LTTS launches NVIDIA-powered digital twin for respiratory diagnostics
LTTS is stepping into the medical‑AI arena with a new digital‑twin platform aimed at respiratory diagnostics. The move comes as clinicians seek more precise tools for visualising the complex anatomy of the lungs, especially when planning invasive procedures like bronchoscopy. While many AI solutions focus on generic image analysis, LTTS is targeting the niche where airway mapping and lesion detection can directly influence patient outcomes.
The company says the system combines high‑resolution imaging with real‑time simulation, giving physicians a virtual sandbox to rehearse interventions. By partnering with NVIDIA, LTTS hopes to tap into hardware‑accelerated inference and advanced segmentation models that can handle the intricacies of blood vessels, lobes and pathological changes. The result, according to LTTS, is an engine that not only renders detailed 3D reconstructions but also supports interactive path‑planning—a capability that could streamline workflow in busy pulmonary clinics.
Built on NVIDIA MONAI for medical image segmentation and NVIDIA TensorRT for optimised AI inference, the platform enables detailed visualisation of airways, blood vessels, lobes and lesions, along with interactive simulation and path‑planning support for bronchoscopy procedures. LTTS said its engine
Built on NVIDIA MONAI for medical image segmentation and NVIDIA TensorRT for optimised AI inference, the platform enables detailed visualisation of airways, blood vessels, lobes and lesions, along with interactive simulation and path-planning support for bronchoscopy procedures. LTTS said its engineering capabilities in medical imaging and proprietary navigation systems allow static CT scans to be converted into dynamic, clinically meaningful digital representations that evolve with patient data. The company said such biological digital twins can support planning and navigation for conditions including lung cancer, COPD and infectious diseases.
"AI is reshaping what's possible in diagnostics and medical technology," said Alind Saxena, executive director and president of mobility and tech at LTTS. "Our collaboration with NVIDIA combines LTTS' expertise in AI-driven diagnostics and predictive analytics, with NVIDIA's powerful modeling and visualisation platform. This lets us engineer a digital twin platform that not only enhances diagnostic accuracy but also gives clinicians an immersive, real-time planning tool ultimately helping deliver better outcomes for patients around the world," Saxena added.
David Niewolny, director of business development for healthcare/medical at NVIDIA, said the partnership demonstrates the potential for accelerated computing in clinical workflows. "Working with LTTS to accelerate the development of AI-enabled medical technology demonstrates how NVIDIA is empowering the healthcare industry with accelerated computing and AI innovation," Niewolny said. "LTTS is enabling transformative solutions that bring the vision of real-time AI and biological digital twins powered by NVIDIA to clinical practice, delivering interactive, real-time visualisation and intelligent guidance to help clinicians provide higher-quality care and achieve better outcomes for patients."
Can clinicians rely on a digital twin for bronchoscopy? While LTTS touts a next‑generation AI‑powered platform built on NVIDIA MONAI and TensorRT, the actual impact on diagnostic precision remains to be proven. The system promises scalable, low‑latency solutions that visualize airways, blood vessels, lobes and lesions, and it adds interactive simulation and path‑planning support for respiratory procedures.
Its debut at the RSNA 2025 conference will be the first public test of those claims. If the platform delivers the promised performance, it could broaden access to advanced imaging tools for clinicians worldwide. Yet adoption will depend on integration with existing workflows and regulatory clearance—factors not addressed in the announcement.
The engine’s reliance on optimized AI inference suggests speed, but whether that translates into measurable clinical benefit is still unclear. A bold step toward AI‑driven diagnostics, but the evidence base is not yet established.
Further Reading
- L&T Technology Services Transforms Respiratory Diagnostics with NVIDIA AI-Powered Digital Twin Technology - Business Wire
- L&T Technology Services Announces the Development of Next-Gen AI-Powered Digital Twin Platform for Respiratory Diagnostics and Lung Navigation - Market Screener
- Papers with Code - Latest NLP Research - Papers with Code
- Hugging Face Daily Papers - Hugging Face
- ArXiv CS.CL (Computation and Language) - ArXiv
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Common Questions Answered
What technologies does LTTS use to power its digital‑twin platform for respiratory diagnostics?
LTTS builds the platform on NVIDIA MONAI for medical image segmentation and NVIDIA TensorRT for optimized AI inference. These technologies enable high‑resolution visualization of airways, blood vessels, lobes, and lesions with low‑latency performance.
How does the LTTS digital twin improve bronchoscopy planning compared to traditional imaging?
The digital twin converts static CT scans into dynamic, interactive 3D models that simulate patient‑specific anatomy. Clinicians can visualize airway pathways, map lesions, and practice path‑planning, which can enhance precision during invasive bronchoscopy procedures.
When and where will the LTTS digital‑twin platform be publicly demonstrated?
LTTS will showcase the platform at the RSNA 2025 conference, marking its first public test. The demonstration aims to validate the system’s claims of scalable, low‑latency visualization and interactive simulation for respiratory procedures.
What specific clinical features does the LTTS digital twin provide for respiratory diagnostics?
The platform offers detailed visualization of airways, blood vessels, lung lobes, and lesions, along with interactive simulation and path‑planning support. These features are designed to assist clinicians in airway mapping and lesion detection, directly influencing patient outcomes.