BU-CAD

by TaiHao Medical  · Based in Taiwan → — Advanced AI Medical Imaging Solutions
General Surgery Oncology Radiology

Regulatory Status Disclosed

Overview

BU-CAD by TaiHao Medical is an AI-powered software designed to assist trained interpreting physicians in analyzing breast ultrasound images. It is intended for patients with soft tissue breast lesions suspicious for breast cancer who are referred for further diagnostic ultrasound examination.

This tool is primarily for radiologists, breast surgeons, and other specialists who interpret breast ultrasound images. It can be integrated into clinical workflows by loading DICOM breast ultrasound and mammography images from local storage or a Picture Archiving and Communication System (PACS). BU-CAD provides automated identification of regions of interest (ROIs) for suspicious soft tissue lesions in up to two orthogonal views and generates lesion contours. It also offers region-based analysis, providing a score of lesion characteristics (SLC) related to malignancy or benignity, BI-RADS categories, and BI-RADS descriptors (shape, orientation, margin, echo pattern, and posterior features).

Notable capabilities include:

  • Efficient Screening: Quickly analyzes breast ultrasound images to identify suspicious lesions and provides relevant information for diagnosis.
  • Diagnostic Assistance: Aids in detecting potential cancer lesions and assessing their malignancy.
  • BI-RADS Classification: Utilizes the American College of Radiology BI-RADS classification system.
  • Rapid Reporting System: Facilitates the generation of professional diagnostic reports, which can be saved locally, uploaded to PACS, or exported to third-party reporting software.
  • Multi-language Reporting: Includes built-in templates for generating reports in multiple languages.

Reviewed by Pouyan Golshani, MD — Interventional Radiologist

Key Features

  • Automatic loading of breast ultrasound DICOM images
  • Computer-aided analysis of suspicious lesions
  • Diagnosis of BI-RADS classification
  • Generation of professional diagnostic reports
  • Multiple language reporting templates
  • Identification of regions of interest (ROIs) and lesion contours
  • Region-based analysis of lesion malignancy
  • Classification of lesion shape, orientation, margin, echo pattern, and posterior features according to BI-RADS descriptors
  • Image viewer for multi-modality digital images (ultrasound and mammography)
  • Tools to adjust, measure, and document images

Use Cases

  • Assisting physicians in breast cancer detection and diagnosis
  • Improving efficiency of image reading for radiologists, breast surgeons, and other specialists
  • Reducing workload for medical personnel
  • Generating standardized and digitized reports
  • Screening for breast lesions
  • Supporting diagnostic ultrasound examinations

What Physicians Need to Know

Key Capabilities
BU-CAD is the first USFDA-approved (K210670) computer-aided diagnostic software for detecting potential cancer lesions in breast ultrasound images. It offers efficient screening by quickly analyzing suspicious lesions and diagnosing their BI-RADS classification, a rapid report system with multi-language templates, and automatic loading of DICOM images. The tool identifies regions of interest (ROIs), lesion contours, provides region-based malignancy analysis, and automatically classifies BI-RADS descriptors (shape, orientation, margin, echo pattern, posterior features). It also functions as a multi-modality image viewer with tools for image adjustment, measurement, and structured report generation.
Clinical Utility
This AI tool assists trained interpreting physicians in analyzing breast ultrasound images for soft tissue lesions suspicious for cancer, aiding in localization and characterization of abnormalities. It significantly improves the efficiency of image reading and report generation, reducing interpretation time by approximately 40%. BU-CAD enhances physicians' determination of BI-RADS descriptors and is suitable for radiologists, breast surgeons, and other specialists, helping to reduce missed detection rates.
Integration Options
BU-CAD automatically loads breast ultrasound DICOM images and supports exporting CAD results to third-party reporting software. Its viewer can load ultrasound and mammography images from local storage or Picture Archiving and Communication Systems (PACS). TaiHao Medical offers professional system integration services, and the product is designed to integrate effectively into most hospital PACS workstations.
Compliance Status
BU-CAD holds USFDA 510(K) approval (K210670), making it the first USFDA-approved CAD software for breast ultrasound. It also has approvals from TFDA (MOHW-MD No. 007651), Thai FDA, MoH, and MDA.
Pricing Model
TaiHao Medical offers customized AI billing programs, indicating a flexible pricing structure tailored to the needs of medical institutions. Specific pricing details are not publicly disclosed.
User Experience
The software is designed for efficient screening and rapid report generation, featuring multi-language reporting templates. It allows users to adjust, measure, and document images, with user-modifiable regions of interest (ROIs) and analysis results. The aim is to reduce physician workload and fatigue associated with high volumes of image interpretation.
Support Quality
TaiHao Medical provides dedicated Product & Technical Support, with contact information available via email ([email protected]). They emphasize a professional AI team capable of providing expert system integration and support.
Implementation Complexity
BU-CAD is designed to be adaptable to the specific needs of various hospital departments. TaiHao Medical offers professional system integration services to assist with implementation, and the automatic loading of DICOM images simplifies the initial data input process.
Evidence Base
FDA 510(K) clearance is supported by a reader study demonstrating a significant decrease in interpretation times (~40%) and improved determination of BI-RADS descriptors by physicians using BU-CAD. The standalone performance reported an AUC_LROC of 0.8203. The AI model is trained using pathology-verified cases and clinically tested to enhance lesion detection and diagnostic sensitivity and specificity.
Physician Tip

BU-CAD serves as a valuable decision support tool, not a replacement for clinical judgment. Leverage its automated BI-RADS classification and lesion analysis to streamline your workflow and reduce interpretation time, especially for high-volume screenings. Always review the AI-generated ROIs and analysis results, as they are user-modifiable, and integrate your clinical expertise for final diagnostic decisions. The multi-language reporting can be particularly useful in diverse clinical settings. Remember that patient management decisions should not be made solely based on BU-CAD analysis.

BU-CAD's ability to automatically load DICOM images and integrate with PACS workstations is a significant advantage for seamless workflow incorporation. Explore its compatibility with your existing reporting software and consider TaiHao Medical's professional system integration services for optimal setup and to maximize its utility within your hospital's infrastructure. The support for multi-modality image viewing (ultrasound and mammography) further enhances its integration potential.

Details

Category Oncology AI, Radiology & Imaging AI
Pricing Unknown
DeploymentSoftware application, likely workstation-based on a standalone Windows-based computer.
Compliance
BAA AvailableUnknown AI-estimated
HIPAA CompliantUnknown AI-estimated
FDA Status 1 AI-estimated

BU-CAD received US FDA 510(k) clearance (K210670) on December 21, 2021. It is indicated to assist trained interpreting physicians in analyzing breast ultrasound images of patients with soft tissue breast lesions suspicious for breast cancer who are being referred for further diagnostic ultrasound examination.

Integrations
EHR Not specified
Specialties General Surgery, Oncology, Radiology

What the Web Says

BU-CAD by TaiHao Medical is an FDA-cleared, AI-driven computer-aided decision support system designed to assist physicians in analyzing 2D and 3D breast ultrasound images. Clinical studies and medical reviews highlight its ability to improve diagnostic specificity and significantly reduce image reading times. While highly regarded in clinical research for its automated BI-RADS categorization, it lacks a presence on mainstream software review platforms like G2, Capterra, or Reddit.

Overall: Positive

Strengths

  • Significantly reduces mean image interpretation times for physicians
  • Improves diagnostic specificity without compromising sensitivity
  • Automatically segments lesions and generates precise contours
  • Provides automated BI-RADS categorization and lexicon descriptors
  • Supports both 2D and 3D breast ultrasound imaging
  • Facilitates rapid, standardized reporting and PACS integration

Limitations

  • Virtually no user reviews or discussions on G2, Capterra, or Reddit
  • Potential risk of user over-reliance on AI-generated marks
  • Performance can be influenced by the quality of the initial ultrasound scan

Based on reviews from: ClinicalTrials.gov, Diagnostic Imaging, Applied Radiology, PLOS Digital Health, PMC (PubMed Central)

Last updated: 2026-07-20

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Press & Coverage

Signify Research
Taihao Biomedical expands in Southeast Asia and enters GE HealthCare's ABUS Ecosystem
Taihao Biomedical has expanded its presence in Southeast Asia by securing regulatory approvals for its breast ultrasound AI solution, BU-CAD, in Thailand, Malaysia, and Vietnam. The company has also partnered with GE HealthCare, integrating BU-CAD with GE HealthCare's Invenia Automated Breast Ultrasound System (ABUS).
2025-08
Applied Radiology
The Current Scope of Artificial Intelligence in Breast US | Applied Radiology
TaiHao Medical Inc.'s BU-CAD system analyzes 2D and 3D breast ultrasound images, evaluating lesion characteristics and generating automated BI-RADS categorizations. A study showed it increased specificity by 13.6% without compromising sensitivity.
2025-04
PMC
Clinical Application of Artificial Intelligence in Breast Ultrasound
BU-CAD, an FDA-approved AI-CADe/x system by TaiHao Medical Inc., assists in breast lesion detection by providing bounding box outlines and generating a lesion characteristic score corresponding to BI-RADS categories. A study showed it significantly increased diagnostic performance and reduced interpretation time.
2025-03
AJR (American Journal of Roentgenology)
Artificial Intelligence for Breast Ultrasound: AJR Expert Panel Narrative Review
BU-CAD (TaiHao Medical) is an FDA-cleared, vendor-agnostic application that provides a score of lesion characteristics for user- or software-identified lesions, along with BI-RADS descriptors. A reader study demonstrated an improvement in mean AUC with BU-CAD use.
2024-12
TaiHao Medical Inc.
TaiHao Takes Southeast Asia by Storm: Expanding into Malaysia and Vietnam!
TaiHao Medical Inc. announced its expansion into Malaysia and Vietnam, indicating a growing presence in the Southeast Asian market for its advanced AI medical solutions.
2024-08
Diagnostic Imaging
AI and Breast Ultrasound: Where Things Stand | Diagnostic Imaging
BU-CAD software by TaiHao Medical improved AUC by seven percent over unassisted reading and reduced interpretation time from 30 to 18 seconds in a study involving 16 physicians. The software correctly diagnosed breast lesions in 163 out of 172 cases.
2024-02
PMC
Theranostics and artificial intelligence: new frontiers in personalized medicine
BU-CAD, developed by TaiHao Medical Inc., is an FDA-approved AI software for the detection of suspicious breast cancer lesions, receiving its clearance in 2021.
2024-03
Ferrum Health
Ferrum Health Partners with TaiHao Medical to Advance Women's...
Ferrum Health has partnered with TaiHao Medical to integrate BU-CADu2122 into its Enterprise AI Platform, providing radiologists with an additional tool for diagnostic ultrasound exams.
2022-08

Videos

Product demos, reviews, and walkthroughs for BU-CAD.

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Frequently Asked Questions

BU-CAD software typically loads breast ultrasound DICOM images automatically, quickly analyzes suspicious lesions, and provides BI-RADS classifications and other relevant diagnostic information. It acts as a decision support tool, assisting physicians in detecting and characterizing breast lesions during interpretation.
Many AI tools for breast ultrasound, including some CAD systems, have received FDA approval or clearance for lesion classification and/or detection. While these systems are intended as decision support, the physician remains ultimately responsible for the diagnosis and patient management.
Traditional CAD primarily detects structures, often with high false-positive rates, while AI-powered BU-CAD not only detects but also characterizes findings, aiming to reduce false positives and improve diagnostic accuracy. Other AI tools exist for breast imaging, including those for mammography and multimodal data analysis.
Pricing models for healthcare AI solutions like BU-CAD can vary, often involving subscription fees, per-study charges, or capital expenditure for on-premise solutions. The return on investment can be realized through improved diagnostic efficiency, reduced unnecessary biopsies, and potentially enhanced patient outcomes.
While AI-CAD systems can improve diagnostic accuracy and reduce reading times, especially for less experienced radiologists, they are currently intended as assistive tools, not replacements for human interpretation. Limitations can include the lack of large and diverse datasets for training, and challenges in transparency and interpretability of AI-based decisions.
BU-CAD systems, especially cloud-based solutions, must comply with stringent data privacy regulations such as HIPAA. Data handling typically involves de-identification and secure transmission protocols to protect sensitive patient information.
Effective use of AI-CAD tools often requires appropriate training for radiologists to integrate the system into their workflow and understand its outputs and limitations. Implementation typically involves integration with existing PACS/RIS systems, which can range from lightweight cloud deployments to more complex on-premise installations.

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Suggest an Edit → | Last Verified: 2026-04-19 | First Added: 2026-04-19
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