VideoCAD is a large-scale synthetic dataset consisting of over 41K annotated video recordings of CAD operations, generated using an automated framework for collecting high-fidelity UI action data from human-made CAD designs. Comparison with existing datasets shows that VideoCAD offers an order of magnitude higher complexity in UI interaction learning for real-world engineering tasks, having a 20x longer time horizon than other datasets

Computer-aided design (CAD) market revenues have experienced slow but steady growth over the past couple of years, with annual totals increasing from about ***** billion U.S. dollars in 2016 to around *** in 2018. Forecasts suggest that the market will experience increased growth in the coming years, expanding to well over ** billion dollars in annual revenue by 2023. Computer-aided design Unsurprisingly, CAD involves the use of specialized computer software to assist in the design of buildings and machinery. Three-dimensional design accounts for the majority of the industry’s revenue given the technology’s primary use in the fields of architecture and machinery design, but a small yet significant segment also focuses on 2D renderings of projects. The CAD market is highly associated with other similar software types such as computer-aided engineering (CAE) and computer-aided manufacturing (CAM). These various software families are often used in combination at various points throughout the digital design process. CAD companies The biggest names in the CAD market have typically been Autodesk and Dassault, both of which account for around a quarter of the industry’s total market share. In 2019 alone, Autodesk reported over *** million dollars in revenue from its AutoCAD and AutoCAD LT products alone, highlighting the widespread usage of these tools in today’s manufacturing environment.

CAD (CAM) Software Market Outlook

The CAD (CAM) software market size is projected to grow from USD 9.3 billion in 2023 to USD 15.7 billion by 2032, at a compound annual growth rate (CAGR) of 5.8%. This growth is driven by the increasing adoption of advanced manufacturing technologies and the rising demand for precision and efficiency in various industrial applications. The surge in digital transformation initiatives and the integration of CAD (CAM) software with artificial intelligence and machine learning also contribute significantly to market expansion.

Several growth factors are propelling the CAD (CAM) software market. Firstly, the automotive and aerospace industries are increasingly leveraging CAD (CAM) software to enhance design accuracy, reduce production times, and minimize errors. These industries demand complex and precise designs, which can be efficiently managed through advanced CAD (CAM) solutions. Additionally, the growing trend towards electric vehicles and the need for lightweight, fuel-efficient automobiles further bolster the adoption of CAD (CAM) software in automotive manufacturing.

Secondly, the healthcare sector is witnessing a substantial uptake of CAD (CAM) software for designing and manufacturing medical devices. With the rising demand for personalized medical solutions, such as prosthetics and dental implants, the need for precise and customizable designs is more critical than ever. CAD (CAM) software enables the creation of intricate medical devices with high precision, enhancing patient outcomes and driving market growth. Furthermore, the ongoing advancements in 3D printing technology complement the capabilities of CAD (CAM) software, providing a synergistic effect that accelerates market expansion in the healthcare domain.

Another significant growth factor is the increasing adoption of Industry 4.0 principles across various sectors, including industrial equipment and high-tech industries. The integration of CAD (CAM) software with IoT devices, big data analytics, and cloud computing facilitates real-time monitoring, predictive maintenance, and enhanced operational efficiency. This holistic approach to manufacturing not only optimizes production processes but also reduces costs and downtime, making CAD (CAM) software an indispensable tool in modern industrial operations. Consequently, the demand for advanced CAD (CAM) solutions is expected to witness a steady rise in the coming years.

The integration of CAD and PLM Software is becoming increasingly crucial in the modern industrial landscape. By combining computer-aided design (CAD) with product lifecycle management (PLM), businesses can streamline their design and manufacturing processes, ensuring a seamless transition from concept to production. This integration allows for better collaboration among teams, improved data management, and enhanced product quality. As industries strive for greater efficiency and innovation, the adoption of CAD and PLM Software is expected to grow, providing a competitive edge to organizations that embrace these technologies.

Regionally, North America holds a dominant position in the CAD (CAM) software market, primarily due to the strong presence of established manufacturing industries and continuous technological advancements. The region's focus on innovation and early adoption of advanced technologies further accelerates market growth. Europe follows closely, driven by significant investments in aerospace and automotive sectors. The Asia Pacific region is anticipated to experience the highest growth rate, attributed to rapid industrialization, increasing foreign investments, and the growing adoption of smart manufacturing practices in countries like China, India, and Japan.

Component Analysis

The CAD (CAM) software market can be segmented by component into software and services. The software segment dominates the market, driven by the continuous demand for advanced design and manufacturing solutions. CAD (CAM) software provides comprehensive tools for creating detailed 2D and 3D models, simulations, and analyses, enabling manufacturers to streamline their design processes and improve product quality. The software segment is further bifurcated into on-premises and cloud-based solutions, each catering to different organizational needs.

On-premises software solutions remain a preferred choice for large enterprises that require high data security and have substantial IT infrastructure. These so

Detergents play an essential role during the isolation of membrane protein complexes. Inappropriate use of detergents may affect the native fold of the membrane proteins, their binding to antibodies, or their interaction with partner proteins. Here we used cadherin-11 (Cad11) as an example to examine the impact of detergents on membrane protein complex isolation. We found that mAb 1A5 could immunoprecipitate Cad11 when membranes were solubilized by dodecyl maltoside (DDM) but not by octylglucoside, suggesting that octylglucoside interferes with Cad11–mAb 1A5 interaction. Furthermore, we compared the effects of Brij-35, Triton X-100, cholate, CHAPSO, Zwittergent 3-12, Deoxy BIG CHAP, and digitonin on Cad11 solubilization and immunoprecipitation. We found that all detergents except Brij-35 could solubilize Cad11 from the membrane. Upon immunoprecipitation, we found that β-catenin, a known cadherin-interacting protein, was present in Cad11 immune complex among the detergents tested except Brij-35. However, the association of p120 catenin with Cad11 varied depending on the detergents used. Using isobaric tag for relative and absolute quantitation (iTRAQ) to determine the relative levels of proteins in Cad11 immune complexes, we found that DDM and Triton X-100 were more efficient than cholate in solubilization and immunoprecipitation of Cad11 and resulted in the identification of both canonical and new candidate Cad11-interacting proteins.

The global workstation desktop market is experiencing robust growth, driven by increasing demand across diverse sectors. The market, estimated at $25 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033, reaching an estimated $40 billion by 2033. This expansion is fueled by several key factors. The rising adoption of advanced technologies like artificial intelligence (AI), machine learning (ML), and big data analytics across industries such as CAD/CAM, Geographic Information Systems (GIS), and simulation necessitates high-performance computing capabilities readily provided by workstation desktops. Furthermore, the increasing need for enhanced visualization and processing power in sectors like plane image processing and media production contributes significantly to market growth. Geographic expansion, particularly in developing economies with growing technological infrastructure and increasing digitalization, further propels the market forward. The demand for specialized workstations tailored to specific application needs, like universal and dedicated workstations, also contributes to market segmentation and expansion. However, certain restraints are impacting market growth. The high initial investment cost of workstation desktops can be prohibitive for some businesses, especially small and medium-sized enterprises (SMEs). Additionally, the rapid advancement of technology necessitates frequent upgrades, leading to higher overall expenditure. Competition from cloud-based solutions and thin clients presents another challenge. Despite these constraints, the continuous innovation in processing power, graphics capabilities, and energy efficiency, alongside increasing demand for high-performance computing, is expected to ensure the sustained growth of the workstation desktop market in the forecast period. The market will likely witness increased competition among key players like Hewlett Packard Enterprise (HPE), Dell, Lenovo, and others, focusing on innovation and catering to niche market segments.

The global Enterprise CAD Software market size was valued at USD 11.2 billion in 2025 and is projected to grow from USD 15.3 billion in 2023 to USD 33.6 billion by 2033, exhibiting a CAGR of 10.4% during the forecast period 2023-2033. The increasing adoption of digital technologies in the manufacturing, construction, and automotive industries, coupled with the growing demand for efficient and accurate product design and development, is driving market growth. The cloud-based segment dominated the market in 2025, accounting for over 60% of the revenue share, and is expected to maintain its dominance throughout the forecast period. The increasing adoption of cloud-based solutions due to their benefits such as flexibility, scalability, and lower upfront costs is contributing to the growth of this segment. In terms of application, the large enterprises segment held the largest market share in 2025, owing to their high adoption of advanced design and simulation technologies for product development. However, the small and medium enterprises (SMEs) segment is expected to grow at a faster CAGR during the forecast period, driven by the growing adoption of CAD software by SMEs to streamline their design processes and improve productivity. The North American region accounted for the largest market share in 2025, and is expected to continue to hold the largest share throughout the forecast period. The presence of a large number of manufacturing and automotive companies in the region, coupled with the high adoption of advanced design technologies, is driving the growth of the market in this region. The Asia Pacific region is expected to grow at the highest CAGR during the forecast period, driven by the growing industrialization and urbanization in the region.

Recent advancements in AI, driven by big data technologies, have reshaped various industries, with a strong focus on data-driven approaches. This has resulted in remarkable progress in fields like computer vision, e-commerce, cybersecurity, and healthcare, primarily fueled by the integration of machine learning and deep learning models. Notably, the intersection of oncology and computer science has given rise to Computer-Aided Diagnosis (CAD) systems, offering vital tools to aid medical professionals in tumor detection, classification, recurrence tracking, and prognosis prediction. Breast cancer, a significant global health concern, is particularly prevalent in Asia due to diverse factors like lifestyle, genetics, environmental exposures, and healthcare accessibility. Early detection through mammography screening is critical, but the accuracy of mammograms can vary due to factors like breast composition and tumor characteristics, leading to potential misdiagnoses. To address this, an innovative CAD system leveraging deep learning and computer vision techniques was introduced. This system enhances breast cancer diagnosis by independently identifying and categorizing breast lesions, segmenting mass lesions, and classifying them based on pathology. Thorough validation using the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) demonstrated the CAD system’s exceptional performance, with a 99% success rate in detecting and classifying breast masses. While the accuracy of detection is 98.5%, when segmenting breast masses into separate groups for examination, the method’s performance was approximately 95.39%. Upon completing all the analysis, the system’s classification phase yielded an overall accuracy of 99.16% for classification. The potential for this integrated framework to outperform current deep learning techniques is proposed, despite potential challenges related to the high number of trainable parameters. Ultimately, this recommended framework offers valuable support to researchers and physicians in breast cancer diagnosis by harnessing cutting-edge AI and image processing technologies, extending recent advances in deep learning to the medical domain.

Recent advancements in AI, driven by big data technologies, have reshaped various industries, with a strong focus on data-driven approaches. This has resulted in remarkable progress in fields like computer vision, e-commerce, cybersecurity, and healthcare, primarily fueled by the integration of machine learning and deep learning models. Notably, the intersection of oncology and computer science has given rise to Computer-Aided Diagnosis (CAD) systems, offering vital tools to aid medical professionals in tumor detection, classification, recurrence tracking, and prognosis prediction. Breast cancer, a significant global health concern, is particularly prevalent in Asia due to diverse factors like lifestyle, genetics, environmental exposures, and healthcare accessibility. Early detection through mammography screening is critical, but the accuracy of mammograms can vary due to factors like breast composition and tumor characteristics, leading to potential misdiagnoses. To address this, an innovative CAD system leveraging deep learning and computer vision techniques was introduced. This system enhances breast cancer diagnosis by independently identifying and categorizing breast lesions, segmenting mass lesions, and classifying them based on pathology. Thorough validation using the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) demonstrated the CAD system’s exceptional performance, with a 99% success rate in detecting and classifying breast masses. While the accuracy of detection is 98.5%, when segmenting breast masses into separate groups for examination, the method’s performance was approximately 95.39%. Upon completing all the analysis, the system’s classification phase yielded an overall accuracy of 99.16% for classification. The potential for this integrated framework to outperform current deep learning techniques is proposed, despite potential challenges related to the high number of trainable parameters. Ultimately, this recommended framework offers valuable support to researchers and physicians in breast cancer diagnosis by harnessing cutting-edge AI and image processing technologies, extending recent advances in deep learning to the medical domain.

Recent advancements in AI, driven by big data technologies, have reshaped various industries, with a strong focus on data-driven approaches. This has resulted in remarkable progress in fields like computer vision, e-commerce, cybersecurity, and healthcare, primarily fueled by the integration of machine learning and deep learning models. Notably, the intersection of oncology and computer science has given rise to Computer-Aided Diagnosis (CAD) systems, offering vital tools to aid medical professionals in tumor detection, classification, recurrence tracking, and prognosis prediction. Breast cancer, a significant global health concern, is particularly prevalent in Asia due to diverse factors like lifestyle, genetics, environmental exposures, and healthcare accessibility. Early detection through mammography screening is critical, but the accuracy of mammograms can vary due to factors like breast composition and tumor characteristics, leading to potential misdiagnoses. To address this, an innovative CAD system leveraging deep learning and computer vision techniques was introduced. This system enhances breast cancer diagnosis by independently identifying and categorizing breast lesions, segmenting mass lesions, and classifying them based on pathology. Thorough validation using the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) demonstrated the CAD system’s exceptional performance, with a 99% success rate in detecting and classifying breast masses. While the accuracy of detection is 98.5%, when segmenting breast masses into separate groups for examination, the method’s performance was approximately 95.39%. Upon completing all the analysis, the system’s classification phase yielded an overall accuracy of 99.16% for classification. The potential for this integrated framework to outperform current deep learning techniques is proposed, despite potential challenges related to the high number of trainable parameters. Ultimately, this recommended framework offers valuable support to researchers and physicians in breast cancer diagnosis by harnessing cutting-edge AI and image processing technologies, extending recent advances in deep learning to the medical domain.

Proteins significantly altered in subjects with CAD compared with subjects without CAD.

Due to the COVID-19 pandemic and the imminent collapse of healthcare systems following the excessive consumption of financial, hospital, and medicinal resources, the World Health Organization (WHO) changed the alert level on the COVID-19 pandemic from high to very high. Meanwhile, the world began to favor less expensive and more precise COVID-19 detection methods. Machine vision-based COVID-19 detection methods especially Deep learning as a diagnostic technique in the early stages of the disease have found great importance during the pandemic.

This is a large public COVID-19 (SARS-CoV-2) lung CT scan dataset, containing total of 8,439 CT scans which consists of 7,495 positive cases (COVID-19 infection) and 944 negative ones (normal and non-COVID-19). Data is available as 512×512px PNG images and have been collected from real patients in radiology centers of teaching hospitals of Tehran, Iran. The aim of this dataset is to encourage the research and development of effective and innovative methods such as deep CNNs which are able to identify if a person is infected by COVID-19 through the analysis of his/her CT scans. As a baseline for this dataset we used a CNN-based approach inspired by transfer learning which we could achieve an accuracy of 99.61% which is very promising.

You may access the related paper at: Deep Convolutional Neural Network–Based Computer-Aided Detection System for COVID-19 Using Multiple Lung Scans: Design and Implementation Study Code is also available at: https://github.com/MehradAria/COVID-19-CAD

Please kindly cite as: Ghaderzadeh M, Asadi F, Jafari R, Bashash D, Abolghasemi H, Aria M. "Deep Convolutional Neural Network–Based Computer-Aided Detection System for COVID-19 Using Multiple Lung Scans: Design and Implementation Study" J Med Internet Res 2021;23(4):e27468 URL: https://www.jmir.org/2021/4/e27468 DOI: 10.2196/27468 PMID: 33848973

Aria M, Ghaderzadeh M, Asadi F, Jafari R. "COVID-19 Lung CT Scans: A large dataset of lung CT scans for COVID-19 (SARS-CoV-2) detection." Kaggle (2021). DOI: 10.34740/kaggle/dsv/1875670.

Mean spirometry and IOS measurements in non-CAD and CAD patients.