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The Hindu
25-05-2025
- Health
- The Hindu
MITS students develop portable, AI-powered skin disease detection device
The final-year ECE students of Madanapalle Institute of Technology and Science (MITS) have successfully developed a portable, AI-powered system for early detection of skin diseases. The MITS management said: 'The device leverages Convolutional Neural Networks (CNNs) to classify skin conditions such as melanoma, basal cell carcinoma, and others through a mobile or web interface. Built on Raspberry Pi, the device includes a buzzer, GSM module, and LCD for real-time alerts and display. In recognition of their achievement, the students were felicitated by the management, including Principal C. Yuvaraj and Correspondent N. Vijaya Bhaskar Choudary.
Time Business News
13-05-2025
- Health
- Time Business News
Revolutionizing Diagnostics with AI: The Future of Medical Imaging Software
The integration of artificial intelligence (AI) into healthcare is ushering in a new era of precision, efficiency, and accessibility. One of the most promising applications of AI lies in medical imaging software development, where smart algorithms are helping doctors detect diseases faster and more accurately. From identifying subtle anomalies in MRI scans to automating tumor detection in CT images, AI is not just a support tool—it's becoming a vital part of diagnostic workflows. In this blog, we explore how AI is shaping the future of medical imaging, the benefits it offers, the development process behind these intelligent tools, and key considerations for healthcare innovators. Medical imaging plays a central role in diagnosing conditions like cancer, stroke, cardiovascular disease, and neurological disorders. However, radiologists often face challenges such as: High workload and burnout Complex imaging data Risk of human error Delays in diagnosis These issues can compromise patient care and burden healthcare systems. AI-powered imaging software addresses these pain points by automating repetitive tasks, enhancing image quality, and providing early warning signals for potential issues—ultimately leading to faster, smarter, and more reliable diagnoses. AI in medical imaging leverages machine learning (ML) and deep learning (DL) techniques to interpret visual data in ways that mimic, and sometimes surpass, human expertise. Here's how AI is used: AI models can precisely segment anatomical structures such as organs or tumors from complex scans, reducing the time radiologists spend on manual annotations. Deep learning algorithms can be trained to detect subtle patterns and abnormalities that may not be obvious to the human eye, aiding early diagnosis of diseases like breast cancer, pneumonia, or brain lesions. By analyzing patterns across multiple patient scans and historical data, AI can forecast disease progression, recurrence, and potential complications. AI streamlines operations by triaging critical cases, organizing imaging data, and flagging images that need urgent review, improving efficiency and reducing diagnostic delays. Developing AI-based diagnostic tools requires collaboration between data scientists, healthcare professionals, and software engineers. Here's a typical roadmap: High-quality, annotated datasets are the foundation. Data should be diverse and compliant with healthcare privacy standards like HIPAA or GDPR. Deep learning models such as convolutional neural networks (CNNs) are trained using labeled medical images (e.g., X-rays, MRIs, CT scans). The training process involves feeding data into the model and refining it based on performance metrics like accuracy, sensitivity, and specificity. The AI system must be rigorously tested on unseen datasets to ensure reliability. Clinical validation with expert radiologists is also necessary before deployment. The software must integrate seamlessly with hospital systems such as PACS (Picture Archiving and Communication Systems) or RIS (Radiology Information Systems). AI medical imaging tools must meet regulatory requirements, such as FDA approval or CE marking, to ensure patient safety and clinical efficacy. Adopting AI in medical imaging offers several advantages: Increased Diagnostic Accuracy: AI can detect minute anomalies and provide second opinions to reduce diagnostic errors. AI can detect minute anomalies and provide second opinions to reduce diagnostic errors. Reduced Turnaround Time: Automation speeds up the review and reporting process. Automation speeds up the review and reporting process. Cost-Effective Care: Early diagnosis can lower treatment costs and improve patient outcomes. Early diagnosis can lower treatment costs and improve patient outcomes. Radiologist Support: AI acts as a reliable assistant, helping radiologists focus on complex cases. AI acts as a reliable assistant, helping radiologists focus on complex cases. Scalability: AI systems can handle high imaging volumes, making healthcare more accessible in rural or underserved regions. Despite its promise, AI in medical imaging comes with challenges: Data Privacy: Ensuring patient data is securely stored and used is critical. Ensuring patient data is securely stored and used is critical. Bias in Algorithms: Models trained on limited or non-representative data can lead to biased results. Models trained on limited or non-representative data can lead to biased results. Regulatory Hurdles: Gaining approvals from medical authorities can be time-consuming. Gaining approvals from medical authorities can be time-consuming. Explainability: Clinicians often require transparency on how AI models reach their conclusions—something that 'black box' models don't always provide. To overcome these challenges, developers should prioritize transparency, fairness, and collaboration with healthcare experts throughout the AI development lifecycle. Several AI imaging solutions have already made a global impact: Google Health developed an AI that detects breast cancer in mammograms with accuracy comparable to top radiologists. developed an AI that detects breast cancer in mammograms with accuracy comparable to top radiologists. Aidoc provides real-time triage of radiology scans to identify critical conditions such as brain hemorrhages and pulmonary embolisms. provides real-time triage of radiology scans to identify critical conditions such as brain hemorrhages and pulmonary embolisms. Zebra Medical Vision offers a wide range of AI solutions that support radiologists in interpreting scans with high precision. The development of AI-driven medical imaging software is more than just a technological advancement—it's a healthcare revolution. As diagnostic needs grow more complex and patient demands increase, intelligent tools offer the speed, accuracy, and scalability that modern medicine requires. For healthcare organizations, investing in AI-powered imaging solutions means delivering better patient care, reducing diagnostic errors, and staying ahead in a rapidly evolving digital health landscape. Want to develop smart medical imaging software tailored to your needs? Partner with experienced AI and healthcare software development companies like Infowind Technologies, which specialize in building intelligent, compliant, and scalable diagnostic tools for the future of healthcare. TIME BUSINESS NEWS
Yahoo
31-03-2025
- Business
- Yahoo
Hyperspectral Imaging Market Research 2025: Strategic Acquisitions Shaping the Industry, Integration of On-Chip Thin-Film Filters and Quantum-enhanced Photonic Sensors - Global Forecast to 2029
Hyperspectral Imaging Market Dublin, March 31, 2025 (GLOBE NEWSWIRE) -- The "Hyperspectral Imaging Market" report has been added to Hyperspectral Imaging Market was valued at USD 301.4 million in 2024, and is projected to reach USD 472.9 million by 2029, rising at a CAGR of 9.40%This report analyzes trends in the global hyperspectral imaging market. It provides the global revenue ($ millions) for market segments and regions, using 2023 as the base year, and estimated market data for 2024 through 2029. The report also focuses on emerging technologies and analyzes the vendor landscape. It concludes with profiles of the leading companies in the market. One of the most notable technological developments is the integration of hyperspectral imaging with machine learning (ML) techniques, such as convolutional neural networks (CNNs), to improve the accuracy and speed of freshness detection in perishable food items. Research has shown that this combination can accurately assess the freshness of products such as tilapia filets and apples. Such advances in hyperspectral imaging enable the rapid and non-destructive evaluation of food quality, enabling the food industry to reduce waste, ensure safety, and enhance consumer imaging technologies are being miniaturized and integrated into small satellite platforms, expanding their use in environmental and agricultural applications. Companies such as Pixxel have developed micro-satellites capable of providing high-resolution imaging across numerous spectral bands, offering precise data for monitoring environmental phenomena such as methane emissions and water quality. These innovations, including quantum-dot-enabled systems, are making hyperspectral imaging more affordable and report includes: 59 data tables and 62 additional tables An overview of the global markets for hyperspectral imaging (HSI) technology Analyses of global market trends, with market revenue data for 2023, estimates for 2024, forecast for 2025 and 2026, and projected CAGRs through 2029 Estimates of the market size and revenue growth prospects of the global market, along with a market share analysis by scanning type, offering, technology, spectral range, application, and region Facts and figures pertaining to the market dynamics, technical advances, regulations, innovations, and the impact of macroeconomic factors Insights derived from the Porter's five forces model, as well as global supply chain analysis, and case studies An analysis of patents and emerging technologies in the hyperspectral imaging space Analysis of the industry structure, including companies' market shares, rankings, strategic alliances, M&A activity, and a venture funding outlook Overview of sustainability trends and ESG developments, with emphasis on consumer attitudes, and the ESG scores and practices of leading companies Company profiles of leading players, including Headwall Photonics Inc., Specim Spectral Imaging Ltd., Corning Inc., Resonon Inc., and Cubert GmbH Companies Featured Bayspec Inc. Brimrose Corp. Chnspec Technology (Zhejiang) Co. Ltd. Corning Inc. Cubert GmbH Cytoviva Inc. Exosens Galileo Group Inc. Hansa Luftbild AG Headwall Photonics Inc. Horiba Ltd. IMEC LLA Instruments Malvern Panalytical Ltd. Norsk Elektro Optikk Northrop Grumman Ornet Sdn Bhd Planet Labs PBC Resonon Inc. Specim Spectral Imaging Ltd. Spectir Surface Optics Corp. Ximea GmbH Key Attributes: Report Attribute Details No. of Pages 137 Forecast Period 2024 - 2029 Estimated Market Value (USD) in 2024 $301.4 Million Forecasted Market Value (USD) by 2029 $472.9 Million Compound Annual Growth Rate 9.4% Regions Covered Global Key Topics Covered: Chapter 1 Executive Summary Market Outlook Scope of Report Market Summary Technological Advances and Applications Market Dynamics and Growth Factors Segmental Analysis Regional Insights and Emerging Markets Conclusion Chapter 2 Market Overview Current Market Scenario and Future Expectations Macro-Economic Factors Inflationary Pressures and Interest Rate Impacts Geopolitical Uncertainties Supply Chain Disruptions Affect Sensor Manufacturing Porter's Five Forces Analysis Value Chain Analysis Regulatory Landscape Chapter 3 Market Dynamics Overview Market Drivers Demand for Remote Sensing Technology Hyperspectral Imaging in Agriculture Hyperspectral Imaging in Healthcare and Space Exploration Government Funding for Hyperspectral Imaging Improved Image Quality and Processing Speed Market Restraints High Costs Associated with the Use of Hyperspectral Imaging Requirement for Faster Computers and More Data Storage Market Opportunities Miniaturized Components for Precise Microscopic Identification Sustainable Recycling and Environmental Monitoring AI Integration for Food Quality Assessment New Applications in Autonomous Vehicles and Smart Cities Global Earth Observatories Chapter 4 Emerging Trends and Technologies Overview Emerging Trends Improved Detection of Food Freshness Strategic Acquisitions Shaping the Industry Enhanced Optical Performance and Specialized Applications High-Performance Sensors, User-Friendly Cameras Emerging Technologies Integration of On-Chip Thin-Film Filters Integration of Quantum-enhanced Photonic Sensors Quantum Dot Revolution and NIR Hyperspectral Imaging Patent Analysis Geographical Patterns Significant Patent Grants Key Findings Chapter 5 Market Segmentation Analysis Segmentation Breakdown Market Breakdown by Scanning Type Key Takeaways Spectral Scanning Spatial Scanning Hybrid Scanning Market Breakdown by Offering Key Takeaways Hardware Software and Services Market Breakdown by Technology Key Takeaways Push Broom Tunable Filters Imaging FTIR Whisk Broom Snapshot Imaging Market Breakdown by Spectral Range Key Takeaways VNIR (Visible-Near Infrared) SWIR (Shortwave Infrared) NIR (Near-Infrared) Long-wave Infrared (LWIR) Market Breakdown by Application Key Takeaways Defense and Intelligence Agriculture and Precision Farming Healthcare and Medical Diagnostics Food Quality and Safety Mining and Mineral Exploration Environmental Monitoring Scientific Research and Academia Other Applications Geographic Breakdown Market Breakdown by Region Key Takeaways North America Europe Asia-Pacific Rest of the World Chapter 6 Competitive Intelligence Market Ecosystem Analysis Ranking of Leading Companies Recent Developments Chapter 7 Sustainability and ESG in the Hyperspectral Imaging Industry ESG Issues in Hyperspectral Imaging Market Environmental Issues Social Responsibilities Governance ESG Performance Analysis Environmental Performance Social Performance Governance Performance Status of ESG in the Hyperspectral Imaging Industry Concluding Remarks Chapter 8 Appendix For more information about this report visit About is the world's leading source for international market research reports and market data. We provide you with the latest data on international and regional markets, key industries, the top companies, new products and the latest trends. Attachment Hyperspectral Imaging Market CONTACT: CONTACT: Laura Wood,Senior Press Manager press@ For E.S.T Office Hours Call 1-917-300-0470 For U.S./ CAN Toll Free Call 1-800-526-8630 For GMT Office Hours Call +353-1-416-8900Sign in to access your portfolio
Associated Press
18-03-2025
- Business
- Associated Press
NB Tech Acquisitions Welcomes Dr. Abdul Jabbar as Senior AI & Machine Learning Specialist
Newport Beach, California--(Newsfile Corp. - March 18, 2025) - NB Tech Acquisitions ('NB Tech'), a leader in technology innovation and investment, is pleased to announce the addition of Dr. Abdul Jabbar as the company's new Senior AI & Machine Learning Specialist, further reinforcing its commitment to building an AI-first infrastructure that powers innovation across e-commerce, fintech, and emerging technologies. Dr. Jabbar brings over a decade of academic and industry expertise, with a Ph.D. in Computer Science from The University of Newcastle in Australia. His research and applied work spans advanced deep learning methodologies, including Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and Deep Reinforcement Learning. He has held roles at leading global institutions such as the Allen Institute for AI (USA), Unbox Research, and L2F (Switzerland), and consulted for hedge funds and proprietary trading firms in the quantitative finance space. 'Dr. Jabbar's deep technical acumen and cross-industry experience make him an ideal fit for NB Tech's mission to build intelligent systems that drive real-world results,' said Eric Liboiron, Founder and acting CEO of NB Tech Acquisitions. 'His work will play a pivotal role in shaping the machine learning backbone of our 'Big Engine' platform.' At NB Tech, Dr. Jabbar will lead initiatives that fuse advanced AI with commercial applications, particularly within the company's expanding Ecom Fund ecosystem and its algorithmic trading strategies through the DX1 Fund. His focus will include predictive modeling, data optimization, and the development of scalable AI tools that enhance decision-making, operational efficiency, and investor outcomes. The hire marks a key milestone in NB Tech's 2025 growth strategy, underscoring its commitment to recruiting world-class talent and deploying transformative technologies that keep the company ahead of the curve. About NB Tech Acquisitions Founded in 2021 and headquartered in Newport Beach, California, NB Tech Acquisitions is a private holding company and technology incubator specializing in acquiring and scaling groundbreaking startups across sectors such as artificial intelligence, ecommerce, and cybersecurity. Through strategic investments and innovative solutions, NB Tech drives transformation and creates exceptional value for its stakeholders. Media Contact: Brooke Rhoden Vice President of Investor Relations NB Tech Acquisitions Phone: 949.229.0977 SAFE HARBOR FORWARD-LOOKING STATEMENTS In connection with the safe harbor provisions of the Private Securities Litigation Reform Act of 1995, NB Tech Acquisitions is hereby providing cautionary statements identifying important factors that could cause our actual results to differ materially from those projected in forward-looking statements (as defined in such act). Any statements that are not historical facts and that express, or involve discussions as to, expectations, beliefs, plans, objectives, assumptions or future events or performance (often, but not always, indicated through the use of words or phrases such as 'will likely result,' 'are expected to,' 'will continue,' 'is anticipated,' 'estimated,' 'intends,' 'plans,' 'believes' and 'projects') may be forward-looking and may involve estimates and uncertainties which could cause actual results to differ materially from those expressed in the forward-looking statements. These statements include, but are not limited to, our expectations concerning our ability to attract investors. We caution that the factors described herein could cause actual results to differ materially from those expressed in any forward-looking statements we make, and investors should not place undue reliance on any such forward-looking statements. Further, any forward-looking statement speaks only as of the date on which such statement is made, and we undertake no obligation to update any forward-looking statement to reflect events or circumstances after the date on which such statement is made or to reflect the occurrence of anticipated or unanticipated events or circumstances. New factors emerge from time to time, and it is not possible for us to predict all such factors. Further, we cannot assess the impact of each such factor on our results of operations or the extent to which any factor, or combination of factors, may cause actual results to differ materially from those contained in any forward-looking statements.
