Author: Deepbio

SEOUL, SOUTH KOREA, November 19, 2024 /EINPresswire.com/ — Deep Bio, a leader in artificial intelligence for digital pathology, announced a collaboration to integrate its prostate cancer analysis solution, DeepDx Prostate, with PathAI’s AISight®1 Image Management System (IMS). This collaboration combines Deep Bio’s clinically validated AI technology with PathAI’s advanced digital pathology platform, expanding access to powerful diagnostic tools for prostate cancer.

DeepDx Prostate, which is CE-marked and clinically validated, provides AI-supported diagnostics for prostate cancer by analyzing digitized slide images of Hematoxylin & Eosin (H&E) stained prostate specimens. The software classifies each lesion by histological type or risk grade, measures lesion size, and generates metrics crucial for cancer diagnosis, prognosis, and treatment planning, including lesion type proportions and overall tissue lesion ratios.

PathAI, the developer of the AISight IMS platform, is a pioneering provider of AI-powered research tools and services for pathology, working with leading life sciences companies, laboratories, and researchers to advance precision medicine. AISight is a complete solution for primary diagnosis, centralizing case and image management in a cloud-native, intelligent platform. It provides pathologists with an intuitive way to view, interpret, share, and manage whole slide images, enhancing both confidence and efficiency in diagnostic workflows. Ideal for clinical settings of all sizes—from individual labs to large hospital networks—AISight is utilized by premier laboratories and research centers globally to streamline digital pathology workflows and AI applications, serving as a central hub equipped with top-tier AI tools for multiple histopathology applications.

“We are thrilled to integrate DeepDx Prostate with PathAI’s AISight IMS platform,” said Deep Bio CEO Sun Woo Kim. “This collaboration allows broader access to our AI-driven diagnostic tools, and we aim to transform prostate cancer diagnostics by empowering clinicians with advanced tools to guide treatment decisions.”

“We are excited to welcome Deep Bio’s DeepDx Prostate algorithm into the AISight platform,” said Andy Beck, CEO of PathAI. “This collaboration exemplifies our commitment to equipping pathologists with cutting-edge tools that enhance precision and increase workflow efficiency. By expanding the reach of advanced AI solutions, we continue to make meaningful strides in improving patient outcomes and transforming digital pathology on a global scale.”

Leveraging PathAI’s rapidly growing install base of AISight users, through this integration, DeepDx Prostate will reach a wider range of clinicians and patients across North America and Europe, providing access to leading-edge diagnostic tools for prostate cancer. This collaboration marks a pivotal step toward Deep Bio’s vision of advancing AI-powered cancer diagnostics on a global scale.

About Deep Bio

Deep Bio is a pioneer in AI-powered pathology, transforming cancer diagnostics with innovative solutions that support pathologists in delivering precise and efficient care. Its flagship product, DeepDx Prostate, is a CE-IVD-marked diagnostic aid extensively vetted through studies of over 700,000 U.S. biopsy specimens. Offering tools for cancer detection, Gleason scoring, and gland-level tumor analysis, DeepDx Prostate enhances diagnostic accuracy while streamlining pathology workflows. Deep Bio’s commitment to advancing digital pathology is driven by collaboration and a dedication to improving patient outcomes

Note: AISight is for Research Use Only in the US; AISight Dx is CE-IVDR in Europe and UKCA in UK

Deep Bio Inc., a leader in AI-driven digital pathology solutions, announced the publication of an external validation study for its DeepDx Prostate AI algorithm, conducted by Stanford Medicine.
The study, titled “External Validation of an Artificial Intelligence Model for Gleason Grading of Prostate Cancer on Prostatectomy Specimens,” was published in the British Journal of Urology International.

SEOUL, SOUTH KOREA, August 6, 2024

The study evaluated the performance of DeepDx Prostate in detecting and grading prostate cancer on whole-mount radical prostatectomy (RP) specimens. The DeepDx Prostate algorithm, which had been originally trained and validated on prostate core needle biopsy (CNB) images from two hospitals in South Korea, was tested to determine its generalizability to different patient populations and much larger prostatectomy specimens. Researchers aimed to assess the model’s performance on RP specimens from an institution it had never encountered before, without any fine-tuning. The validation study demonstrated the following key findings:

1. High Accuracy: The algorithm achieved an impressive sensitivity of 0.997 and specificity of 0.88 in detecting cancer presence in radical prostatectomy specimens.
2. Agreement with Pathologists: The AI showed strong agreement with expert uropathologists, with Cohen’s Kappa values of UWK 0.91 for cancer presence, QWK 0.89 for Gleason grade classification, and QWK 0.89 for risk group identification (benign, low [GG1], intermediate [GG 2-3], and high-risk [GG 4-5]).
3. Generalizability: The algorithm demonstrated robust performance across different datasets and tissue types, highlighting its potential for widespread implementation across various healthcare settings.

Sun Woo Kim, CEO of Deep Bio, commented, “Accurate grading of Gleason scores is key to optimizing treatment plans and predicting outcomes. The swift and precise results provided by DeepDx Prostate will be invaluable in clinical practice.”

Bogdana Schmidt MD, MPH, the lead author of this validation study and current assistant professor at the University of Utah, said, “The validation of DeepDx Prostate through this study underscores its generalizability and potential for widespread clinical implementation. This tool will aid in the timely and accurate grading of prostate cancer, which is crucial for developing effective treatment plans.”

The study concludes that DeepDx Prostate algorithm is an accurate tool for identifying and grading prostate cancer on digital histopathology images of whole-mount RP specimens, demonstrating almost perfect concordance with expert GU pathologists and impressive performance in various clinically relevant tasks.

Deep Bio Inc. is committed to advancing AI technology to enhance diagnostic accuracy and efficiency in pathology. When used for its intended tissue type—CNB samples—DeepDx Prostate demonstrates 99% sensitivity and 97% specificity.

About Deep Bio

Established in 2015, Deep Bio Inc. is a medical AI development company specializing in deep learning and cancer pathology diagnostics. The company develops in-vitro diagnostic medical device software (IVD SaMD) that enhances the accuracy of cancer diagnosis and prognosis, aiding in better treatment decision-making.

Deep Bio’s prostate cancer diagnostic AI solution, which has obtained the European CE-IVD certification, analyzes high-resolution Whole Slide Images (WSI) to identify and segment cancerous lesions. The software classifies each lesion by histological type or risk grade, measures lesion size, and provides various metrics, such as the proportion of each lesion type and the overall tissue lesion ratio, essential for cancer diagnosis, prognosis, and treatment planning.

The AI solution provides detailed analysis results and reports, improving patients’ and professionals’ accessibility to medical information. This innovation earned Deep Bio the CES Innovation Award in 2024.

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.

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.

 

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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