Techsomed Launches Ultrasound-Based Software Suite for Liver Tumor Ablation Therapy
Featuring the previously FDA-cleared BioTraceIO Vision and BioTraceIO Precision modalities, the BioTrace software suite combines real-time ultrasound guidance and advanced AI technology to help bolster outcomes with liver tumor ablation therapy.
For patients with liver tumors, the emergence of a new software suite may enhance the accuracy and efficiency of ultrasound-guided treatment.
The recently launched BioTrace software suite includes BioTraceIO Vision and BioTraceIO Precision, two modalities that were previously cleared by the Food and Drug Administration (FDA) for use in liver tumor ablation, according to Techsomed, the manufacturer of the modalities.
For interventional radiologists, BioTraceIO Vision may enhance pre-op planning of liver tumor ablation procedures and BioTraceIO Precision provides assessments for post-ablation tissue damage based on real-time ultrasound images, according to Techsomed, the developer of both software modalities. (Image courtesy of Techsomed.)
For the assessment and treatment planning of patients with liver tumors, TechsoMed said interventional radiologists may utilize the artificial intelligence (AI)-powered BioTraceIO Vision for targeted ablation therapy based on ultrasound guidance. In order to access predictive insights on post-ablation tissue damage, clinicians may employ BioTraceIO Precision to get assessments based on real-time ultrasound imaging, according to Techsomed.
“We are thrilled to bring our BioTrace software suite, including both BioTraceIO Vision and BioTraceIO Precision, for patient care in the United States. … We look forward to partnering with leading medical institutions to revolutionize the standard of care in ablation therapy, offering patients the benefits of enhanced treatment precision aimed to improve outcomes,” noted Yossi Abu, the CEO of Techsomed.
Recent Videos
New Mammography Studies Assess Image-Based AI Risk Models and Breast Arterial Calcification Detection
Can Deep Learning Provide a CT-Less Alternative for Attenuation Compensation with SPECT MPI?
Employing AI in Detecting Subdural Hematomas on Head CTs: An Interview with Jeremy Heit, MD, PhD
Pertinent Insights into the Imaging of Patients with Marfan Syndrome
How Will the New FDA Guidance Affect AI Software in Radiology?: An Interview with Nina Kottler, MD, Part 2
How Will the New FDA Guidance Affect AI Software in Radiology?: An Interview with Nina Kottler, MD, Part 1
Teleradiology and Breast Imaging: Keys to Facilitating Personalized Service, Efficiency and Equity
Radiology Study Finds Increasing Rates of Non-Physician Practitioner Image Interpretation in Office Settings
Addressing the Early Impact of National Breast Density Notification for Mammography Reports
Where the USPSTF Breast Cancer Screening Recommendations Fall Short: An Interview with Stacy Smith-Foley, MD
Related Content
New bpMRI Study Suggests AI Offers Comparable Results to Radiologists for PCa Detection
April 15th 2025Article
Demonstrating no significant difference with radiologist detection of clinically significant prostate cancer (csPCa), a biparametric MRI-based AI model provided an 88.4 percent sensitivity rate in a recent study.
Can CT-Based AI Radiomics Enhance Prediction of Recurrence-Free Survival for Non-Metastatic ccRCC?
April 14th 2025Article
In comparison to a model based on clinicopathological risk factors, a CT radiomics-based machine learning model offered greater than a 10 percent higher AUC for predicting five-year recurrence-free survival in patients with non-metastatic clear cell renal cell carcinoma (ccRCC).
The Reading Room: Artificial Intelligence: What RSNA 2020 Offered, and What 2021 Could Bring
December 5th 2020Podcast
Nina Kottler, M.D., chief medical officer of AI at Radiology Partners, discusses, during RSNA 2020, what new developments the annual meeting provided about these technologies, sessions to access, and what to expect in the coming year.
Study Suggests AI Software May Offer Standalone Value for X-Ray Detection of Pediatric Fractures
April 9th 2025Article
Artificial intelligence (AI) software demonstrated a 92 percent sensitivity for detecting fractures in a study involving over 1,600 X-rays from a tertiary pediatric emergency department.
