In digital pathology, advancements and proliferation in artificial intelligence (AI) and digital laboratory equipment are enabling streamlined workflows and new insights. However, they can also multiply the potential challenges laboratories face with software integration. To fully leverage these advancements, laboratories need the capability to seamlessly integrate different tools into their digital pathology ecosystem. Open pathology embodies this approach, enabling labs to implement the pathology platform(s), AI, and laboratory equipment of their choice. 

Digital pathology platforms designed for open pathology offer users substantial advantages, allowing the creation of digital pathology ecosystems optimized for their lab’s needs and able to evolve with advancing technologies. Integrated systems adapted to users’ specific workflows enable increased efficiency through streamlined interfacing, requiring fewer logins and switches between programs. Digital pathology solutions that don’t support open integrations can trap users within a limited ecosystem, preventing them from fully benefiting from AI and other emerging technologies. As AI continues to rapidly advance, flexibility in digital pathology is more essential than ever. 

At Indica Labs, we’ve been committed to open pathology since our founding, and this focus has guided the development of our HALO platforms for both life sciences and clinical applications. In the rest of this blog post, we’ll discuss how we’re enabling open pathology in our platforms and explore three of the partnerships through which we’re integrating leading third-party AI into our platforms.

Indica Labs’ Commitment to Open Pathology 

Indica Labs is dedicated to supporting and enabling open pathology throughout the design, implementation, and use of our platforms. This commitment empowers labs to implement a digital pathology ecosystem customized to their specific needs and workflows. 

Supporting Open Standards 

• We support open standards and are developing toward them in our platforms, freeing users to work across diverse data formats and tools. For images and annotations, our platforms can read both OME and DICOM files and can rewrite native formats into OME.TIFF. 

• For AI models, we support the ONNX format, enabling users to easily transfer models between HALO AI and other platforms. Users of our HALO AI deep learning toolbox can both import models in ONNX format and export trained models in this format. 

• For LIS/EHR integration, we support HL7 and HL7 FHIR, facilitating streamlined integration between HALO platforms and laboratory information systems or electronic health records through these industry standards.

Championing Integration 

• We designed our HALO platforms with open, powerful APIs, supported by comprehensive documentation and sample code, enabling and simplifying custom integrations. 

• For HALO® users needing highly specific image analysis functions, we provide software development kits that allow users to mold the platform to their specialized needs and workflows by creating custom modules. 

• HALO AP® also has a software development kit that includes example code, support, and a dedicated development environment to facilitate the integration of third-party AI. 

Ensuring Choice 

• Our platforms are scanner agnostic and support a wide array of image file formats from all leading scanner manufacturers, allowing labs to choose the scanner that best fits their needs. We also actively collaborate with scanner developers to ensure ongoing compatibility. 
• We work with third-party image analysis and IMS vendors to enable integration of HALO image analysis with their systems, supporting labs that prefer to use HALO alongside a third-party platform.