Michael Barbella, Managing Editor06.06.23
See-Mode Technologies received regulatory approvals earlier this year in Australia and New Zealand for its artificial intelligence (AI)-powered software that automatically analyzes and reports breast and thyroid ultrasound scans. Such AI-backed innovations are expected to drive growth of Australia's ultrasound systems market, according to GlobalData.
Ultrasound imaging is one of the most extensively used medical imaging techniques in Australia and globally. GlobalData’s research indicates the ultrasound systems market comprised about 14.8% of Australia's diagnostic imaging market last year. GlobalData’s report, “Innovation in Healthcare: Ultrasound Imaging System,” provides detailed information regarding emerging disruptive ultrasound imaging system innovations.
Automation of ultrasound workflow provides significant benefits such as increasing consistency in image analysis and avoiding human errors in image interpretation. AI-backed See-Mode's application helps to automate workflow, using advanced computational modeling techniques and deep learning to analyze medical images. It will help clinicians interpret and analyze clinical images, thereby improving workflow efficiency.
“The integration of AI with ultrasound technologies will not only aid radiologists but also reduce turnaround time. Additionally, AI-backed analysis is expected to improvise diagnostic accuracy in the detection of breast and thyroid tissue abnormalities,” GlobalData Medical Devices Analyst Dr. Satyajeet Salunkhe said.
For breast and thyroid tissues, See-Mode provides a classification of lesions in clinical images as per BI-RADS and TI-RADS rating systems, which help in risk assessment and stratification. See-Mode helps provide confidence to clinicians and may reduce unwanted thyroid biopsies. For breast tissue imaging, See-Mode can help clinicians identify potential lesions that may remain undetected in routine examinations. Additionally, the technology automates the identification of other vascular pathologies such as occlusion, refluxes, and stenosis.
“See-Mode represents a unique example of the implementation of AI to automate complex medical imaging analysis in routine clinical practice. Such AI-backed solutions will not only reduce errors and provide accurate analysis of ultrasound images but also bring reliability in reporting and avoid instances of misclassification of ultrasound images,” Salunkhe concluded.
Ultrasound imaging is one of the most extensively used medical imaging techniques in Australia and globally. GlobalData’s research indicates the ultrasound systems market comprised about 14.8% of Australia's diagnostic imaging market last year. GlobalData’s report, “Innovation in Healthcare: Ultrasound Imaging System,” provides detailed information regarding emerging disruptive ultrasound imaging system innovations.
Automation of ultrasound workflow provides significant benefits such as increasing consistency in image analysis and avoiding human errors in image interpretation. AI-backed See-Mode's application helps to automate workflow, using advanced computational modeling techniques and deep learning to analyze medical images. It will help clinicians interpret and analyze clinical images, thereby improving workflow efficiency.
“The integration of AI with ultrasound technologies will not only aid radiologists but also reduce turnaround time. Additionally, AI-backed analysis is expected to improvise diagnostic accuracy in the detection of breast and thyroid tissue abnormalities,” GlobalData Medical Devices Analyst Dr. Satyajeet Salunkhe said.
For breast and thyroid tissues, See-Mode provides a classification of lesions in clinical images as per BI-RADS and TI-RADS rating systems, which help in risk assessment and stratification. See-Mode helps provide confidence to clinicians and may reduce unwanted thyroid biopsies. For breast tissue imaging, See-Mode can help clinicians identify potential lesions that may remain undetected in routine examinations. Additionally, the technology automates the identification of other vascular pathologies such as occlusion, refluxes, and stenosis.
“See-Mode represents a unique example of the implementation of AI to automate complex medical imaging analysis in routine clinical practice. Such AI-backed solutions will not only reduce errors and provide accurate analysis of ultrasound images but also bring reliability in reporting and avoid instances of misclassification of ultrasound images,” Salunkhe concluded.