PowerLook Tomo Detection V2 Software
Overview
PowerLook Tomo Detection V2 Software, developed by iCAD, is a computer-assisted detection and diagnosis (CAD) software device. It is specifically designed to be used concurrently by interpreting physicians while reading digital breast tomosynthesis (DBT) exams from compatible DBT systems. The software utilizes deep learning technology and artificial intelligence to analyze 3D DBT slices, detecting soft tissue densities (such as masses, architectural distortions, and asymmetries) and calcifications.
The system provides detections and ‘Certainty of Finding’ and ‘Case Scores’ to assist radiologists in identifying suspicious breast lesions, which can then be confirmed or dismissed by the interpreting physician. PowerLook Tomo Detection V2 was noted for its ability to optimize DBT reading efficiency, with clinical studies showing an average reduction in reading time by 29.2% without statistically significant impact on sensitivity, specificity, or recall rate. It was formerly branded as ProFound AI for Digital Breast Tomosynthesis 2.0 and is part of iCAD’s broader suite of breast health solutions.
Reviewed by Pouyan Golshani, MD — Interventional Radiologist
Key Features
- AI-powered detection of soft tissue densities (masses, architectural distortions, asymmetries)
- Detection of calcifications in 3D DBT images
- Deep learning technology for feature computation and pattern recognition
- Concurrent-read functionality for interpreting physicians
- Provides 'Certainty of Finding' and 'Case Scores'
- Optimizes digital breast tomosynthesis (DBT) reading efficiency
- Aids in identifying suspicious breast lesions
- Compatible with various DBT systems (e.g., Hologic Selenia Dimensions, GE Pristina)
- Integration in standard reading environment (PACS)
- DICOM Structured Report, GSPS, Secondary Capture output format
Use Cases
- Concurrent reading of digital breast tomosynthesis (DBT) exams
- Assisting radiologists in identifying suspicious breast lesions and calcifications
- Improving radiologist efficiency and workflow in breast cancer screening
- Enhancing diagnostic confidence in breast imaging
- Reducing DBT exam interpretation time
What Physicians Need to Know
PowerLook Tomo Detection V2 (now ProFound AI for DBT) is a concurrent-read CAD solution, meaning it's intended to be used by the interpreting physician while reading DBT exams, not as a standalone diagnostic tool. Leverage the 'Certainty of Finding' and 'Case Scores' as a guide to prioritize suspicious areas, but always integrate with your clinical judgment. The software's ability to reduce reading time can be particularly beneficial for managing high volumes of DBT studies and potentially reducing radiologist fatigue. Note that while it aids in detecting both soft tissue densities and calcifications, its initial design focused on soft tissue densities.
The software is designed for seamless integration into existing breast imaging workflows, supporting common DBT systems (Hologic, GE, Siemens) and adhering to DICOM standards. It can integrate with PACS and provide scores in worklists, but typically requires local deployment on dedicated hardware. Ensure your current imaging infrastructure is compatible and consider the IT resources needed for local deployment and ongoing maintenance.
Details
| Category | Oncology AI, Radiology & Imaging AI |
| Pricing | Contact vendor — Not publicly available; contact iCAD for pricing information. |
| Deployment | Locally on dedicated hardware |
| Compliance | |
| BAA Available | Unknown AI-estimated |
| HIPAA Compliant | Yes AI-estimated |
| FDA Status |
Yes AI-estimated PowerLook Tomo Detection V2 Software received FDA clearance (K182373) on December 6, 2018. It is classified as a Class II medical device under 21 CFR 892.2090, Radiological Computer Assisted Detection/Diagnosis Software for Lesions Suspicious For Cancer. |
| Integrations | |
| EHR | Not specified |
| Specialties | Radiology |
What the Web Says
PowerLook Tomo Detection V2 Software by iCAD Inc. is an AI-based computer-assisted detection (CAD) and diagnosis tool designed to help physicians identify suspicious lesions and calcifications in digital breast tomosynthesis (DBT) images. It utilizes deep learning techniques to analyze 3D DBT slices, providing detection marks and confidence scores (0-100%) to assist radiologists in evaluating findings, improving accuracy, and reducing reading time during breast cancer screening exams. The software is intended to be used concurrently by interpreting physicians while reading DBT exams from compatible systems like Hologic Selenia Dimensions and GE Pristina.
Overall: PositiveStrengths
- Significantly reduces radiologist reading time (average of 29.2%, up to 50%).
- Improves radiologist accuracy and diagnostic performance (AUC) in detecting breast cancer.
- Increases sensitivity in cancer detection, including subtle and small lesions, and those in dense breasts.
- Maintains or improves specificity and can reduce recall rates in non-cancer cases.
- Provides confidence scores for detected lesions, aiding in evaluation.
- Streamlines workflow and enhances the overall reading experience for radiologists.
Limitations
- May increase false-positive rates, potentially leading to unnecessary additional imaging, radiation exposure, biopsy, and patient anxiety.
- The CAD does not detect microcalcifications and may miss some soft tissue densities, requiring a complete review of all images.
- Safety and effectiveness have not been established for non-standard mammographic views or in patients with breast implants for views that include the implant.
- Can be criticized for generating multiple annotations per case, potentially leading to high false-positive rates and radiologists disregarding marks.
- Some AI systems, in general, have been reported to be wrong frequently on various findings.
- Initial versions were only available for specific DBT platforms (e.g., GE Healthcare), though later versions expanded compatibility.
Based on reviews from: iCAD Inc., X-ray Interpreter, PubMed Central (PMC), Imaging Technology News, GlobeNewswire, Medium, Oncodaily, Reddit, G2, Capterra, accessdata.fda.gov
Last updated: 2026-07-20
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