{"id":9058,"date":"2025-06-30T07:55:37","date_gmt":"2025-06-30T05:55:37","guid":{"rendered":"https:\/\/cim.eu\/casi-studio\/soundflex\/"},"modified":"2026-01-26T11:57:57","modified_gmt":"2026-01-26T10:57:57","slug":"soundflex","status":"publish","type":"case-study","link":"https:\/\/cim.eu\/en\/case-studies\/soundflex\/","title":{"rendered":"Soundflex"},"content":{"rendered":" \n

Funded by the European Union \u2013 Next Generation EU<\/em><\/p> \n\r\n

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\"\"\/<\/figure> \n<\/div>\r\n<\/div>\r\n","protected":false},"featured_media":9059,"template":"","annualita":[182],"area-di-intervento":[260],"area_tecnologica":[177],"settore":[205],"tecnologia-abilitante":[201],"class_list":["post-9058","case-study","type-case-study","status-publish","has-post-thumbnail","hentry","annualita-23-26","area-di-intervento-product","area_tecnologica-digital-factory","settore-biomedical","tecnologia-abilitante-artificial-intelligence-deep-learning-collaborative-robotics-computer-vision"],"acf":{"proponente":"Prensilia S.r.l.","descrizione_breve":"Automation of quality testing of ultrasound probes through AI and collaborative robotics","logo_finanziatore":"","importo_finanziato":"","contesto":"In the biomedical sector, the quality verification<\/strong> of ultrasound probes is essential to ensure diagnostic accuracy. However, the tests currently in use are often performed manually, resulting in variability, slowness, and poor repeatability. These tests require a strong subjective component from the operator, generating results that are not standardized and difficult to replicate. The need for efficiency and precision<\/strong> has therefore driven the development of automated<\/strong> and more reliable<\/strong> solutions<\/strong>. <\/span>","soluzione":"The Soundflex project introduced an innovative solution based on the combination of collaborative robotics<\/strong> and artificial intelligence<\/strong> to automate the entire process<\/strong> of testing ultrasound probes. After an in-depth analysis of manual operating methods, a system was developed that involves the automatic positioning of the probe through a robotic arm - cobot<\/strong> - and the analysis of ultrasound images through neural networks<\/strong> trained on specific data from different types of probes. In particular, algorithms<\/strong> were created for probe-cone alignment, initially with a classification and regression approach, then optimized into a purely classification version. The image quality test was also automated, using a ResNet18 neural network to compare the acquired images with a reference Phantom. The entire system operates locally, in a Docker environment, ensuring fast response times and data security<\/strong>. <\/span>","impatto":"Thanks to this solution, the testing process<\/strong> has been completely automated<\/strong>, eliminating the need for manual intervention and significantly increasing the repeatability and reliability of the results. The developed neural networks achieved an accuracy of over 99% in image classification, thus improving quality control. Furthermore, Prensilia's ability to independently adapt the algorithms to new types of probes makes the system highly scalable<\/strong> and efficient, with tangible benefits in terms of reducing time and errors<\/strong>. <\/span>","supporto_cim":"CIM played a key role in the realization of the project by supporting Prensilia with advanced technical expertise<\/strong> in the field of AI<\/strong>. This enabled the company to acquire strategic skills for the autonomous management of the developed technologies.\nThe support included: <\/span>\n\n\u00a0\n
    \n \t
  • The design and development of algorithms<\/span><\/li>\n \t
  • The integration between AI and collaborative robotics as well as the validation of technical solutions and performance analysis <\/span><\/li>\n \t
  • A dedicated training program<\/strong><\/span><\/li>\n<\/ul>","gallery":"","video_di_progetto":"","partners":[9366],"tecnologia":"","competenza_in_campo":"Training, design, development, and integration"},"yoast_head":"\nCasi Studio - Soundflex - CIM<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/cim.eu\/en\/case-studies\/soundflex\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Casi Studio - Soundflex - CIM\" \/>\n<meta property=\"og:description\" content=\" Funded by the European Union \u2013 Next Generation EU \" \/>\n<meta property=\"og:url\" content=\"https:\/\/cim.eu\/en\/case-studies\/soundflex\/\" \/>\n<meta property=\"og:site_name\" content=\"CIM\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/cim40\/\" \/>\n<meta property=\"article:modified_time\" content=\"2026-01-26T10:57:57+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/cim.eu\/wp-content\/uploads\/2025\/10\/Progetto-senza-titolo-33-scaled-1.png\" \/>\n\t<meta property=\"og:image:width\" content=\"2560\" \/>\n\t<meta property=\"og:image:height\" content=\"1280\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/cim.eu\\\/en\\\/case-studies\\\/soundflex\\\/\",\"url\":\"https:\\\/\\\/cim.eu\\\/en\\\/case-studies\\\/soundflex\\\/\",\"name\":\"Casi Studio - 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