Nov 30,2023

IBM, Boehringer partner on genAI, foundation models for therapeutic antibody research

German pharmaceutical company Boehringer Ingelheim and tech giant IBM are teaming up to harness the power of genAI and foundation models to further biologic drug discovery. The partnership leverages IBM’s foundation model technologies, consisting of pre-trained, large-scale neural network architectures, to accelerate the identification of novel antibodies vital for therapeutic development. These antibodies will be created with properties such as high specificity and affinity to address various diseases, including cancer, autoimmune disorders, infectious diseases and other medical conditions. Boehringer Ingelheim plans to produce and experimentally assess the refined antibody candidates in mini-scales, thereby validating the in-silico methods. The feedback loop is designed to enhance the efficiency and accuracy of antibody discovery techniques.

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Nov 27,2023

AI-Powered TIL Analysis by Lunit Unveils Prognostic Insights for Colon Cancer - published in npj Precision Oncology

Lunit published a new study in npj Precision Oncology, using Lunit SCOPE IO to predict prognosis in stage II–III colon cancer via AI-powered spatial analysis of tumor-infiltrating lymphocytes (TIL). This study showed that Lunit SCOPE IO and the power of AI for spatial TIL analysis can provide clinicians with a practical and efficient tool to enhance prognosis prediction. The study utilized Lunit SCOPE IO, Lunit's AI-powered hematoxylin and eosin (H&E) whole-slide image (WSI) analyzer, to evaluate intratumoral TIL (iTIL) and tumor-related stromal TIL (sTIL) densities from WSIs of 289 patients with stage II–III colon cancer treated with surgery and adjuvant therapy. Patients with confirmed recurrences showed significantly lower sTIL densities compared to those with no recurrence. The study also identified distinct risk groups: patients with the lowest iTIL and sTIL were grouped as high-risk, patients with higher sTIL than the median as low-risk, and intermediate-risk group. These groups were predictive of recurrence-free survival time and were independently associated with clinical outcomes after adjusting for other clinical factors. This result suggests that AI-powered TIL analysis can provide practical and reliable prognostic information for stage II–III colon cancer.

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Nov 30,2023

Dedalus and Ibex Launch an End-to-End AI-powered Digital Pathology Solution, Streamlining Cancer Diagnosis Across Europe

Dedalus Group, the leading anatomic pathology information systems provider in Europe, and Ibex Medical Analytics, the market leader in artificial intelligence (AI)-powered cancer diagnostics, announced the launch of a fully integrated End-to-End AI-powered Digital Pathology Solution for cancer diagnosis. The new solution is now commercially available in major European markets and sets a new standard for anatomic pathology lab digitization by seamlessly integrating Ibex's Galen™ platform into Dedalus' comprehensive Digital Pathology (DP) Solution. Galen's AI-powered findings are available on the joint solution, that supports pathologists in the diagnosis of prostate, breast and gastric biopsies with improved accuracy, AI-powered decision support tools, streamlined reporting, improved laboratory efficiency, and increased diagnostic confidence.

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Dec 01,2023 TOP STORY

Lunit to Advance Singapore’s Healthcare Landscape with AI-powered Chest Screening Solution Integrated into National AI Platform

Lunit, , a leading provider of AI-powered solutions for cancer diagnostics and therapeutics, has joined forces with NTT DATA Singapore to introduce Lunit INSIGHT CXR to Singapore’s public healthcare system. Following the agreement, Lunit successfully delivered Lunit INSIGHT CXR to NTT DATA, which, in turn, facilitated the delivery to Synapxe, Singapore’s national health-tech agency. Utilizing AimSG, Singapore’s national AI platform for medical imaging, Synapxe integrated Lunit INSIGHT CXR into the clinical radiology workflow of two leading public hospitals: Singapore General Hospital (SGH) and Changi General Hospital (CGH), each with over 1,000 beds. The AimSG platform is a collaboration amongst Synapxe, SingHealth, Lunit, NTT DATA, and its partners (DeepTek and CARPL), with support from Singapore’s Ministry of Health (MoH) and Smart Nation Singapore. AimSG facilitates seamless integration of AI imaging solutions, like Lunit INSIGHT CXR, into existing clinical radiology workflows.

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Nov 27,2023 TOP STORY

Paige and Nuance Collaborate to Establish the Largest Consultation Network in Pathology

At the Radiological Society of North America's (RSNA) 109th Scientific Assembly and Annual Meeting, Paige, a leader in end-to-end digital pathology solutions and clinical AI applications that assist in diagnosing cancer, announced it has teamed up with Nuance, a Microsoft company, to create the largest digital consultation network in pathology. This secure platform will revolutionize collaboration among U.S. pathologists, making consultations faster and more efficient. The platform leverages the scale and capabilities of Nuance’s PowerShare image sharing network together with Paige’s expertise in digital pathology and its leading FDA approved viewer. By merging Paige's cutting-edge digital pathology tools, like FullFocus® and FullFolio™, with PowerShare, Nuance's nationwide image sharing network, connecting over 14,000 sites for seamless medical image sharing, laboratories with any type of digital capability gain frictionless access to top-tier hospitals and healthcare systems for expert consultations, enhancing the diagnostic process.

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Nov 20,2023

New Study Backs that Lunit's AI Model Transforms Chest Radiograph Reporting, Enhancing Screening Efficiency and Safety - to be presented at RSNA 2023

Lunit is set to unveil eight groundbreaking abstracts at the Radiological Society of North America (RSNA) 2023 Annual Meeting in Chicago from November 26 to November 30. Three will be presented orally, and the remaining five will be featured as ePosters.In an oral presentation, Lunit explores the effectiveness of a newly developed normal filtering (NF) AI model in autonomously reporting normal chest radiographs. Lunit also investigates mammographic parenchymal patterns and longitudinal changes to develop an AI model predicting future breast cancer risk.

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Nov 28,2023 TOP STORY

Personalis and Tempus Enter into a Strategic Collaboration to Advance Cancer Testing

Personalis, a leader in advanced genomics for precision oncology, and Tempus announced a strategic collaboration to co-commercialize NeXT Personal® Dx, Personalis’ whole genome-based liquid biopsy laboratory developed test (LDT) for detection of molecular residual disease (MRD) and recurrence in cancer. NeXT Personal Dx is a leap forward in tumor-informed approaches, setting the new standard in performance of MRD tests with unprecedented sensitivity and high specificity. The test was launched by Personalis in October of this year. As a part of the deal, Tempus will:Provide up to $12 million to Personalis as certain milestones are met, to help fund clinical evidence development for NeXT Personal Dx; Integrate NeXT Personal Dx into its testing menu; and provide access to oncologists through its sales and marketing efforts

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Nov 22,2023

Lunit's AI-Powered Mammography Analysis Solution Proves Comparable to Radiologists in Breast Cancer Detection - published in European Radiology

Lunit (KRX:328130.KQ), a leading provider of AI-powered solutions for cancer diagnostics and therapeutics, today announced that in a study recently published in European Radiology, Lunit's AI-powered mammography analysis solution, Lunit INSIGHT MMG, has been validated for its effectiveness in standalone breast cancer detection. The research encompassed mammography screenings performed between August 4, 2014, and August 15, 2018, in the Region of Southern Denmark. It involved 249,402 screenings (149,495 women), and a total of 2,033 breast cancers. Screenings were assessed as normal or abnormal by breast radiologists through double-reading with arbitration. The study's key findings revealed that when Lunit INSIGHT MMG's cut-off score was matched at the first reader mean specificity (capacity of correctly identifying cancer-free exams), it exhibited no statistically significant difference in overall accuracy compared to that of radiologists. However, when the AI threshold was matched at the first reader mean sensitivity (capacity of correctly diagnosing cancer), it showed lower specificity (97.5% vs. 97.7%) and positive predictive value (17.5% vs. 18.7%) and a higher recall rate (3.0% vs. 2.8%) than first readers. The study findings suggest that with an appropriate cut-off score, Lunit INSIGHT MMG could feasibly replace first readers in a mammography double-reading setting. The cancers detected by AI but missed by radiologists suggest that integrating AI to support double-reading within screening could lead to an increase in the overall number of detected cancers.

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Nov 24,2023 TOP STORY

AI identifies Non-Smokers at High Risk for Lung Cancer

The United States Preventive Services Task Force (USPSTF) currently recommends lung cancer screening with low-dose CT for adults between the ages of 50 and 80 who have at least a 20 pack-year smoking history and currently smoke or have quit within the past 15 years. One reason federal guidelines exclude never-smokers from screening recommendations is because it is difficult to predict lung cancer risk in this population. For the study, Cardiovascular Imaging Research Center (CIRC) researchers set out to improve lung cancer risk prediction in never-smokers by testing whether a deep learning model could identify never-smokers at high risk for lung cancer, based on their chest X-rays from the electronic medical record. The "CXR-Lung-Risk" model was developed using 147,497 chest X-rays of 40,643 asymptomatic smokers and never-smokers from the Prostate, Lung, Colorectal, and Ovarian (PLCO) cancer screening trial to predict lung-related mortality risk, based on a single chest X-ray image as input. Of 17,407 patients (mean age 63 years) included in the study, 28% were deemed high risk by the deep learning model, and 2.9% of these patients later had a diagnosis of lung cancer.

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Nov 21,2023 TOP STORY

Huma obtains multi-condition Saudi FDA Class C regulatory certification for its configurable SaMD disease management platform

Huma Therapeutics announced today that it has received Saudi FDA Class C certification for a disease-agnostic Software as a Medical Device (SaMD) in Saudi Arabia. Huma's SaMD platform powers digital health pathways through which data are collected from patients for self-management or to be assessed centrally by clinicians. These include remote patient monitoring (RPM) programmes, companion apps, risk assessment tools, dose calculators, algorithms. With the recent attainment of Saudi FDA Class C certification, complementing its existing Class IIb regulatory certification in the EU and Class II 510(k) in the USA, the SaMD platform reaches a new pinnacle in patient care versatility. This achievement enables the platform to monitor patients across all age groups, including infants and those pregnant, and seamlessly integrates with various external devices such as heart rate and blood sugar monitors, and smart inhalers. Beyond facilitating disease self-management for patients, the platform is equipped to aid clinicians in managing critical and serious conditions, including heart failure, COPD, asthma, cancer, rare diseases, pre-/post-op monitoring and more.

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