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[1] Mango, V.L., Sun, M., R. T., & Ha, R. Should We Ignore, Follow or Biopsy? Impact of Artificial Intelligence Decision Support on Breast Ultrasound Lesion Assessment. American Journal of Roentgenology, 214(6), 1445-1452 (2020).
[2] Barinov, L., Jairaj, A., Becker, M., Seymour, S., Lee, E., Schram, A., …&Paster, L. Impact of data presentation on physician performance utilizing artificial intelligence-based computer-aided diagnosis and decision support systems. Journal of digital imaging, 32(3), 408-416. (2019).
Breast cancer diagnosis using AI in ultrasound
Determination of the probability of malignancy of breast nodules and classification of tumors aligned to BI-RADS.
KOIOS MEDICAL has developed an ultrasound breast detection system that analyzes over 17,900 unique features per image. It simultaneously executes algorithms trained by more than a million of images related to pathology results, and accurately assessing the likelihood of having a breast malignant tumor. It also provides a classification of the tumors aligned to BI-RADS.
Applications and diseases
This tool is useful for helping radiologists to interpret a breast echography and speeding up the decision-making process. It facilitates and improves radiologists’ assessment when studying a breast exam. Hence, it allows clinicians to early diagnose breast cancer for better patient outcomes and to eliminate unnecessary treatment.
Detectable pathologies
Advantages
Higher productivity with less stress. It reduces workload for ultrasound reading.
Higher accuracy and confidence. It can reduce false positive and detect more cancers.
Better outcomes for the patient It allows reducing benign biopsies up to 55%, having a lower number of undetected cancers and allowing the patient to access to treatment earlier, thus reducing costs.
References
[1] Mango, V.L., Sun, M., R. T., & Ha, R. Should We Ignore, Follow or Biopsy? Impact of Artificial Intelligence Decision Support on Breast Ultrasound Lesion Assessment. American Journal of Roentgenology, 214(6), 1445-1452 (2020).
[2] Barinov, L., Jairaj, A., Becker, M., Seymour, S., Lee, E., Schram, A., …&Paster, L. Impact of data presentation on physician performance utilizing artificial intelligence-based computer-aided diagnosis and decision support systems. Journal of digital imaging, 32(3), 408-416. (2019).
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