DELRAY BEACH, Fla., Aug. 13, 2026 /PRNewswire/ — According to MarketsandMarkets™, the Disposable Medical Sensors Market is projected to reach USD 16.13 billion by 2031 from USD 11.86 billion in 2026, at a CAGR of 6.4% during the forecast period.

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Disposable Medical Sensors Market Size & Forecast:

Market Size Available for Years: 2026–20312026 Market Size: USD 11.86 billion2031 Projected Market Size: USD 16.13 billionCAGR (2026–2031): 6.4%

Disposable Medical Sensors Market Trends & Insights:

The disposable medical device sensors market covers Europe, North America, Asia Pacific, Latin America, the Middle East & Africa, and the GCC Countries. North America is expected to grow at the fastest CAGR of 6.7% during 2026–2031.In 2025, the biosensors segment held a 55.6% share of the disposable medical device sensors market by type, making it the leading segment. This dominance is driven by their widespread adoption in glucose monitoring, infectious disease testing, point-of-care diagnostics, and continuous patient monitoring applications.By application, the diagnostic devices segment accounted for the largest share of 57.1% of the disposable medical device sensors market in 2025, driven by increasing demand for rapid, accurate, and cost-effective diagnostic solutions. The widespread adoption of disposable sensors in glucose monitoring, infectious disease testing, and point-of-care diagnostics has significantly contributed to the segment’s leading position.

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The growth of the global disposable medical device sensors market is primarily driven by the increasing prevalence of chronic diseases, the rising demand for continuous patient monitoring, and growing concerns regarding hospital-acquired infections (HAIs) and cross-contamination. The expanding adoption of single-use sensors in critical care, diagnostic testing, and minimally invasive procedures, coupled with the increasing demand for home healthcare and remote patient monitoring solutions, is further fueling market growth. In addition, rapid technological advancements in biosensors, wearable medical devices, flexible sensor technologies, and wireless connectivity have enhanced the accuracy, reliability, and ease of use of disposable medical device sensors. Increasing healthcare expenditure, favorable government initiatives supporting digital health, and expanding healthcare infrastructure in emerging economies are also expected to create significant growth opportunities for market participants.

Despite these favorable growth drivers, the market faces several challenges. Stringent regulatory requirements for product approval, lengthy certification processes, and the need to comply with rigorous quality and safety standards can delay product commercialization. Additionally, pricing pressures, reimbursement limitations in certain regions, and concerns related to integrating disposable sensors with connected healthcare systems and ensuring data security pose challenges for manufacturers. Nevertheless, ongoing innovation and increasing investments in advanced sensor technologies are expected to support the long-term growth of the disposable medical device sensors market.

By product, the biosensors segment accounted for the largest share of the global disposable medical device sensors market in 2025.

Based on product, the disposable medical device sensors market is segmented into biosensors, accelerometers, temperature sensors, image sensors, and other sensors. The biosensors segment accounted for the largest share of the market in 2025. The significant share of this segment can be attributed to the extensive adoption of disposable biosensors across various diagnostic and monitoring applications, including glucose monitoring, infectious disease testing, pregnancy testing, drug and alcohol screening, and continuous glucose monitoring (CGM) systems. The growing prevalence of chronic diseases, increasing demand for rapid and accurate diagnostic solutions, and rising adoption of point-of-care testing technologies have further supported the growth of the biosensors segment. Additionally, advancements in biosensor technologies, such as improved sensitivity, miniaturization, and enhanced accuracy, have expanded their applications across hospitals, diagnostic laboratories, and home healthcare settings, driving their continued dominance in the disposable medical device sensors market.

By type, the strip sensors segment accounted for the largest market share in 2025.

Based on type, the disposable medical device sensors market is segmented into strip sensors, wearable sensors, invasive sensors, and ingestible sensors. The strip sensors segment accounted for the largest share of the market in 2025. The leading position of this segment is primarily attributed to the widespread use of disposable strip sensors in glucose monitoring, infectious disease testing, pregnancy testing, and other rapid diagnostic applications. The increasing global burden of diabetes, infectious diseases, and other chronic conditions has significantly contributed to the rising demand for glucose test strips and diagnostic test strips. Furthermore, the growing adoption of strip sensors in point-of-care and home-based testing is supported by their key advantages, including affordability, ease of operation, portability, rapid results, and reduced risk of contamination. These benefits, along with the increasing preference for convenient and decentralized diagnostic solutions, continue to drive the growth of the strip sensors segment in the disposable medical device sensors market.

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By application, diagnostic testing accounted for the largest market share in 2025.

Based on application, the disposable medical device sensors market is segmented into patient monitoring, diagnostic testing, therapeutics, and imaging. The diagnostic testing segment accounted for the largest share of the market in 2025. The dominant share of this segment is primarily attributed to the increasing prevalence of lifestyle-related disorders, infectious diseases, and other chronic conditions, which has accelerated the demand for rapid and reliable diagnostic solutions. The extensive use of disposable sensors in glucose monitoring, infectious disease detection, pregnancy testing, and other point-of-care diagnostic applications across hospitals, clinics, laboratories, and home healthcare settings has further supported segment growth. Additionally, the growing preference for early disease detection, self-monitoring devices, and decentralized diagnostic solutions, along with the advantages of disposable sensors such as quick results, ease of use, and reduced risk of cross-contamination, continues to drive the expansion of the diagnostic testing segment.

Key Players

Leading players in the Disposable Medical Sensors companies include Abbott Laboratories (US), F. Hoffmann-La Roche Ltd (Switzerland), Medtronic (Ireland), and GE HealthCare Technologies Inc. (US), among other players.

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BEIJING, Aug. 13, 2026 /PRNewswire/ — WiMi Hologram Cloud Inc. (NASDAQ: WIMI) (“WIMI” or the “Company”), a globally leading technology provider, announces a major breakthrough in releasing Hybrid Quantum Neural Network (H-QNN), an innovative hybrid quantum-classical neural network technology tailored for the image recognition sector. The technology creatively integrates parameterised quantum circuits with classical neural network architectures and has been successfully deployed for binary image classification tasks on the MNIST dataset, delivering outstanding performance in classification accuracy, feature representation capability, and model training efficiency.

Image classification stands as one of the most fundamental and critical tasks within the field of computer vision. Spanning handwritten digit recognition, facial recognition, industrial defect detection, and autonomous driving perception systems, image classification technology forms the core foundation of nearly all modern AI visual systems. Conventional deep learning models primarily rely on Convolutional Neural Networks (CNNs) to execute feature extraction. CNNs extract edge, texture, contour, and semantic information step-by-step via sliding convolution kernels across images, then leverage fully connected networks to render classification decisions. Nevertheless, as data dimensions expand and image features grow increasingly intricate, traditional neural networks have gradually revealed multiple inherent limitations.

First, data distributions within high-dimensional feature spaces often feature complex nonlinear structures, requiring classical networks to incorporate massive quantities of parameters to construct sufficiently sophisticated decision boundaries. Second, deep network training is prone to issues such as vanishing gradients, local optima, and overfitting. In addition, training large-scale models demands enormous computational resources and energy consumption. Meanwhile, advances in quantum computing have opened new avenues to address the aforementioned challenges.

Its core design philosophy centers on fully harnessing the high-dimensional feature mapping capacity of quantum circuits during image classification: complex pattern recognition tasks are delegated to quantum layers, while parameter optimisation and final classification decisions are handled by mature, stable classical neural networks. This architecture design effectively circumvents the constraints imposed by the limited scale of current quantum hardware, while maximising the inherent advantages of quantum computing in feature representation.

From an overall architectural perspective, WIMI’s H-QNN establishes a complete end-to-end data processing pipeline that enables deep integration between quantum computing and classical computing.

To render classical images processable by quantum computers, the conversion of classical data into quantum-compatible data must be resolved first. Each image in the MNIST dataset consists of a 28×28-pixel grid with 784 grayscale values in total. Directly loading all pixel data into a quantum system would incur prohibitive quantum resource overhead. Accordingly, H-QNN first employs a classical preprocessing module to conduct dimensionality reduction and normalisation on input images. Standardised feature vectors are subsequently converted into data formats compatible with quantum state representation before entering the quantum encoding phase. Within this phase, classical features are mapped to the amplitudes and rotation angles of quantum bits. Each qubit is initialised to the ground state 0, and feature encoding is implemented through rotation gates. Quantum rotation operations translate image features into quantum state parameters; following this transformation, pixel information from the original image is embedded within the quantum state space, laying the groundwork for subsequent quantum feature learning.

One of the most pivotal innovations of WIMI’s H-QNN lies in its parameterised quantum feature learning module. Where traditional CNNs use convolution kernels to learn image features, this responsibility is undertaken by parameterised quantum circuits in H-QNN. The quantum layer is composed of stacked rotation gates and entanglement gates: rotation gates execute local feature transformations, whereas entanglement gates establish correlation relationships between distinct qubits.

Quantum states continuously evolve throughout this process. Driven by quantum entanglement mechanisms, intricate correlational structures emerge across multiple qubits, a correlation capacity far surpassing the linear connection schemes adopted by traditional neural networks. When certain patterns within images carry complex spatial relationships, quantum entanglement naturally captures these high-order features. This mechanism is particularly well-suited to processing nonlinear distributions embedded within high-dimensional data. Complex feature representations that may require hundreds or even thousands of neurons in classical networks can be expressed with far fewer parameters within the quantum feature space.

The essence of classification lies in identifying decision boundaries, which traditional neural networks construct through multi-layer nonlinear transformations. By contrast, WIMI’s H-QNN leverages the exponential dimensional advantage of quantum state spaces to deliver a more flexible classification mechanism. Within quantum state space, samples belonging to different categories are mapped to distinct spatial regions. Following multi-layer quantum transformations, data points that prove difficult to separate in classical space become far more distinguishable. Quantum feature mapping effectively amplifies inter-class distances, lowering classification difficulty; such quantum-enhanced feature spaces can substantially boost model recognition performance.

Upon completing quantum feature learning, the quantum system transmits extracted information back to the classical neural network via quantum measurement. The quantum measurement layer retrieves quantum state information and converts it into classical numerical values. Specifically, feature vectors are obtained by measuring the expectation values of Pauli operators, which are then fed into the classical classification network. The measurement process functions to compress and project high-dimensional features learned within quantum space back into classical space. Since complex feature extraction has already been completed by the quantum layer, the subsequent classification network can maintain a compact structure, allowing the overall model to sustain high precision while cutting down computational overhead.

Moving forward, WIMI intends to extend this model to more complex datasets. The company will also explore deeper quantum network architectures, adaptive quantum feature extraction mechanisms, and large-scale quantum entanglement learning frameworks. As quantum hardware performance sees sustained improvements, hybrid quantum-classical neural networks are poised to transition from laboratory research to industrial deployment, delivering transformative value across intelligent manufacturing, autonomous driving, remote sensing recognition, biomedical treatment, and financial analytics. The launch of H-QNN not only demonstrates the immense potential of quantum computing to empower artificial intelligence but also charts a new development trajectory for next-generation intelligent computing architectures, marking a vital milestone as quantum-enhanced machine learning advances from theoretical research toward practical real-world applications.

About WiMi Hologram Cloud Inc.

WiMi Hologram Cloud Inc. (NASDAQ: WIMI) focuses on holographic cloud services, primarily concentrating on professional fields such as in-vehicle AR holographic HUD, 3D holographic pulse LiDAR, head-mounted light field holographic devices, holographic semiconductors, holographic cloud software, holographic car navigation, metaverse holographic AR/VR devices, and metaverse holographic cloud software. It covers multiple aspects of holographic AR technologies, including in-vehicle holographic AR technology, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR virtual advertising technology, holographic AR virtual entertainment technology, holographic ARSDK payment, interactive holographic virtual communication, metaverse holographic AR technology, and metaverse virtual cloud services. WiMi is a comprehensive holographic cloud technology solution provider. For more information, please visit http://ir.wimiar.com.

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The original version of this announcement is the officially authorized and only legally binding version. If there are any inconsistencies or differences in meaning between the Chinese translation and the original version, the original version shall prevail. WiMi Hologram Cloud Inc. and related institutions and individuals make no guarantees regarding the translated version and assume no responsibility for any direct or indirect losses caused by translation inaccuracies.

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New platform automates supplier quote collection, comparison and sourcing decisions to help contractors reduce material costs and make purchasing faster and easier

IRVINE, Calif., Aug. 13, 2026 /PRNewswire/ — Raiven, the procurement platform purpose-built for the building trades, today announced the launch of Raiven 2.0, a major new release that brings the company’s patented Raiven Best Value™ sourcing technology directly into its platform and extends its functionality to the field.

Raiven 2.0 is designed to address the inherently complex many-to-many relationships between contractors and suppliers. It is an agentic application layer that brings optimal rules-based decision making to every purchase every time for every role.

Rather than simply identifying the lowest unit price, Raiven Best Value™ evaluates the factors that determine the actual cost and practicality of a purchase, including availability, lead time, freight, supplier location, delivery options, urgency, etc., enforced by each customer’s purchasing rules. Raiven’s approach to best-value sourcing is protected by U.S. Patent No. 12,327,222 B2.

“Contractors should not have to spend hours sorting through emails, spreadsheets, and inconsistent supplier quotes just to figure out the best way to buy,” said Manoj Puthenveetil, CEO of Raiven. “In an industry facing an acute labor shortage, productivity and margin improvement are no longer optional. Raiven 2.0 turns AI into practical business value by helping contractors make better procurement decisions faster, without having to navigate the hype and complexity surrounding AI.”

For building trades contractors, the opportunity is significant. Materials are typically the second-largest cost after labor, yet much of the industry still manages purchasing through fragmented processes involving email, spreadsheets, phone calls, and individual supplier relationships. Raiven 2.0 brings those activities together in one system and converts supplier information into a clear, actionable, auditable buying recommendation.

From quote collection to intelligent purchasing

Self-service sourcing. Purchasing teams can create a request once and distribute it across their preferred supplier network while Raiven helps manage sourcing in the back office and in the field.

AI-powered quote comparison. Raiven reads supplier quotes, normalizes the information, and compares responses, giving users both a recommended sourcing strategy and access to the underlying supplier details, all within their company’s buying policies.

Airstream Services, a rapidly growing multi-trade service business based in Nashville, is among the customers beginning to implement Raiven 2.0 as part of its procurement strategy.

“Saving time and money is an important priority for every service business. As we expand rapidly in the Nashville area, improving our margins through best-value procurement decisions is an important strategy for us. That’s exactly what Raiven 2.0 helps us to do,” said Gus Puga, Owner and CEO of Airstream Services.

Building the Agentic Chief Procurement Office

Raiven 2.0 also establishes the platform foundation for the company’s broader vision: creating an Agentic Chief Procurement Office for the building trades.

Raiven has built an agentic framework to manage sourcing, purchasing, supplier management, spend intelligence, and buying policy, bringing procurement capabilities historically only available to the largest contractors and multi-location building trades businesses.

“Every work order has one part labor and one part material. If you don’t control both, you control neither” said Tracy K. Price, Chairman of the Board of Raiven. “Raiven 2.0 is the realization of the ambition for the patent that we submitted four years ago, and it is now a reality.”

About Raiven

Raiven is the Agentic Chief Procurement Office for the trades, built for electrical, HVAC, and multi-trade contractors. The platform combines trade-specific AI agents, a mobile application, procurement data and insights, Raiven Assist, and a curated supplier network that works alongside contractors’ existing supplier relationships.

At the core is Raiven Best Value™, the trades’ patented system for value-based procurement decisions. Rather than optimizing for the lowest line-item price, Raiven evaluates each purchasing decision across a dynamic mix of factors including price, availability, supplier reliability, lead time, freight, total cost, customer priorities, and more. The result is the best value for the job, not simply the lowest price.

Raiven helps contractors increase the capacity of their existing teams, improve margin control, and gain greater visibility into what they buy, where they buy it, and what it costs. Learn more at Raiven.com.

Media Contact
Jo Jenkins
Raiven
jo.jenkins@raiven.com

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