But this process of worklist cherry-picking can create major workflow problems at the end of the day, when all that’s left is a list of complex, time-consuming cancer reads. In this post, we’ll take a closer look at the problem of worklist cherry-picking in radiology practices – and explain how �Թ�����ѳԹϺ� can help lighten your radiologist workload.

Why Do Radiologists Put Off Cancer Reads?

In our work with radiologists, we consistently hear about avoiding cancer reads. As a result, these MRI and CT studies are rarely a priority on anyone’s worklist. Here are a few reasons why:

They’re time-consuming. While the average chest or abdomen CT read can be completed in 10 minutes or less, complex cancer scans take twice as long. To avoid these time-sucking tasks, radiologists tend to leave them on the worklist – in hopes someone else will take it first.

They don’t pay well. Although cancer reads may take twice as long, they don’t pay twice as much. When a time-intensive cancer read is compensated at the same rate as a simple scan, there’s little incentive to complete it.

They impact productivity. A radiologist’s productivity is measured in relative value units (RVUs). For most radiologists, this metric is directly tied to productivity bonuses. But since cancer reads aren’t worth more RVUs than other scans, completing them can actually drive down productivity metrics.

They’re complicated. Cancer reads require a specialized level of input and expertise. If a general radiologist doesn’t complete these types of reads on a daily basis, that can also make them an intimidating task.

They’re not urgent. When cancer reads appear on the worklist, a radiologist knows the job is not typically related to an emergent need. Yet another reason to put off the job they didn’t want to do anyway.

The Problem With Uncleared Worklists

As we all know, putting off work only delays the inevitable. And avoiding cancer reads doesn’t change your radiologist workload: These reads eventually need to be completed.

In most radiology practices, a worklist must be cleared before the end of a shift. But when you put off the hardest jobs for last, you’re left with a stack of complex cancer reads. This creates even more problems.

Radiologists end up working longer hours to clear the worklist, forcing them to stay after hours or log-in from home to finish the job. And relationships with customers or image centers can be put at risk when reads take longer than expected. 

The stress of it all leaves radiologists constantly feeling like there’s too much work, and not enough time to complete it. Not only does a heavy radiologist workload impact job satisfaction – but it’s also a contributing factor to widespread radiologist burnout.

Stop Worklist Cherry Picking With �Թ�����ѳԹϺ�

At �Թ�����ѳԹϺ�, we’ve developed a solution to the problem of worklist cherry-picking. How? By creating a powerful new tool that leverages artificial intelligence to cut cancer read times in half.

When a radiologist uses �Թ�����ѳԹϺ�, he or she benefits from guided, structured workflows designed by leading radiologists and researchers with a deep knowledge of best practices for advanced cancer reads. The system automatically measures each tumor target, applies all the prior labeling information, and calculates the percentage changes. In addition to drastically reducing read times, this AI guidance can train radiologists on best practices – helping them navigate even the most complex reads with confidence. 

Not only does our software cut cancer read times in half, but a multi-institutional study validated 25% more accurate reads and a 58% improvement in inter-observer agreement. And to top it off, �Թ�����ѳԹϺ� saves time on reporting – by seamlessly and automatically generating patient reports with no effort required by the radiologist.

By adding �Թ�����ѳԹϺ� to your radiologists’ toolbox, you can change the way they approach cancer reads – and finally put an end to worklist cherry-picking.

Cut Cancer Read Times in Half

To learn how you can improve the efficiency and accuracy of cancer reads, today.

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However, there’s no exaggeration behind our claims of dramatically reducing read times for cancer patient evaluations. To show �Թ�����ѳԹϺ� in action, our founder Dr. Andrew Smith recently demonstrated how a radiologist can complete a follow-up cancer read in under two minutes.

Watch his below and follow along as we provide a step-by-step overview of how easy it is to use our �Թ�����ѳԹϺ� software.

Step One: Activate Co-Pilot

To start a cancer patient evaluation, the first step is activating our �Թ�����ѳԹϺ� Co-Pilot. With a single click, this AI-guided system provides a rapid review of the patient’s prior and preliminary CT or MRI reads – automatically taking the radiologist to each tumor being tracked in succession.

Step Two: Measure Tumors

As each successive tumor target is displayed, the radiologist confirms that the right slice has been chosen with a single click. At that point, the system automatically measures the target, applies all the prior labeling information, and calculates the percentage changes. 

Clicking the “next” button repeats this process for each target in the exam, so the radiologist never has to take his or her eyes off the patient.

Step Three: Track Incidental Findings

For lesions that are being tracked but not measured, the radiologist can quickly make an assessment by comparing the image to prior exams. This process is done in the same fashion, using a single click to note the lesion as either absent, present, or unequivocally progressed in the current exam.

Additional review for incidental findings can be assessed by the radiologist using the �Թ�����ѳԹϺ� guided workflow. Where necessary, areas of potential concern can be identified with a single click to require follow up at the next exam, ensuring the finding is evaluated.

Step Four: Generate Patient Report

While the radiologist is stepping through this process, the �Թ�����ѳԹϺ� system is working in the background to simultaneously create a clear, accurate, visualized report. This report automatically generates graphs, tables, images, and text – enabling oncologists and patients to see a clear, accurate, and concise overview of the patient’s current condition.

Cancer Analysis Made Easy

With �Թ�����ѳԹϺ�, it really is that easy for a radiologist to complete an advanced cancer patient reading. Not only does our software cut cancer read times in half, but a multi-institutional study shows it also results in 25% more accurate reads and a 58% improvement in inter-observer agreement. To learn more about how �Թ�����ѳԹϺ� can help you improve the efficiency and accuracy of cancer reads, contact a member of our team.

The post How To Complete a Cancer Read in Under Two Minutes appeared first on �Թ�����ѳԹϺ�.

While AI was initially utilized in academic settings for research, the realization that AI solutions can automate and/or standardize specific complex image interpretation tasks in high-value workflows has driven acceptance in clinical settings. For example, within the past few years, AI in CT imaging has gained considerable acceptance.

The number of pathologies identified by AI-based applications is increasing across radiology, especially in CT imaging. Cyril Di Grandi, cofounder and CEO of Avicenna. AI, says the market reached a turning point in 2022 with greater consolidation of data, driven by manufacturers’ increasing willingness to propose complete and coherent offerings.

“The year was also marked by a large number of studies demonstrating the eco- nomic and clinical justification of the use of AI in CT imaging,” he says.

Steve Worrell, CEO of Riverain Technologies, says AI solutions that allow physicians to put more focus on critical aspects to achieve better outcomes in less time—such as lesion detection and characterization, automated measurements, and segmentations on CT images—are in high demand. He believes acceptance will become commonplace once reimbursement for AI use becomes available.

“We are on the cusp of a transition from the early adopter to early majority phase,” Worrell says. “That AI solutions have begun to become mainstream is remarkable. There is a wide variety of solutions available for clinical use, with more innovation coming fast and furious.”

Elad Walach, cofounder and CEO of Aidoc, has seen extensive engagement and discussion around AI and its ability to address real-world challenges for medical facilities. He says radiologists are leading the way in these discussions because they understand the potential efficiencies that AI can bring to radiology practices, enhancing their ability to treat patients.

“The idea that radiologists are in the driver’s seat for hospital-wide AI adoption is something we observed again in our conversations at RSNA 2022 this year,” he says.

What’s Available

Bob Jacobus, CEO of �Թ�����ѳԹϺ�, says decision-makers in radiology have reached their limit on “AI hype” and are increasingly demanding both a justification for purchasing and a clear use case that solves a radiologist’s pain point. For this reason, he believes that the bar is being raised, and many of the 2015–2021 vintage companies won’t survive much longer without a clear value proposition for the radiologist.

“We see the trend towards product utility and away from AI as a panacea or even a product, for that matter,” Jacobus says. “In our opinion, AI is not a product at all, but is instead a technique, and a very useful tool used as a means to an end. … We’re solving pain points such as radiologist shortages, burnout, callbacks, and highly variable reporting.”

Aidoc offers radiology software for its AI operating system, aiOS, which integrates AI algorithms into customizable clinical workflows, assisting physicians across service lines with delivering care and communicating follow-up actions for their patients. In Q4 2022, Aidoc increased its FDA clearances to 12 with the addition of two CT-based AI solutions—one for aortic dissection and another for all large and medium vessel occlusions.

“The data tsunami—along with a staffing drought—are exacerbating a healthcare crisis,” Walach says. “This is where AI can provide support in the healthcare environment today. We derive insights from complex data, starting with diagnostic imaging, to aid health care teams in optimizing patient treatment, which results both in improved clinical outcomes and economic value.”

In �Թ�����ѳԹϺ�’ case, the “end” it is seeking is an interactive software system that enables radiologists to evaluate cancer patients faster, with greater accuracy and better communication to oncologists. “Advance cancer reads are a currently low- or no-margin work activity for radiology,” Jacobus says. “We transform this critical, complex task into a high-margin, high-value part of the radiologists’ day. �Թ�����ѳԹϺ� shortens the read time by half and reduces the read complexity. The result is ‘un–cherry picking,’ and it doesn’t require any worklist orchestration at all.”

Di Grandi believes radiology AI will only continue to thrive if the solutions are funded and deliver clinical impact, coupled with an economic benefit to the hospital and payers. Additionally, he says products must help address the challenges associated with the increasing volume of associated examinations.

“We aim to cover two distinct but related areas—emergency imaging and rapid detection of serious or life-threatening pathologies,” Di Grandi says. “Our applications help reduce errors and speed up patient management. We are also developing a new range of AI applications for incidental pathology discovery. In this case, the AI application scans prior exams that were not initially intended to detect the pathology, allowing medical teams to route the patient to the best care management pathway.”

Riverain’s approach has been to focus on the clinical impact of AI for radiologists, keeping in mind that any solution that aids the radiologist must not add to the complexity of the interpretation process. “Additional interfaces, widgets, clicks—they all increase the time spent reading the study,” Worrell says. “Our solutions offer the ability for radiologists to be certain of their findings search. We call that capability Clear Visual Intelligence. Our patented vessel suppression technology gives the radiologist an unobstructed view of the thorax within their existing workflow. This enables radiologists to see past obstructions to detect cardio-thoracic diseases correctly and quickly with Certainty of Search.”

Lung cancer screening programs are a notable example of where vessel suppression technology can improve outcomes. “The chest [lung and heart] is one of the most challenging anatomical regions to read due to a multitude of diseases and reading complexity, leading to a high exam reading burden for the radiologist,” Worrell says. “Missed lung cancer is the second most frequent cause of malpractice, with risks increasing along with radiologists’ workload. ClearRead CT gives radiologists an unobstructed view within the existing workflow so they can focus on what matters, to detect, precisely characterize, and report findings.”

Safety Net

AI and deep learning can reproduce complex tasks almost perfectly. For certain tasks, Di Grandi says, this technology allows the creation of algorithms capable of comparable performances to those of a human expert. Thus, these tasks can be automated and executed in parallel with existing workflows to increase the capacity of existing radiology teams.

“The management of neurological emergencies is one the most compelling use cases for AI,” he says. “For a patient with a stroke, time is the most important variable in their chance of survival without significant health consequences. Therefore, a completely automated tool that alerts all the clinical teams in charge of a patient’s care in a few seconds will clearly improve multidisciplinary communication and optimize outcomes.”

Aidoc technology analyzes medical imaging to provide comprehensive solutions for flagging acute abnormalities across the body, helping radiologists prioritize life-threatening cases and expedite patient care. “Our current algorithms include aortic dissection, intracranial hemorrhage, vessel occlusion, pulmonary embolism, and cervical-spine fractures, as well as incidental pulmonary embolisms, free-air, and rib fractures,” Walach says. “This technology helps doctors identify potential points of concern so they can offer more timely treatment and care to patients.”

The company’s AI layer in CT imaging acts as a safety net for the physician, Walach adds, constantly analyzing health care data and imaging in the background to help detect critical issues. Once an issue is identified, the system immediately notifies the appropriate physician or care team.

For example, if a patient undergoes a routine chest exam to follow up on an existing condition or disease, approximately 3% of those patients will have an incidental pulmonary embolism, which could be life-threatening. However, incidental findings are frequently handled too late or missed altogether. With Aidoc’s always-on AI, the technology immediately detects a suspected pulmonary embolism and notifies it as a priority for the radiology team to review. Care teams are activated more quickly to determine diagnosis and urgent treatment can be provided.

Increasing Deployment

According to the ACR, “clinical deployment of AI is still in its early stages.” An ACR Data Science Institute AI Survey published in the Journal of the American College of Radiology in 2021 notes that only 30% of radiologists use AI clinically in current practice, which indicates that much needs to be done to facilitate wide-scale acceptance and adoption of AI.

“A significant challenge for AI is trust,” Worrell says. “Clinicians must have high confidence in any tool that aids their workflow. Scalability—the selected AI solution must have the ability to operate at necessary size, speed, and complexity—is also required. An AI solution that cannot handle the volume of imaging it is expected to process will only frustrate the end user.”

Walach says hospital leaders’ decision overload associated with enterprise-wide software investments often presents a challenge for wide-scale AI adoption. Radiologists can help lead the way in this respect as they are in a unique position to steer hospital teams on AI strategy by pro- viding insight into return on investment (ROI), increased collaboration, and enterprise technology that ensures patients receive appropriate treatment.

“Another challenge is that facilities cannot simply manage 100 single-point solutions, and this is a frequent obstacle to wider adoption,” he says. “Additionally, there is still some initial skepticism from medical practitioners who don’t have firsthand experience working with the power of AI. AI should be looked at as a tool that empowers physicians and a technology that will help health systems rise above some of today’s challenges to operate at an even higher standard.”

Commercial deployment of Avicenna. AI’s applications grew significantly in 2022, with more than 140 hospitals now equipped with its software in 14 countries worldwide. In a market of tens of thousands of potential customers, however, Di Grandi says AI deployment is just beginning.

“The market is still predominantly driven by early adopters who are investing time and resources to evaluate the impact of AI in clinical practice,” he says. “Going forward, determining the true ROI of AI solutions and how they are financed will be the key to large-scale deployment.”

AI is playing a significant role in getting health care to the point where it is delivering data-driven, scientifically driven, precision medicine. AI is fundamental to achieving these goals due to the complexity of the data and the impracticality of addressing the problem outside AI.

Di Grandi believes the future of this technology will be an augmented radiologist/clinician who will use AI in an integrated way in their practice to optimize their time and performance. “Some routine exams will not even be reviewed by physicians anymore, and even some ‘abnormal’ exams will initially be prepared by AI to allow the physician to optimize their time and maximize their added value on complex cases,” he says.

According to Walach, the application of AI is the biggest paradigm shift in radiology technology since PACS was introduced half a century ago. “Just as PACS revolutionized the field of radiology, AI is improving workflows and collaboration while reducing waiting times, helping radiologists make better diagnoses and improving patient care,” he says. “While some view health care AI as the future of tech, the future is already here. AI simply enables new levels of optimization in their work, allowing them to perform more efficiently and safely.”

By Keith Loria

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When we founded �Թ�����ѳԹϺ�, our guiding philosophy was simple: To help radiologists move twice as fast through the evaluation of advanced cancer patients. Now, after years of intense development, our AI-enabled radiology software is doing just that – consistently delivering cancer imaging reads in half the time.

In this post, we’ll share how �Թ�����ѳԹϺ� is revolutionizing cancer patient evaluation by using augmented intelligence to improve the efficiency and accuracy of cancer reads.

Current Radiology Tools are Slow and Inefficient

For radiologists, the process of completing a single cancer imaging read can take 20 minutes or more. It’s undoubtedly a complex, time-intensive process. But in our experience, these long read times don’t just reflect the difficulty of cancer analysis and reporting. They also highlight the inefficiencies that are inherent in a radiologist’s current tools and processes.

Radiologists are some of the most tech-savvy physicians in healthcare. But for some reason, most still rely on a process that hasn’t changed in twenty years.

For example, when completing a cancer read, radiologists often have to physically search the patient history to identify which tumor targets to measure. Then, they have to manually measure and label those tumors – while dictating locations on images for translation into a text report.

Not only does this highly manual process leave results prone to errors, but it’s incredibly tedious and inefficient. From a cost standpoint, cancer reads also represent some of the lowest-margin work that will come across a radiologist’s worklist. Payor reimbursement is just too low to cover the time and complexity of most cancer reads.

�Թ�����ѳԹϺ� Reduces Cancer Read Times for Radiologists

In developing our �Թ�����ѳԹϺ� software, the need for better cancer imaging analysis tools became clear. So we worked with a team of advisors from the fields of radiology and oncology to engineer a better solution.

For radiologists, we leveraged the power of artificial intelligence to simplify and streamline cancer patient evaluation. We created single-click algorithms that automate tumor identification, measurement, and anatomic labeling. Then, we implemented AI-guided workflows to help improve accuracy and consistency – while drastically reducing read times.

The results speak for themselves: Using �Թ�����ѳԹϺ�, radiologists can consistently complete cancer reads nearly twice as fast. When comparing �Թ�����ѳԹϺ� to manual methods, our studies have observed a decrease in baseline read times from 18.7 minutes to only 9.8 minutes. For follow-ups, the results are even more remarkable.

Visual Reports Deliver More Value for Oncologists

Helping radiologists save time and improve reporting accuracy was always a top priority. But during the development process, we also wanted �Թ�����ѳԹϺ� to deliver value for oncologists.

Our visualized reports reflect this focus on meeting the needs of oncologists, too. When a radiologist’s cancer imaging analysis is complete, �Թ�����ѳԹϺ� automatically generates a clear, highly standardized report. Each report is data rich with actual images embedded, yet simple to understand – even for patients

When reviewing our reports, oncologists tell us that they’ve never received anything like this for advanced cancer patients. It’s a new development for them – to have clear, accurate, concise information about their patients – which is why 100% of oncologists prefer �Թ�����ѳԹϺ� reports over standard text-based reports. 

In our research, we’ve also validated that �Թ�����ѳԹϺ� reports can lead to a 58% improvement in inter-observer agreement among oncologists. Finally, with its simple presentation of key aspects of patient progress over time, the �Թ�����ѳԹϺ� report is extremely helpful for patients to access, too.

Experience the Future of Cancer Imaging Analysis

�Թ�����ѳԹϺ� is 100% focused on cancer patient evaluation. Our goal is to deliver superior tools to radiologists and superior information to oncologists – leading to superior patient care. If you’re interested in learning more about our cancer imaging analysis tools, schedule an �Թ�����ѳԹϺ� software demo today.

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Cancer is the second leading killer of Americans, and that means that nearly every single reader of this article will be (or have a family member) impacted by cancer. This year, nearly 1.9 million people will be diagnosed with cancer in the U.S.,¹ a figure that has been growing consistently over the past decade.

Early detection and cancer screening efforts are driving an uptick in cancer cases reported, simultaneously offsetting progress in reducing new cases with efforts across the spectrum of technology, from public health initiatives to advances in genetic profiling. Oncologists order roughly 12 million² CT, MRI, and PET scans annually and depend on their colleagues in radiology to provide accurate, insightful, and timely patient information from which to base care decisions and communicate effectively to patients.

Despite the exponential growth of research to broaden the information sources available (i.e., genetic profiling and response monitoring, cancer-related biomarkers), the radiology report continues to be the ground truth of patient condition and therapy response for cancer. These reports are most often dictated into speech-recognition software, overly populated with extra wording, have a tendency towards subjective language, and must be carefully checked by the radiologist for inadvertent transcription errors.

During a memorable conversation from my early days in this field, an experienced oncologist noted, “I don’t even read [the reports]. I just do the reads myself … the radiologists all think they’re Shakespeare.” A jarring admission, and a problem worth solving.

My vote for “what’s next in cancer imaging” is delivering to oncologists a dramatically higher level of advanced, image-based data for quality cancer care. While I’m an informed and interested observer, I’m neither a radiologist nor an oncologist, so this goal may seem mundane when stacked against the proliferation of scientific and technical innovations bombarding radiology today. But I would argue that this goal is better seen as overdue rather than mundane.

This is a simple but significant and achievable goal, complementing the advancements being made by colleagues in genetics, detection and screening, therapeutics, and other advancements in the fight against cancer mortality. By delivering clear, data-rich, objective reports, linking key elements of patient imaging history, and communicating key information clearly and concisely in a more standardized manner, radiologists enhance the investments and advancements being made across the spectrum of cancer research and care.

Specifically, I think our industry needs to deliver more innovation to address three key challenges that radiology faces in delivering the best quality for cancer patient evaluation:

1. Limited access to expertise: Radiologists with oncologic expertise are too few and too concentrated in larger hospitals. Cancer disease assessment requires the use of multiple image modalities, body regions, and timepoints, as well as specific knowledge of a multitude of emerging cancer treatments and unique treatment responses. Currently, with no subspecialty defined specific to oncologic imaging, only seven fellowship programs exist in the U.S. for such training. Hospitals and radiology groups without a high volume of cancer reads typically also lack the resources and expertise needed to provide the highest-quality reads. Only 20% of U.S. counties have radiologists with subspeciality training in any area, so many cancer patients are being served by a general radiologist.
2. Cancer reads are complex: Another challenge to dramatic improvement in this area is that advanced imaging-based data is time-consuming to generate. Advanced data goes beyond just reporting findings and includes calculations and classification of patient response into categories that require knowledge and implementation of guidelines and criteria (e.g. LI-RADS). And, advanced data includes a longitudinal component, which requires time-consuming comparison across time points. From a financial standpoint, the relative value unit (RVU) for an abdominal CT with contrast is the same no matter the indication, so the economics for a complex metastatic cancer follow-up are terrible when compared to the same reimbursement for a quick abdominal pain assessment.
3. Limited communication tools: Advanced imaging-based data is difficult to communicate. Standard reports include narrative text, with measurements mixed in and easily lost. Compared to graphs and tables, text-based reporting is a poor fit for communicating longitudinal data. Adding images further enhances communication, as has been well-documented. But it is seldom employed in practice.

The acceleration of therapeutic- and technology-driven innovation in cancer detection, treatment, and monitoring is of tremendous benefit to patients. Radiologists are a critical link in the chain of progress and have much to offer to ongoing successes. Radiology value is maximized in this chain when it is applied with focused expertise in cancer, and when that expertise is communicated in a highly effective manner using the best reporting tools available. My hope is that this is the focus of what’s next in cancer imaging and what is to come.

By Bob Jacobus, CEO of �Թ�����ѳԹϺ�

References

1. CA Cancer J Clin 2022;72:7-33.
2. (84.5 million CTs, 35.7 million MRIs, 2.2 million PET scans; based on hospital data, we estimate about 10% are for ongoing cancer patients)

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�Թ�����ѳԹϺ� co-founder Dr. Andrew Smith developed an innovative digital biomarker for accurately detecting and staging liver fibrosis and decompensation, termed Liver Surface Nodularity (LSN), and published the research along with company co-founders Paige Severino and Bob Jacobus. Additional co-authors of the paper included industry partners and researchers from Duke University, UAB, The Cleveland Clinic, University of Wisconsin, Mayo Clinic and UC San Diego.

The article, entitled “Multi-institutional Evaluation of the Liver Surface Nodularity Score on CT for Staging Liver Fibrosis and Predicting Liver-Related Events in Patients with Hepatitis C,” was nominated by the AJR editorial board and will be included in the February 2023 issue of the AJR.

�Թ�����ѳԹϺ� commercialized Dr. Smith’s research by developing a software module that enables patient evaluation from standard CT scans. Prior to this innovation, physician options for such liver analysis were limited to invasive liver biopsies or complex evaluations using specially outfitted MRI equipment.

“LSN dramatically reduces the equipment costs for hospitals,” said Bob Jacobus, CEO, �Թ�����ѳԹϺ�. “And, since LSN analysis eliminates the need for patient fasting, this clinical biomarker will reduce patient cancellations and increase equipment utilization. This is just one example of the impact of digital biomarkers on reducing healthcare costs.”

The software is currently in use in hospitals in the U.S. and Europe. To learn more about �Թ�����ѳԹϺ�’ products, please visit / or

�Թ�����ѳԹϺ� �Թ�����ѳԹϺ�

�Թ�����ѳԹϺ� was founded to help radiologists be more productive when evaluating patients with advanced cancers and chronic liver disease. In addition to the company’s LSN software, its advanced cancer analysis system helps radiologists evaluate patients twice as fast, with increased accuracy, decreased variation in care, and clearer reports for oncologists and their patients. The company holds 20 U.S. and international patents, and its technology is currently in use at 10 healthcare institutions globally.

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“�Թ�����ѳԹϺ� expanded its investor base with field experts eager to capitalize on what they see as a breakthrough technology coming to market.”

�Թ�����ѳԹϺ� was launched in 2019 by Bob Jacobus, Paige Severino, and radiologist and researcher Dr. Andrew Smith. The company has been developing software to more accurately assess cancer treatment effectiveness — leveraging augmented intelligence to improve radiology workflows and deliver detailed updates of therapy progress to oncologists and patients.

“We are excited to have a mix of existing and new investors support �Թ�����ѳԹϺ�, including several radiologists who are attracted to our promising and innovative technology,” said Bob Jacobus, CEO. “�Թ�����ѳԹϺ� expanded its investor base with field experts eager to capitalize on what they see as a breakthrough technology coming to market.”

�Թ�����ѳԹϺ� plans to invest in R&D resources to continue its quest to help radiologists deliver improved speed, accuracy, consistency, and clarity of patient condition.

To learn more about �Թ�����ѳԹϺ�, please visit, /

�Թ�����ѳԹϺ� �Թ�����ѳԹϺ�

�Թ�����ѳԹϺ� was founded by a radiologist to help radiologists raise their standard of care in evaluating cancer patients. Specifically, the company’s software solution uses AI to transform the low margin, difficult, tedious task of cancer patient evaluation into a high margin, high quality, time-saving deliverable to oncologists and patients, which in the process improves care and raises the satisfaction level of patients and their physicians.
Contacts

Media:
Erin Farrell Talbot
Farrell Talbot Consulting, Inc.
917-232-9309
erin@farrelltalbot.com

The post �Թ�����ѳԹϺ� Raises $1.26 MM to Accelerate Innovation and R&D for its AI-enabled Cancer Imaging Technology appeared first on �Թ�����ѳԹϺ�.

�Թ�����ѳԹϺ�, launched in 2019 by Jacobus, Paige Severino, and radiologist and researcher Dr. Smith is developing software to more accurately assess cancer treatment effectiveness. The founders set out to leverage augmented intelligence in innovative imaging software solutions, improve radiology workflows, quantitative metrics, and reporting in clinical practice and clinical trials.

Dr. Smith, our initial CEO, will take on the newly created role of Chief Medical Officer while continuing his role as Chairman of the Board. Dr. Smith is currently a Radiologist and Oncologic Imager at the University of Alabama at Birmingham. He is Vice Chair of Clinical Research, Chief of Body CT, and is also the Clinical Director of AI at the University’s Heersink Institute of Biomedical Innovation.

“I am excited to take on the role of Chief Medical Officer at �Թ�����ѳԹϺ�, where I will be able to focus on the clinical aspects, the necessary research and new and innovative advancements needed in radiology and cancer treatment evaluation,” said Dr. Smith. “�Թ�����ѳԹϺ� was created to utilize advanced technologies to help standardize reporting with greater inter-observer agreement, while decreasing variations in impressions – making it more useful and easier for oncologists to interpret data and information for their patients.”

Jacobus was the company’s initial COO and has more than 20 years of experience in founding, investing in and raising capital for startups. He is an experienced financial professional, with significant industry experience in investment banking, early stage ventures, financial management, new business development, engineering, operations, and manufacturing. Jacobus also has deep experience in the biotech, medical products manufacturing, software, transportation and insurance sectors.

“I am honored to lead this innovative company that is delivering solutions to radiologists who work on important issues such as saving lives and working with advanced cancers,” said Jacobus. “My role as CEO is to lead �Թ�����ѳԹϺ� as it helps solve real radiology pain points using AI technologies to improve accuracy, consistency, and efficiency in radiology.”

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While some of the hype is warranted, some isn’t. And many radiologists are left wondering, “Can AI improve radiology image analysis?” We might be a little biased, but for us, it’s a resounding “yes.”

Benefits of AI for radiology image analysis

Radiologists’ skills and know-how are irreplaceable. The best AI tools recognize this and work to augment your efforts, not replace them. It does this in two ways: faster reads and improved accuracy.

These two benefits eliminate the need for radiologists and oncologists to duplicate their efforts. No one needs to dictate notes or create reports. The AI can handle that, providing standardization and peace of mind for your team.

Faster reads

Faster reads generate higher output. You can complete more reads—and help more patients—each day. But there’s another reason that might be even more beneficial.

If you spend less time reading images, you can play a more effective diagnostic role, incorporating data from various sources, not just images. AI offers the ability to deliver more impactful care, so you can make a more significant difference in patients’ lives.

Improve read accuracy

AI can’t replace your expertise. Instead, it builds on existing knowledge learned from large data sets of previous scans—in some cases, this includes hundreds of thousands of images. With this knowledge, AI software can interpret a new image and detect tumors faster than standard methods.

It can also help when reviewing multiple scans for a single patient. When opening a new image, AI can retrieve previous images and notes automatically. This makes it fast and easy to pick up where you left off and helps you complete accurate reads quickly.

Additional benefits

Faster, more accurate reads have positive ripple effects that can trickle down throughout many areas of your work. From increased inter-observer agreement to reduced burnout, AI has a lot to offer radiologists.

Eliminate major errors and increase inter-observer agreement

AI reduces errors in radiology image analysis. With learned knowledge from large data sets of previous scans, AI can accurately identify, measure, and report on detected tumors. 

It also provides standardization. Without that, reads can vary, and inter-observer agreement dips to an inefficient level. Oncologists wind up repeating the same effort made by radiologists, taking up more time and reducing overall productivity.

Reduce burnout

A showed that only 25% of radiologists felt happy. And 44% experienced some degree of burnout. 

AI allows radiologists to work more efficiently, so burnout becomes less of an issue. Your team can perform at the top of their licensure and enjoy the job satisfaction they need for long-term well-being.

Faster report turnaround times

With data embedded into workflows, it’s more easily extracted to make report development and delivery a quick and easy process. You don’t have to go searching for information. The software can handle that for you.

Smooth integrations

Importantly, AI can assist in integrating patient and image data into other tools like PACS, RIS, and EHRs. This allows for a more holistic diagnosis process and more customized treatment plans.

Collectively, AI is becoming an important tool in the quest to develop multi-faceted patient data for better care and more successful outcomes. 

Support for non-diagnostic tasks

AI also supports a variety of non-diagnostic tasks to improve operations for radiology image analysis. These tasks can include order entry support, patient scheduling, resource allocation, and improved workflows. 

Yes, AI can improve radiology image analysis

The benefits of AI in radiology are seemingly endless. And as technology develops further, new ways of helping radiologists will develop. Thankfully, some of those cutting-edge capabilities are already available today.

Founded by radiologist Dr. Andrew Smith, MD, PhD, �Թ�����ѳԹϺ� is on a mission to leverage artificial intelligence to help radiologists perform at their highest levels. We want to augment your abilities through improved workflows, quantitative metrics, and automated reporting. 

�Թ�����ѳԹϺ� workflows are twice as fast as traditional manual image evaluation and dictated text reports. Our solutions allow you to keep your eyes on the images so you can focus on high-level tasks, increase productivity, and reduce burnout.

Contact �Թ�����ѳԹϺ� today to schedule a demo.

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�Թ�����ѳԹϺ� the study

During the study, providers compared body CT images from 120 consecutive patients with multiple serial imaging exams and advanced cancer treated with systemic therapy using current-practice methods and AI-Assisted methods. Current practice methods included dictated text-based reports and separately categorized responses (CR, PR, SD, and PD). The AI-Metrics Platform used custom AI algorithms for tumor measurement, target and non-target location labeling, and tumor localization at follow up. The AI-assisted software was able to automatically categorize tumor response per RECIST 1.1 calculations and displayed longitudinal data in the form of a graph, table, and key images. The studies were read independently in triplicate for assessment of inter-observer agreement according to the metrics: major errors, time of image interpretation, and inter-observer agreement for final response category.

What they found

�Թ�����ѳԹϺ� increased reporting accuracy by 25%, reduced errors by 99%, cut interpretation time in half, increased inter-observer agreement among oncologists by 58% and among radiologists by 45%, and was preferred by 96% of radiologists and 100% of oncologists compared to current practice with manual image assessments and text reports. In a (called eMASS; included guided workflows and annotation tools, but no AI algorithms), eMASS reduced errors and time of evaluation was twice as fast, which indicated better overall effectiveness than standard of care, manual tumor response evaluation methods for three different therapy response criteria.

The Verdict

AI-assisted advanced cancer longitudinal imaging response evaluation significantly reduced major errors, was nearly twice as fast, and increased inter-observer agreement relative to the current-practice method. The decisive advantages of the �Թ�����ѳԹϺ� Platform establishes a new and improved standard of care in oncology and radiology.

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