaluating an ANN to classify children with FASD using non-invasive and more accessible data. This data used comes from a battery of tests obtained from children, including psychometric, saccade eye movement, and diffusion tensor imaging (DTI). We study the different configurations of ANN with dense layers being the psychometric data that correctly perform the best with 75% of the outcome. The other models include a feature layer, and we used it to predict FASD using every test individually. Model obtained obtained an accuracy of 88.46% (psychometric, 74.07% (Antisaccadic), 72.24% (Prosaccadic), 88% (Memory guide saccade), and 75% (DTI). These results suggest that the ANN approach is a competitive and efficient methodology to detect FASD. These results are an improvement on Zhang’s 2019 model, which used the same data with less accuracy level.

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Smart Sensors for Healthcare and Medical Applications

2021

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A Mechatronic Platform for Computer Aided Detection of Nodules in Anatomopathological Analyses via Stiffness and Ultrasound Measurements

Cited by 4 publications

References 37 publications

An Improved Strategy for Detection and Localization of Nodules in Liver Tissues by a 16 MHz Needle Ultrasonic Probe Mounted on a Robotic Platform

An Improved Strategy for Detection and Localization of Nodules in Liver Tissues by a 16 MHz Needle Ultrasonic Probe Mounted on a Robotic Platform

Using Artificial Neural Network to Detect Fetal Alcohol Spectrum Disorder in Children

Smart Sensors for Healthcare and Medical Applications

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