Manually reviewing breast images for small abnormalities in a deluge of hundreds of mammographic images can mean long days for radiologists, which can lead to reader fatigue and increase the potential
for missed cancers. False positives from older 2D computer-aided detection (CAD) technologies result in unnecessary biopsies, which are not only stressful for patients, they have been shown to cost the healthcare system more than $2 billion per year.[i]
Artificial intelligence (AI) presents new opportunities to improve radiologists’ performance and enhance patient care. Used in diagnostic technologies, AI can increase radiologists’ diagnostic accuracy, save time and improve patient satisfaction. However, not all AI vendors are created equally. Before investing in AI technology to enhance your diagnostic capabilities, the following 10 factors are important to consider.
1. Clinical Value
The AI product should offer significant clinical value. It should:
- Improve radiologist sensitivity and increase the numbers of cancers radiologists detect;
- Improve specificity, making the reading more accurate; and
- Reduce recall rates, thereby reducing the burden of unnecessary supplemental imaging or biopsies, and minimize reading time for radiologists.
ProFound AI is a high-performing workflow solution available for 2D and 3D mammography, or digital breast tomosynthesis (DBT), featuring the latest in deep-learning AI. This leading-edge
technology was the first software of its kind to be FDA-cleared; it is also CE marked and Health Canada licensed. It is clinically proven to offer an 8 percent increase in sensitivity, a 6.9 percent increase in specificity, a 7.2 percent reduction in the rate of false positives and unnecessary recalls, and a 52.7 percent reduction in reading time.[ii]
2. Algorithm and Data Quality
The AI vendor should offer a true deep-learning algorithm and provide evidence about the quality and quantity of images used. The credibility of the images and the credentials of the team members, such as
image annotators and chief investigators, who validated the images are critical. A trusted vendor will explain the types of images obtained, the clinical expert protocol design used, whether clinical biomarkers or biopsies were used to validate the annotated images, and if different image sets were used for training and testing the algorithm.
ProFound AI uses a true deep-learning algorithm. iCAD employs biostatisticians and chief investigators from leading academic medical centers and private clinics to help design clinical protocols detailing
optimal population size, heterogeneity of population and gantry manufacturers. We follow strict FDA guidelines for developing a medical product that requires separation of training and regulatory-validation data sets, and we employ expert truthers (radiologists) to provide ground truth. Our ground truthing for cancers is always done with biopsy-proven pathology data by trained radiologists and clinical experts.
3. Workflow Integration
When choosing an AI solution, it is critical to determine whether the AI results can integrate easily and seamlessly into your preferred diagnostic viewer. The vendor should explain how quickly its
technology can deliver results, whether this data can integrate into your PACS and primary viewing interfaces, how many PACS vendors its AI product integrates with, and whether there is a dedicated expert resource to manage PACS upgrades.
The vendor should explain if its AI product can export results in major reporting structures, and what happens if you switch to another gantry vendor. End-to-end complete integration into a voice-dictation, PACS or RIS-driven workflow should always be an option, allowing radiologists to always keep an eye on images and not worry about using an additional interface. True deep-learning AI should not be introducing unnecessary technology complexity into readers’ already rich workflow. It should be a true concurrent reader assisting radiologists in the background.
Built on powerful GPU technology and algorithm optimization techniques, ProFound AI is compatible with multiple vendors, PACS, and workstation vendors, and delivers results rapidly. Our expert technical
sales and integration consultants provide on-site service and support customers’ changing needs.
4. Data Collection and Clinical Validation
In deep-learning AI, data are gold. The quality of data with which the technology is trained determines the quality of its clinical outcomes. A reliable AI vendor will have an internal dedicated team of experts
for data collection, curation and annotation, and software updates should be seamless. It is important for the vendor to explain the technology’s process for collecting, curating and annotating data, as well as its indications for use (independent reader or concurrent reader), and how to swiftly re-train its algorithms and introduce updated software versions. Ask the vendor if the technology is supported by clinical research (including whether it is a prospective, retrospective or true reader study), the number of readers that participated in the study, and whether the study was single-center or multi-center. Furthermore, the most important metric is the quality of the outcomes and claims, not merely the number of images on
which the algorithm is trained.
With ProFound AI, our customers have access to internal experts dedicated to data collection agreements, the secure transfer of data, anonymizing, ingesting data and sending it for multiple-expert annotation. It is supported by a multi-center, global (US and Europe), retrospective reader study––with HIPAA-compliant IRB protocol for data collection––that evaluated the concurrent use of AI to shorten DBT reading time, while maintaining or improving radiologist sensitivity and specificity. Our reader study was conducted with 24 certified MQSA radiologists (13 expert breast imagers and 11 general radiologists, with 34 years or less experience in clinical practice).[ii]
5. Product Regulation
The AI vendor should explain which US and non-US regulatory approvals it has, if any. For FDA approval, find out the type of clearance the vendor received.
In December 2018, ProFound AI for DBT became the first artificial intelligence software for DBT to be cleared by the U.S. Food and Drug Administration; it was also CE marked and Health Canada licensed
that same year. ProFound AI for 2D Mammography was CE marked in July 2019.
6. IT Development and Support
The AI solution must be cost-effective, secure and work quickly. Ask the vendor to explain its IT consultancy and whether its AI maximizes hardware resources for optimal performance and cost. A vendor should also explain the extent and duration of its global support as well as who will provide technical support. Having support within the same time zone is an important consideration for critical issues involving product and field integrations.
iCAD’s expert team of integration specialists helps to optimize IT workflow and provides a tailored solution using a variety of modern-architecture deployments, which minimizes the hardware footprint and
reduces demand on internet bandwidth.
7. Operational Efficiency
The AI vendor should explain how to prioritize high-complexity patients to provide immediate supplemental screening or biopsy for some women, and how to distribute workload across multiple readers to ensure a uniquely tailored workload for each radiologist (i.e. specialist versus general
radiologist).
ProFound AI provides Certainty of Finding lesion and Case Scores, which helps radiologists assess caseloads and assists with clinical decision-making. By using ProFound AI’s Case Scores, you can easily distribute workload from your workflow list in PACS or your reading station. Vendors such as GE and ThreePalm have implemented this successfully today for the assessment of cases, regardless of complexity.
8. Purchasing and Contracting
An AI vendor should outline what purchasing models it offers––perceptual Cap-EX or operational Op-Ex licenses. The vendor should work with you to find the best solution for your financial needs especially in times when funds are tight, and prioritization of financial resources is needed. iCAD offers a flexible suite of financing models that can help facilities afford our solution and help to ensure a sound return on their
investment.
9. ROI, Value Proposition and Product Vision
When choosing an AI vendor for 2D and 3D mammography, knowing the product’s financial impact is crucial. Ask the vendor to explain how your patients and your facility will benefit financially, and
what its near-mid-term product vision is. Select a vendor whose vision aligns with your longer-term growth goals. The AI solution should increase the accuracy of diagnosis, lower time of reads and total cost of patient spend over time. Vendors should satisfy a quadruple aim: demonstrate improved clinical experience, better outcomes, improved patient experience and lower costs. Furthermore, the vendor should always have a comprehensive near- and long-term product roadmap that is aligned with customer’s goals.
ProFound AI is clinically proven to cut reading time by more than half,[ii] which frees up work time to spend with patients, or perform an additional biopsy or additional reads.
10. Capital Funding and Leadership
Being able to raise capital through multiple funding rounds reflects a vendor’s potential long-term success. The AI vendor should explain how much capital funding it has raised; the number of funding rounds, how long the typical funding round lasts and when the most recent round was completed; and disclose who its investors are. The vendor leadership team’s credibility and experience and its medical advisory board are integral to the vendor’s success. Ask the vendor about the leadership team’s experience in taking healthcare technology products to market, and whether its executive team has prior similar experience working for successful, revenue-generating companies.
iCAD’s senior executive team offers a legacy of leadership and expertise in women’s imaging and the AI market. Our medical team comprises renowned global experts from leading academic medical centers and hospitals, including the University of Pennsylvania, NYU Langone Health, Elizabeth Wende Breast Care and Boca Raton Regional Hospital. We aim to serve millions of patients and physicians with clinically proven AI tools that save lives and enhance patient care.
[i] Vlahiotis A, Griffin B, Stavros AT, Margolis J. Analysis of utilization patterns and
associated costs of the breast imaging and diagnostic procedures after screening mammography. Clinicoecon Outcomes Res. 2018;10:157-167
https://doi.org/10.2147/CEOR.S150260
[ii] Conant, E. et al. (2019). Improving Accuracy and Efficiency with Concurrent Use of Artificial Intelligence for Digital Breast Tomosynthesis. Radiology: Artificial Intelligence. 1 (4). Accessed via https://pubs.rsna.org/doi/10.1148/ryai.2019180096