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    Dr. Fouad Kettani’s Experience Using DeepDx Prostate: A Testimonial

    We are thrilled to share the insights of Dr. Fouad Kettani, who recently tested our DeepDx Prostate solution. Dr. Kettani highlighted several positive aspects of our product, particularly emphasizing its ease of use and the depth of AI-generated results.
    He noted that DeepDx Prostate significantly reduces subjectivity and inter-observer variability, marking it as a valuable tool in prostate cancer diagnostics.

    Don’t miss his full testimonial to understand how DeepDx Prostate can enhance diagnostic accuracy and improve patient outcomes.

    Deep Bio Unveils Research Findings on Performance Evaluation of AI Algorithm for Breast Cancer Analysis

    Deep Bio utilizes deep learning-based algorithms to differentiate breast cancer lesions accurately, optimizing diagnostic accuracy

    SEOUL, SOUTH KOREA, May 21, 2024

    Deep Bio today announced the publication of a study evaluating the performance of its AI algorithm for breast cancer analysis to accurately differentiate between invasive ductal carcinoma (IDC) lesions from non-invasive ductal carcinoma in situ (DCIS) lesions in biopsies taken after breast cancer surgery.

    The research, conducted in collaboration with the Catholic University of Korea Bucheon St. Mary’s Hospital and Korea University Guro Hospital, has been published in a special issue of MDPI Bioengineering titled “Computational Pathology and Artificial Intelligence.”

    Breast cancer is the most prevalent cancer among women worldwide, accounting for 24.5% of all female cancers, with a mortality rate of 15.5%, the highest among women’s cancers. Pathologically, breast cancer is categorized into various types, with invasive ductal carcinoma (IDC) being the most common, constituting 70-80% of all cases. Accurate differentiation between IDC and ductal carcinoma in situ (DCIS) is critical for effective treatment planning. The “2022 Korean Breast Cancer White Paper” published by the Korean Breast Cancer Society, reported 24,933 IDC and 4,816 DCIS cases in South Korea in 2019, underscoring the need for precise diagnostic tools. Predicting the characteristics, size, and severity of lesions in invasive ductal carcinoma (IDC) and non-invasive ductal carcinoma (DCIS) accurately through this study indicates a significant advancement.

    Traditional pathology involves examining cancer cells’ growth patterns and histological characteristics using a microscope. However, mixed patterns of IDC and DCIS within the same lesion can complicate manual assessment and prognosis prediction. This is where AI-powered algorithms can revolutionize diagnostics.

    Specifically, Deep Bio’s Multi-resolution Selective Segmentation Model for Breast Cancer (MurSS) addresses these challenges by analyzing hematoxylin and eosin (H&E) stained breast cancer pathology slide images to segment breast cancer lesions automatically. This model enhances diagnostic accuracy by leveraging multi-resolution images and introduces a selective segmentation method to automatically exclude uncertain areas from learning, thereby increasing the stability and reliability of model results.

    MurSS achieved a pixel-level accuracy of 96.88% (95% confidence interval 95.67% to 97.61%) on breast cancer H&E slides, outperforming existing deep learning models.

    CTO Tae-Yeong Kwak of Deep Bio said, “Using the Multi-resolution Selective Segmentation Model (MurSS), we can more accurately measure cancer areas on whole slide image of breast tissues, aiding in the measurement of invasive cancer area by excluding in situ carcinomas,” he added. “I hope our AI algorithm for breast cancer enables pathologists to identify precise cancer lesions by improving predictions with automated cancer marker analysis.”

    Deep Bio continues to innovate with AI solutions for breast cancer analysis. DeepDx® Breast – Resection is a screening solution that automatically detects areas of interest in breast excision slide images, while DeepDx® Breast – SLNB (Sentinel Lymph Node Biopsy) has maintained its top performance since securing first place in the Camelyon17 Challenge in 2019, a global image analysis competition focused on breast cancer lymph node metastasis detection.

    About Deep Bio

    Deep Bio is an AI healthcare company dedicated to advancing the field of cancer pathology. Focusing on deep learning, the company develops cutting-edge In Vitro Diagnostic Software as Medical Devices (IVD SaMDs) to empower pathologists and medical professionals with state-of-the-art tools for more accurate cancer diagnosis and prognosis.

    For more information, visit the website: www.deepbio.co.kr.

    [BioSpectrum Asia] AI will be integrally involved in not only the diagnosis of cancer but also in determining the prognosis and best therapeutic option

    South Korea based Deep Bio, a pioneering artificial intelligence (AI) healthcare firm focused on cancer pathology, is making waves in the industry. Their recent involvement in the innovative CancerX initiative, part of the White House Cancer Moonshot programme, marks a pivotal moment. Spearheading advancements in deep learning and cancer pathology, Deep Bio aims to revolutionise cancer diagnosis and prognosis. Sun Woo Kim, CEO, Deep Bio sheds light on their transformative mission, AI-driven healthcare, data privacy, among others.

     

    Link to Article

    What inspired Deep Bio to focus on developing in vitro diagnostic software for cancer pathology?

    With the advancement of AI like deep learning, AI can distinguish images and classify them for the first time, just as the human eyes can. I found that cancer detection and diagnosis were very subjective at that time. Pathologists have difficulty providing the exact tumour measurements using a microscope because human pathologists estimate the tumour area and provide the proportion of cancer information. Moreover, interobserver and intraobserver variability in the grading of tumours can impact therapy selection and patient outcomes. It would be good to apply AI technology for cancer pathology for accuracy and consistency.

     

    How does Deep Bio leverage deep learning in its in vitro diagnostic software for cancer pathology?

    Identifying diverse tissue morphological patterns related to tumour malignancy, differentiation levels, and prognosis in cancer pathology is crucial.

    Deep learning proves highly effective in recognising specific patterns within large datasets, as demonstrated by its surpassing human recognition in the ImageNet challenge.

    Leveraging this capability, Deep Bio is developing in vitro diagnostic software for cancer pathology. This software employs deep learning-based image analysis for various tasks, such as identifying tissues and cancerous lesions, distinguishing between cell nuclei and cell membranes, classifying and grading histologic tumour types, estimating gene mutations, and predicting patients’ prognoses.

    The goal is to enhance the precision and efficiency of cancer diagnostics by harnessing the power of deep learning to analyse intricate tissue patterns and provide valuable insights into various aspects of cancer pathology.

    What are some of the challenges that Deep Bio has faced in developing and implementing its AI healthcare solutions?

    The efficacy of deep learning hinges on access to substantial datasets to achieve high accuracy. However, acquiring such extensive datasets challenges medical data and sensitive personal information.

    This difficulty is compounded by the intricate nature of obtaining significant annotation data from pathology experts for various organs and cancer types. The demand for pathological diagnosis is rising while the number of pathologists is declining, exacerbating the challenge of building comprehensive datasets.

    Complicating matters further is the issue of inter-observer variability, making both data collection and performance evaluation exceptionally challenging. To address these complexities, our diagnostic AI applications undergo rigorous comparisons with pathologists, measuring accuracy and speed.

    The expectation is that these AI applications will match and surpass pathologists in terms of accuracy and speed, meeting user expectations. Achieving this while ensuring cost-effectiveness poses a formidable engineering task, requiring innovative solutions to overcome the hurdles associated with limited data access, inter-observer variability, and the evolving landscape of pathology demands.

    What collaborative efforts does Deep Bio engage in with healthcare providers or institutions to implement and refine its technology solutions?

    Deep Bio’s medical AI solutions are developed based on extensive medical data and expert knowledge. Collaborating with healthcare professionals, particularly pathologists, is indispensable for developing and delivering optimal solutions. We engage closely with domestic and international healthcare institutions and pathologists to ensure compliance with data regulations, construct essential datasets, assess performance, and pinpoint areas for refinement. Our principal collaboration entails partnering with providers of digital pathology platforms to ensure the stable delivery of our solution and perpetually enhance its efficacy through resolving engineering challenges.

     

    How does Deep Bio address concerns regarding data privacy and security when dealing with sensitive patient information?

    Deep Bio implements strict data security measures to prevent unauthorised access to medical data. Our research systems operate on segregated networks, and access to research data is restricted to designated researchers; We hold ISO 27001 certification for our information security management system.

    Deep Bio’s solutions feature robust security measures in line with the South Korean Ministry of Food and Drug Safety (MFDS) cybersecurity checklist. For example, all data communication is encrypted, and unauthorised access attempts are promptly blocked. Our cloud services comply with HIPAA regulations, ensuring secure storage, transmission, and processing of protected health information (PHI).

     

    How does Deep Bio perceive the current and future trends in AI-driven healthcare, particularly in the context of cancer pathology? 

    Deep Bio perceives the current and future trends in AI-driven healthcare, particularly in cancer pathology, amid a notable shift from analog to digital pathology. This transformation involves moving from traditional glass slide reviews to evaluating digitised images captured by high-definition scanners viewed on computer monitors.

    This change allows pathologists to conduct remote reviews, fostering accessibility for patients in underserved areas. Concurrently, it fuels the development of AI-assisted pathology, utilising digitised images to train algorithms that assist pathologists in making more accurate diagnoses, prognoses, and predictions of therapeutic responses.

    Deep Bio anticipates digitising nearly all pathology cases in the envisioned future. These digitised images would play multifaceted roles, serving as pre-screen analyses before pathologist reviews, real-time support during consulting reviews, or post-sign-out quality control (QC) reviews to detect misdiagnoses or discrepancies. This trajectory reflects a broader trend of seamlessly integrating AI into healthcare workflows, enhancing diagnostic precision, efficiency, and accessibility in cancer pathology.

    Are there any upcoming projects or products that you are particularly excited about?

    We believe that AI will be integrally involved in not only the diagnosis of cancer but also in determining the prognosis for the patient and predicting the best therapeutic option – essentially, the realm of precision medicine. In the case of prostate cancer, many men do not require definitive treatment and are candidates for surveillance. AI can be used to identify those cases that may require more definitive therapy versus those that can be safely monitored.  Much of this will be done based on the analysis of morphologic images captured from the hematoxylin and eosin (H&E) stained slides. In addition, the future holds a more comprehensive integration of AI, incorporating additional layers of information such as genetic sequencing data and population or metadata. This holistic approach aims to significantly improve the precision and effectiveness of diagnosis, prognosis, and predictive medicine.

    As for our expansion plans, our DeepDx Prostate product is CE-marked for distribution throughout the European Union. In addition, our DeepDx Prostate product is available through our channel partner’s image management systems (IMS). Many image management vendors are already embedded within hospitals and pathology labs worldwide. So, one mechanism for widespread adoption is to make our algorithms available across multiple platforms. We currently have various customers in the United States that utilise our algorithm as a Laboratory Developed Test (LDT). These laboratories have rigorously validated our algorithm in their laboratories under Clinical Laboratory Improvement Amendments (CLIA).  Over the next several months, we will add channel partners and expand our distributor network worldwide.

     

    Ayesha Siddiqui

    Deep Bio to Participate in USCAP 2024

    Deep Bio participated in

    Deep Bio to Present Research Findings at Poster Sessions during AACR Annual Meeting 2024

    Three posters will be presented at the AACR Annual Meeting 2024 to spotlight Deep Bio’s pioneering research and advancements in AI within digital pathology.

     

     

    SEOUL, SOUTH KOREA, March 28, 2024

    Deep Bio, a frontrunner in AI-powered cancer diagnostics, will unveil three distinct research findings at the poster sessions during the American Association for Cancer Research (AACR) Annual Meeting, scheduled for April 5-10, 2024, in San Diego, CA. These results validate Deep Bio’s advancements in AI-driven pathology, showcasing how novel AI techniques amplify the company’s proficiency across a spectrum of tissue and cancer types

    Sun Woo Kim, CEO of Deep Bio, said, “I am proud to see our research findings being presented at the American Association for Cancer Research (AACR) for the third consecutive year, as we integrate AI into diagnostic pathology to improve pathology workflow and patient care.”

    Innovative Research Highlights at AACR:

    Poster Presentation: Semi-Automated Ki-67 Index Assessment Using Top-k Hotspot Recommendation in Ki-67 IHC Stained WSIs

    1. Session Date & Time: Monday, April 8, 2024, from 9 a.m. to 12:30 p.m.
    2. Abstract #: 2308
    3. Poster #: 19
    4. Poster session: Liquid Biopsy and Precision Oncology
    5. Lead Author: Hyeon Seok Yang
    6. Presenter: Hyeon Seok Yang
    7. Overview: This research uses deep learning and image analysis to evaluate cell analysis and top-K hotspot recommendations. The study was performed on Ki-67 IHC-stained WSIs and compared with the pathologist’s Ki-67 score.

    Poster Presentation: Enhancing Multi-organ Frozen Section Cancer Discrimination Model by Sharing Cancer Discrimination and Organ Classification Task

    1. Session Date & Time: Tuesday, April 9, 2024, from 9 a.m. to 12:30 p.m.
    2. Abstract #: 4916
    3. Poster #: 12
    4. Poster session: Artificial Intelligence and Machine/Deep Learning 3
    5. Lead Author: Joonho Lee
    6. Presenter: Joonyeong Cho
    7. Overview: This research explores the automated analysis of H&E-stained FS WSIs using deep learning to discriminate cancer and classify organs

    Poster Presentation: Morphological Feature Discrepancies in Wild-type vs. BRCA1/BRCA2 Mutated High-grade Serious Ovarian Cancer

    1. Session Date & Time: Tuesday, April 9, 2024, from 9 a.m. to 12:30 p.m.
    2. Abstract #: 4913
    3. Poster #: 9
    4. Poster session: Artificial Intelligence and Machine/Deep Learning 3
    5. Lead Author: JaeHeon Lee
    6. Presenter: Hyunil Kim
    7. Overview: This research illustrates significant morphological discrepancies between WT and BRCA1/BRCA2 mutated HGSOC cells, highlighting the impact of these genetic mutations on cell size, shape, and texture.

    About Deep Bio

    Deep Bio is an AI healthcare company dedicated to advancing the field of cancer pathology. Focusing on deep learning, the company develops cutting-edge In Vitro Diagnostic Software as Medical Devices (IVD SaMDs) to empower pathologists and medical professionals with state-of-the-art tools for more accurate cancer diagnosis and prognosis.

    For more information, visit the website: www.deepbio.co.kr.

    [Posters] A Deep Learning-Based Tumor Area Identification Using a Semi-Supervised Approach for IHC Stained Images in Non-Small Lung Carcinoma

    Yunseob Hwang1), Hyeon Seok Yang1), Yoon-La Choi2), Kyungsoo Jung2), Young Kee Shin3), 4), Ji-Hye Nam3), 4), Jun Young Choi5), Kyung-Eui Park5), Tae-Yeong Kwak1), Sun Woo Kim1), Hyeyoon Chang1)

    1) Deep Bio Inc. | 2) Samsung Medical Center | 3) Seoul National University | 4) Logongbio-Convergence Research Foundation | 5) Abion

    ABSTRACT #427

    Deep Bio to Present Their Research Findings at The Poster Sessions During the USCAP 113th Annual Meeting

    SEOUL, SOUTH KOREA, March 19, 2024 /EINPresswire.com/ — Four research results will be presented to validate Deep Bio’s pioneering research and breakthroughs in AI advancements in digital pathology.
    Link to the Article 

    Deep Bio, a leader in AI-driven solutions for cancer diagnostics, announced that four different research results will be presented at the poster presentations during the 2024 United States and Canadian Academy of Pathology (USCAP) Annual Meeting, to be held in Baltimore from March 23 to March 28. The posters will describe Deep Bio’s pioneering advancements in AI-driven pathology, demonstrating how novel AI techniques enhance the company’s expertise across diverse tissue and cancer types.

    Sun Woo Kim, CEO of Deep Bio, said, “With the increasing demand for advanced medical technologies based on AI, digital pathology has recently emerged as a significant topic. We proudly present the meaningful research results of deep-learning-based cancer diagnosis at the world-class pathology conference for six consecutive years since 2018. We will continue our research to ensure that our deep learning technology contributes to a new era of diagnosis, ranging from cancer diagnosis and prognosis to treatment, across various types of cancer.”

    Innovative Research Highlights at USCAP:

    • Poster Presentation (#167): A Deep Learning-Based Tumor Area Identification Using a Semi-Supervised Approach for IHC Stained Images in Non-Small Lung Carcinoma
    o Session Date & Time: March 25, 2024, From 1:00 to 4:30 p.m.
    o Lead Author: Yunseob Hwang
    o Overview: This research evaluates a semi-supervised approach to cancer area detection models in cMET IHC-stained whole slide images (WSIs) of non-small cell lung cancer (NSCLC). Manually delineating entire cancer areas in large tissues like resection slides remains a labor-intensive task for developing AI models. Due to this fact, this research introduces a simple yet effective semi-supervised learning approach for tumor area segmentation deep learning models using IHC stained images.

    • Poster Presentation(#180): Enhancing Multi-organ: Frozen Section Cancer Discrimination Model with Additional Formalin-Fixed Paraffin-Embedded Whole Slide Images
    o Session Date & Time: March 26, from 9:30 to 12:00 p.m.
    o Lead Author: Joonho Lee
    o Overview: This research explores the automated analysis of H&E-stained Frozen Section (FS) WSIs using a deep learning model across various tissue types to enhance the accuracy of FS cancer discrimination. This deep-learning model is specially developed to determine the presence of cancer in H&E-stained FS whole slide images in the Breast, Lung, Stomach, Breast Sentinel Lymph Node, and Prostate, using H&E-stained and Formalin-Fixed Paraffin-Embedded (FFPE) WSIs.

    • Poster Presentation(#210): Enhancing Frozen Section Whole Slide image classification by Style transfer with CycleGAN
    o Session Date & Time: March 27, from 1:00 to 4:30 p.m.
    o Lead Author: Junho Lee
    o Overview: This research introduces a style transfer model to transform the delineation of FFPE data into Frozen Section (FS) data, enriching the data and enhancing feature discrimination. Unlike the Formalin-Fixed Paraffin-Embedded (FFPE) tissue slides, the FS Whole Slide Image (WSI) often has low slide quality due to artifacts, leading to inferior prediction performance compared to the FFPE WSI. Therefore, training-styled-transferred FFPE slides can enhance the predictive performance of FS slides at the slide level and improve the discriminative feature of patch-level images.

    • Poster Presentation(#173): Storage Optimization for Digital Pathology Images: Super-Resolution based image compression
    o Session Date & Time: March 26, from 9:30 to 12:00 p.m.
    o Lead Author: Soyeon Jang
    o Overview: This research proposes a super-resolution-based image compression technique to reduce storage demands without sacrificing image quality since storing large volumes of digital pathology data requires monetary investment for institutions to process high caseloads.

    About Deep Bio

    Deep Bio is an AI healthcare company dedicated to advancing the field of cancer pathology. Focusing on deep learning, the company develops cutting-edge In Vitro Diagnostic Software as Medical Devices (IVD SaMDs) to empower pathologists and medical professionals with state-of-the-art tools for more accurate cancer diagnosis and prognosis. Deep Bio is committed to improving cancer treatment decisions and patient outcomes by harnessing the power of artificial intelligence.

    For more information, visit the website: www.deepbio.co.kr.

    Deep Bio’s DeepDx Prostate Featured in CancerX’s Solutions Catalog, Pioneering Digital Innovation in Oncology

    SEOUL, SOUTH KOREA, February 5, 2024 /EINPresswire.com/ — Deep Bio, a pioneer of AI-powered cancer diagnostics solutions, proudly announced the inclusion of DeepDx Prostate in CancerX’s Solutions Catalog. This significant participation positions Deep Bio as a key contributor to CancerX’s inaugural project, which is dedicated to dismantling barriers to digital innovation in oncology.

    Link to the Article 

    The Solutions Catalog, launched in 2023, represents CancerX’s inaugural concrete initiative, strategically designed for digital transformation to enhance access to cancer care and alleviate financial burdens on patients.

    The Solutions Catalog, an integral part of CancerX’s groundbreaking project, is segmented into three key areas: Screening/Diagnosis, Treatment/End-of-Life Care, and Survivorship. It acts as a reference guide showcasing commercialized digital products and solutions in medical institutions across the United States. The CancerX project ensures immediate access to cutting-edge digital cancer diagnostics and treatment solutions from the featured companies in the Solutions Catalog for healthcare organizations nationwide.

    The latest update of the CancerX Solutions Catalog features Deep Bio’s AI-powered software, DeepDx Prostate. This groundbreaking solution empowers pathologists to deliver more precise and efficient prostate diagnoses by automating prostate cancer lesion identification, grading, and quantification of tumor proportion measurements. The solution also significantly decreases turnaround times, minimizes subjectivity in diagnosis, and reduces costs associated with overdiagnosis or unnecessary treatments. These groundbreaking features have immense potential to reduce health inequities in cancer care access and alleviate financial strains on patients.

    Sun Woo Kim, CEO of Deep Bio, highlights the urgency of tackling prostate cancer, the second-leading cause of death among American men. “The innovative features of DeepDx Prostate have immense potential to reduce health inequities in cancer care access and alleviate financial strains on patients,” Kim emphasizes the positive impacts of DeepDx Prostate in the medical field. “Due to that fact, Deep Bio’s listing in the Solutions Catalog signifies a transformation of the diagnostic landscape but also contributes to escalating the quality of medical services for prostate cancer patients,” he said.

    Deep Bio is proud to contribute to CancerX’s mission of advancing cancer care through innovative digital solutions. The collaboration underscores a shared commitment to improving patient outcomes and enhancing the overall landscape of cancer diagnostics and treatment.

    For more information, please visit Deep Bio’s website or contact sales@deepbio.co.kr.

    Deep Bio Announces Launch of DeepDx Prostate in Switzerland, Paving the Way for European Expansion

    SEOUL, SOUTH KOREA, JANUARY 31, 2024 / PRNewswire/ – Deep Bio’s AI-powered Prostate Cancer Diagnostic Software is Now Available on the Swiss Market, Advancing Healthcare Access Across Europe.

    Deep Bio, a leader in artificial intelligence (AI)-powered cancer diagnostics, proudly announced the availability of its CE-marked in-vitro diagnostic device, DeepDx Prostate, on the Swiss market.
    This strategic market expansion and product launch signify a significant milestone in Deep Bio’s roadmap for expanding its footprint in Europe.

    Link to the Article

    DeepDx Prostate is an AI-powered software for the assessment of prostate cancer on digital whole slide images of hematoxylin and eosin-stained prostate core-needle biopsies. By automating the identification, grading, and quantification of cancerous lesions, this diagnostic software empowers pathologists to make more accurate and efficient prostate cancer diagnoses, reducing subjectivity and turnaround times. DeepDx Prostate is the first in Deep Bio’s suite of diagnostic software products that aim to enhance diagnostic accuracy and in turn, optimize clinical management and patient outcomes.

    “We are excited to introduce DeepDx Prostate to Switzerland,” said Sun Woo Kim, the CEO and founder of Deep Bio. “Increasing the reach of DeepDx Prostate to additional countries underscores Deep Bio’s unwavering commitment to improving global access to high-quality healthcare and advancing prostate cancer diagnosis through AI-driven innovation.”

    Deep Bio continues to expand its global reach through partnerships with digital pathology leaders in the US, Europe, and India. The company conducts collaborative research with leading US research institutions, including Stanford Medical School and Harvard Dana-Farber Cancer Institute, with its clinical validation studies published in prestigious medical journals, such as Cancers and npj Digital Medicine.

    For more information, please visit Deep Bio’s website or contact sales@deepbio.co.kr.

    About Deep Bio

    Deep Bio Inc. is an AI healthcare company with expertise in deep learning and cancer pathology. As the country’s first to obtain Korea’s MFDS (Ministry of Food and Drug Safety) approval of an AI-based cancer diagnostic support solution, Deep Bio’s vision is to radically improve the efficiency and accuracy of pathologic cancer diagnosis and prognosis.

    DeepDx® Prostate is a clinically-validated AI for the assessment of prostate cancer. Extensively tested at US CLIA labs (700k cores between 2019 and 2021), DeepDx® Prostate can alleviate the shortage of pathologists and the resultant increase in workload, while reducing diagnostic subjectivity. To learn more, visit https://www.deepbio.co.kr.

    Deep Bio becomes first Korean cancer diagnostic AI company to win CES Innovation Award

    SEOUL, SOUTH KOREA, NOVEMBER 21, 2023 / PRNewswire/ – Won the CES Innovation Award for pioneering a new milestone in Korean cancer diagnosis AI solutions.

    Link to the Article 

    Deep Bio announced today that it has been awarded the CES Innovation Award as the first Korean cancer diagnosis A.I. company in CES 2024, the world’s largest electronics exhibition, to be held in Las Vegas, USA, in the upcoming January.

    The Consumer Technology Association (CTA) today announced the products and technologies that won CES Innovation Awards. Deep Bio won the Innovation Award in the Digital Healthcare category, the first time the A.I. diagnostic-aid software company helped detect meaningful findings among cancer patients.

    Deep Bio recognizes this award as an achievement that demonstrates its world-class technology in cancer diagnosis and prognosis and raises the profile of Korean cancer diagnosis A.I. companies.

    Deep Bio leverages its expertise in deep learning and cancer pathology to provide state-of-the-art in vitro diagnostic software (IVDs, SaMDs) that analyzes various cancers’ cancerous areas and severity to help medical professionals make more informed decisions. As such, Deep Bio’s DeepDx® Prostate has a high precision of 99% sensitivity and 97% specificity.

    “Deep Bio has proven its technical excellence both internally and externally, winning the silver medal at the Edison Awards in the United States, which is called the ‘Oscar of Innovation’ in 2021 and was won by Tesla’s Elon Musk and Apple’s Steve Jobs, as well as the first place in 2019 at the CAMELYON16 Challenge, which evaluates image recognition technology,” said Kim Sun-woo, CEO of Deep Bio. “We will strive to improve the cancer diagnosis environment and provide cutting-edge cancer diagnosis AI solutions to medical professionals through generous investment in technology to overcome the biggest challenge of humanity, cancer.”

    Meanwhile, CES 2024 will be held at the Las Vegas Convention Center in the United States from January 9th to 12th, and Deep Bio’s award can be searched on the CES Innovation Awards website.

    For more information, please visit Deep Bio’s website or contact sales@deepbio.co.kr.

    About Deep Bio

    Deep Bio is an AI healthcare company focusing on deep learning to advance the field of cancer pathology. By developing cutting-edge in vitro diagnostic software (IVD SaMD), Deep Bio strives to provide pathologists and medical professionals with state-of-the-art diagnostic aids for more accurate cancer diagnosis and prognosis. Its main products include DeepDx® Prostate, an AI software for prostate cancer analysis, which was the first in vitro diagnostic product to be approved by the Ministry of Food and Drug Safety in Korea. In 2019, the product won the Silver Award at the U.S. Edison Awards, which was won by Apple (2012) and Tesla (2014), and also won first place at the CAMELYON16 Challenge, a global AI digital pathology image analysis competition. In 2021, Deep Bio was recognized as one of the top 100 AI startups in Korea for the third year in a row, and has been recognized as a company that will lead the innovation of digital healthcare in the future. For more information, please visit Deep Bio’s website at