International Research Award on Computer Vision: September 2024

Sunday, September 29, 2024

AI-powered computer vision accelerates innovation

Few industries are as important to the wellbeing of people than those that produce the medical devices and drugs that enhance and save lives. And few technologies will be as transformative as artificial intelligence as it touches more and more industries, including life sciences.

No doubt, the use of AI in the medical device and pharmaceutical industries is growing. Advances in computer vision and machine learning are helping companies get medical devices and drugs to market in a more reliable and safe way, and sometimes at a reduced cost.

Pharmaceutical companies, such as J&J, GSK, AstraZeneca, Novartis, Pfizer, Sanofi, Eli Lilly, and others, have made significant investments in AI technology, including equity investments, acquisitions of, or partnerships with, AI-focused companies, building internal capabilities, or a combination of approaches, reports BiopharmaTrend.com. Also, as more AI-based tools and devices are approved, providers can use them in their work.

Images in AI

Computer vision, which enables software to analyse images, is a form of AI that will be used in every industry to make products and services better, more quickly. In the life sciences industries, AI and computer vision will be game-changing technologies that will have as many uses as imaging allows.

For instance, by gathering images of manufacturing defects, users can train an AI model to identify defects and negate the need for manual inspections, while improving quality and process speed. That can help life sciences companies identify defects faster and allow for continuous improvement of manufacturing processes.

Companies are already using computer vision platforms to classify pills, inspect vials, conduct quality and assurance for packaging, or find and eradicate defects in medical device components. With a computer vision platform, visual inspections can happen faster and with greater reliability than if done manually. For one thing, an AI system won’t lose focus, as humans sometimes do.

OmniAb, for instance, leverages computational, hardware-based, and genetic technologies to enable rapid development of innovative therapeutics. By automating a manual visual review, it increased its inspection throughput by up to 10 times and found up to 30% more potential objects of interest than by visual inspection.

“Using the latest AI-based technologies will not only reduce the time needed for the products to come to the market, but will also improve the quality of products and the overall safety of the production process, and provide better utilisation of available resources, along with being cost-effective, thereby increasing the importance of automation,” concludes a study published in Drug Discovery Today.

Regulating software tools

These pharmaceutical and medical device companies also face regulations that - while intended to boost safety of medical devices and drugs - make it harder for them to deploy the latest technology advancements in the production of these products.

As such, medical device and pharmaceutical companies constantly need to balance the desire to move fast with the need to meet Good Manufacturing Practices (GMP), which are intended to ensure that products perform as expected, specifically those contained in the Code of Federal Regulations, Title 21, Food and Drugs (21CFR).

The FDA regulates medical devices and drug development. In the interest of safety, it mandates that all software tools be validated, meaning they be checked and tested to ensure that they will perform a certain way all the time. This helps maintain the safe production and delivery of medical devices and drugs.

However, software validation can also take months and require future updates as tools change, which is frequently the case.

In today’s world, software changes too fast for companies to continually validate. As a result, they might:Miss innovations in tools because they don’t, or can’t, regularly validate their tools.
Put software on-premise and then freeze it and miss out on the benefits of the cloud.
Continually validate, which is time-consuming and costly.

Balancing speed and safety

Validation does not have to occur after each software release update. However, each release has to be judged for impact. If a change affects a regulated function, it has to be validated.

For FDA-regulated companies, this means that the ability to adopt new software advancements presents opportunities to continuously improve what they do. But the frequency of software advances also presents a challenge because of the validation requirements. As more vendors create cloud-based AI solutions that pharma and medical device makers will want to use, due to a number of benefits that come from cloud offerings, validation requirements may rise because cloud-based technologies are often evolving rapidly.

In the MedTech industry, validation costs range between 1 and 1.5 times the cost of implementation of software used to support production, automation, and quality systems, found Axendia, an analyst firm. The “medical device industry lags in implementation of automated systems and new technologies, due to lack of clarity, outdated compliance approaches, and perceived regulatory burden,” Axendia stated.

Companies need to meet FDA regulations to avoid being cited for being out of compliance. Such citations can be costly in terms of remediation and reputation.

Most likely, FDA regulations and guidelines will always be under review and patient safety must remain paramount. However, companies can reduce validation time and cost factors by looking for:Products that have controlled release cycles. By knowing when a software tool will be updated, companies can document, test, and validate upcoming features before the new software is launched, so that companies can readily deploy it. Also, by leveraging pre-built validation documentation, teams can focus solely on the execution of the validation and greatly speed up the entire process.
Partners that know the validation ropes. This can help companies scale more quickly and effectively, reducing validation timeframes from months to weeks because partners have expertise on validation requirements, including documentation. Also, such partners can quickly retrain and revalidate an AI model if drug or medical device makers change their manufacturing processes or make changes to devices.

Democratising access to AI

Complying with upfront and ongoing validation will only get more complex as AI tools and computer vision platforms get more numerous.

But the payoff will be worth it. Every new technology takes time to fold into existing processes. AI, starting with computer vision, is a revolutionary tool for medical device, pharmaceutical, and life sciences industries, and we’ll see rapid innovation in the coming years as access to AI becomes more democratised.

The faster the benefits of computer vision and other AI tools get deep into the pharma and medical device industries, the faster companies and consumers will both benefit.

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Saturday, September 28, 2024

Zebra Technologies Adds New Deep Learning Tools to Aurora Machine Vision Software

Zebra Technologies announces a series of advanced AI features enhancing its Aurora machine vision software to provide deep learning capabilities for complex visual inspection use cases.

Sixty-one percent of manufacturing leaders globally expect AI to drive growth by 2029, according to Zebra’s 2024 Manufacturing Vision Study. Another Zebra report on AI in the Automotive industry found that AI, such as deep learning, is being used across the automotive supply chain, but users want their AI doing more – these new features respond to the needs of industry.

Zebra’s Aurora software suite with deep learning tools provides powerful visual inspection solutions for machine and line builders and engineers, programmers and data scientists in the automotive, electronics and semiconductor, food and beverage and packaging industries. The suite features no code deep learning optical character recognition (OCR), drag and drop environments, and extensive libraries that allow users to create solutions to solve complex use cases that traditional rules-based systems struggle to address.

“Manufacturers across many industries face longstanding quality issues and new challenges with advances in materials and sectors such as automotive and electronics,” said Donato Montanari, Vice President and General Manager, Machine Vision, Zebra Technologies. “They are looking for new solutions that complement and expand their current toolbox with AI capabilities needed for more effective visual inspection, particularly in complex use cases.”

Aurora Design Assistant

Users of Zebra’s Aurora Design Assistant integrated development environment can create applications by constructing and configuring flowcharts instead of writing traditional program code. The software also enables users to design a web-based human-machine interface (HMI) for the applications.

The software now comes with deep learning object detection and the latest version of the Aurora Imaging Copilot companion application with a dedicated workspace for training a deep learning model on object detection. Separate add-ons are available for training a deep learning model with an NVIDIA GPU card and running a deep learning model to perform inference or prediction on an NVIDIA GPU and Intel integrated GPU, respectively.

Aurora Vision Studio

Machine and computer vision engineers using Aurora Vision Studio can quickly create, integrate, and monitor powerful machine vision applications. Its advanced and hardware-agnostic software provides an intuitive graphical environment for the creation of sophisticated vision applications without the need to write a single line of code. It has a comprehensive set of over 3,000 proven and ready-to-use filters, enabling machine and computer vision engineers to design customised solutions in a simple, three-step workflow: design the algorithm, create a custom local HMI or on-line Web HMI and deploy it to a PC-based industrial computer.

A deep learning toolchain has been switched to a new training engine with mechanisms for training data balancing which leads to better training results on low quality datasets. Training is now faster and more repeatable, and the deep learning add-on is compatible with Linux systems, for inference only.

Aurora Imaging Library

Zebra’s Aurora Imaging Library software development kit is for experienced programmers coding vision applications in C++, C# and Python. It includes a broad collection of tools for processing and analysing 2D images and 3D data using traditional rules-based methods as well as those based on deep learning.

The latest additions expand its capabilities with the introduction of anomaly detection tools using deep learning for defect detection and assembly verification tasks where the aim is to find abnormalities. Unlike other available deep learning tools, the training is unsupervised, only needing normal references.

The deep-learning-based OCR tool uses a pre-trained deep neural network model to read characters, digits, punctuation marks and certain symbols without the need to specify or teach it specific fonts. The deep learning-based OCR tool includes string models and constraints to enable more robust and relevant reading.

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Thursday, September 26, 2024

Wired To Micrometer Precision With Integrated Image Processing

Wire bonding is a key process in semiconductor production. Extremely fine wires with diameters of 15 to 75 micrometers are used to create tiny electrical connections between a semiconductor chip and other components. The distances between the bond wires are often less than 100 micrometers. Any deviation, however small, can lead to connection errors. Wire bonding therefore requires the highest precision and forms the basis for the production of high-performance electronics, which are used in many different applications. F&S BONDTEC Semiconductor GmbH from Austria, relies on image processing technology with industrial cameras from IDS Imaging Development Systems for the precise determination of wire positions and for quality assurance.

Wire bonders are available with various degrees of automation. With manual devices, each bond position must be approached manually before the corresponding connections can be made. Semi-automatic machines automatically position the wire after the first bond to create a wire bridge. Fully automatic machines use a structure recognition system to determine the position of the chips. Here, the production of all wire bridges is completely automatic. The operator only has to change the wire or tool on the bonder occasionally and take care of loading and unloading.

F&S Bondtec uses image processing with IDS industrial cameras for various tasks in the production process, especially in the semi-automatic machines of the 56i series and the automatic wire bonders of the 86 series. “Our wire bonds connect previously placed microchips or other components with different contact points on printed circuit boards and breathe life into the chips. However, positional inaccuracies of the components can occur during the upstream processes. Our machines have to determine these positional inaccuracies using the IDS camera image and our own image recognition software and update the wire bond positions accordingly,” explains Johann Enthammer, Managing Director and CTO at F&S Bondtec.

For each bonding process, parameters such as ultrasonic amplitude, force, time or the movement sequence for setting up the bonding bridges must also be programmed in advance. The camera’s image feed is also used when creating these programs. For example, you can drag a wire in the live image and change its position. The axes can also be adjusted by clicking on the image.

On the software side, the Austrian company relies on a specially developed image recognition library that works with position/pixel mapping, greyscale recognition and edge detection, for example.

Visual Assessments of Bond Connections

Once the bonding process is complete, the camera is used again, as Johann Enthammer explains: “After welding, the wire bonds are visually checked by the operator via the camera image. Among other things, the position and shape of the bond bridges are assessed. The camera image therefore has more than just one function during the bonding process.”

Between one and seven industrial cameras are used per system. Depending on the type, these can be the particularly compact and cost-effective uEye XCP models. At just 29 x 29 x 17 millimeters, they are the smallest housed IDS cameras with C-mount and have a completely enclosed die-cast zinc housing. Their screw-type USB micro-B connection and compatibility with the Vision Standard (U3V / GenICam) simplify integration. F&S Bondtec also uses uEye CP cameras. These tiny powerhouses offer maximum functionality with extensive pixel pre-processing and are also perfect for multi-camera systems thanks to the internal 120 MB image memory for buffering image sequences. Users can choose from a large number of modern CMOS sensors. They also score points with a compact housing measuring just 29 x 29 x 29 millimeters.

Camera Selection

The small design of the models and the large number of different sensors were important criteria when selecting the camera, as was the low thermal expansion. However, the free IDS peak software development kit with all the programming interfaces and software tools required for operating and programming the cameras was also crucial. Easy-to-understand convenience functions ensure an intuitive programming experience and quick and simple commissioning of the industrial cameras.

Johann Enthammer confirmed: “The driver shows very stable runtime behavior. The easy-to-program API and the plug and play functions with running software convinced us. This is because there are many different use cases for our systems that can be implemented with the API without any problems. Our machines can be equipped with up to seven different bond heads. A different IDS camera can be integrated in each one.”

The wire bonders from F&S Bondtec ensure stable connections in semiconductor production. With the help of integrated image processing, the manufacturing quality and productivity of the systems can be further increased and rejects avoided. At the same time, the cameras make work easier for the operators. In addition to standard products, the company develops special machines and customized software solutions that also use AI models. “We definitely see a lot of potential for the use of artificial intelligence in our applications in the future,” says Johann Enthammer. Image processing opens up completely new potential, especially in conjunction with AI, particularly in terms of efficiency, precision and quality.

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Wednesday, September 25, 2024

The Role of Computer Vision in Robotics

As robots take on more sophisticated roles in diverse environments such as manufacturing, healthcare, and autonomous vehicles, computer vision becomes crucial. It acts as the "eyes" of these machines, allowing them to perceive and interact with their surroundings. This capability is key to ensuring robots can carry out tasks with the accuracy, efficiency, and flexibility needed to adapt to different situations.

From Blind Machines to Visionary Robots

The integration of computer vision into robotics represents a significant milestone in the evolution of both fields. Early robotics systems predominantly relied on pre-programmed instructions, lacking the capacity to adapt to dynamic environmental conditions.

Nevertheless, the introduction of computer vision in the late 20th century marked a major turning point in robotics. However, early computer vision systems were hampered by the limited computational power and primitive algorithms of the time. These constraints led to slow and often inaccurate systems capable of performing only basic tasks like edge detection and simple object recognition.

The field of computer vision has since undergone a dramatic transformation through advancements in machine learning (ML), particularly in the domain of deep learning. Neural networks, particularly convolutional neural networks (CNNs), have enabled robots to perform complex visual tasks such as real-time object detection, scene understanding, and facial recognition with unprecedented accuracy. These advances have revolutionized robotics by allowing robots to operate independently in uncontrolled environments, using real-time visual data to make decisions. This evolution continues today, driven by ongoing research and technological innovations.
Fundamentals of Computer Vision in Robotics

Computer vision in robotics relies on several fundamental principles that allow machines to interpret visual data. Image processing, a key principle, involves converting raw visual data from cameras or sensors into a digital format suitable for robot analysis. This entails various techniques like filtering, edge detection, and image segmentation, which assist in isolating salient features from an image.2

Another fundamental component is object detection, where the robot identifies and classifies elements within its surroundings. This is facilitated through machine learning algorithms, particularly deep neural network models, which are trained on extensive datasets to discern patterns and attributes. Once trained, these models can accurately recognize and categorize objects in real time, even in intricate and crowded environments.2

Depth perception is another essential element, enabling robots to understand the distance and spatial relationships between objects. This is particularly crucial for tasks requiring precise movements, such as grasping objects or navigating through an environment. Depth perception is often achieved through stereo vision, where two cameras capture different angles of the same scene, or through sensors like light detection and ranging (LiDAR), which measures the distance to objects using laser pulses.2

These fundamental principles underpin how computer vision enhances robotic capabilities, making them more autonomous, intelligent, and adaptable to various tasks.
Object Detection and Recognition in Robotics

Object detection and recognition are central to the role of computer vision in robotics, enabling robots to identify and interact with objects in their environment. These capabilities are essential for tasks like autonomous navigation, object manipulation, and inspection. Recent breakthroughs in deep learning have significantly improved the accuracy and speed of object detection algorithms, making them more reliable and effective for practical, real-world applications.3

In autonomous vehicles, object detection and recognition are crucial for ensuring both safety and effective navigation. These systems enable vehicles to identify pedestrians, other vehicles, traffic signs, and obstacles, allowing them to make critical real-time decisions that are essential for safe driving. This underscores the vital role of computer vision in robotics, where precise and dependable perception is key to achieving fully autonomous operation.3
Navigation and Mapping

Visual perception is another essential element for robotic systems, enabling them to navigate and map their surroundings effectively. A key application in this area is Simultaneous Localization and Mapping (SLAM), which allows robots to build detailed representations of unknown environments while continuously estimating their own location within those spaces. This capability is especially important for autonomous robots operating in dynamic and unstructured settings, where they must adapt to changes and navigate complex environments with precision.4

Additionally, vision-based navigation systems have played a crucial role in the development of autonomous delivery robots. These robots rely on computer vision to navigate through bustling urban environments, skillfully avoiding obstacles and determining the most efficient routes to their destinations. The continuous improvement of these systems underscores the importance of computer vision in advancing robotic navigation and mapping capabilities, making autonomous deliveries more reliable and effective in real-world settings.

Human-Robot Interaction (HRI)

The incorporation of computer vision in robotics has proven crucial for enabling effective HRI. For robots to work effectively alongside humans, they must be able to understand and respond to human actions, gestures, and emotions. Computer vision enables robots to interpret visual cues from humans, facilitating more natural and intuitive interactions.5

Gesture recognition is another vital application of computer vision in HRI. By detecting and interpreting hand gestures, robots can be controlled more intuitively, improving their usability across a wide range of environments, from industrial settings to smart homes. The ability of robots to understand and respond to human gestures and emotional cues highlights the crucial role of computer vision in creating more interactive and user-friendly robotic systems. This capability not only enhances the efficiency of robots but also makes them more adaptable to human needs, fostering smoother and more natural interactions.5

Quality Control and Inspection

In manufacturing, computer vision is increasingly being utilized for quality control and inspection. This technology ensures that products meet precise standards by detecting imperfections that might be overlooked by human inspectors. Automated inspection systems equipped with computer vision capabilities allow for fast and accurate product analysis, enabling the real-time identification of defects. This enhances the overall efficiency and reliability of manufacturing processes, ensuring that only high-quality products reach the market.6

Research shows that deep learning models are highly effective at identifying flaws in electronic components on production lines. This approach greatly improves defect detection accuracy, reducing the reliance on manual inspection and boosting overall production efficiency. The application of this technology isn't confined to electronics; it also spans industries such as automotive, pharmaceuticals, and food processing.

The capability of computer vision systems to inspect products with remarkable precision, often surpassing human capabilities, highlights their crucial role in enhancing quality control processes. As these systems continue to evolve, they are set to become even more integral to manufacturing, ensuring that products consistently meet the highest quality standards with greater efficiency and reliability.
Breakthroughs in Computer Vision for Robotics

The field of computer vision in robotics is continuously evolving, with new research pushing the boundaries of what robots can achieve. This section highlights the ongoing advancements in this area.

One such study, published in IEEE Sensors Journal, introduced an innovative approach to vision-based grasping in robotics. The researchers developed a deep learning model that enables robots to skillfully grasp objects in cluttered environments by predicting the optimal grasp points using visual data. This research has important implications for industrial automation, where precise handling of a diverse range of objects is crucial. The ability of robots to accurately determine and execute the best grasp in complex settings enhances their efficiency and versatility in tasks such as assembly, packaging, and material handling, making this advancement a significant step forward in robotic automation.7

Another breakthrough study published in IEEE investigated the use of computer vision in medical robotics, specifically focusing on minimally invasive surgery. The researchers developed a real-time visual guidance system designed to enhance a surgeon's ability to maneuver instruments within the human body with greater precision. This system significantly reduces the risk of errors during surgery, leading to improved patient outcomes. This advancement marks a significant leap forward in the application of robotics in healthcare, highlighting the growing potential of computer vision to transform medical procedures and enhance surgical accuracy.8

Another noteworthy article in Scientific Reports explored the development of a vision-based robotic system designed for agriculture. This system leverages advanced computer vision algorithms to autonomously identify and harvest fruits, optimizing yield while significantly reducing labor costs. The research highlights the potential of computer vision in agriculture, offering a path toward more efficient and sustainable farming practices. By automating the harvesting process, this technology not only improves productivity but also addresses labor shortages, opening the door to a new era of agricultural innovation.

Future Prospects and Conclusion

The future of computer vision in robotics is looking promising, with advancements in deep learning and hardware set to significantly enhance its capabilities. As efficiency improves, computer vision will become increasingly integral to robotics, enabling machines to perform complex tasks with greater autonomy. The integration of AI technologies such as natural language processing and reinforcement learning, coupled with innovations like neuromorphic computing, will lead to more powerful and energy-efficient systems, expanding their influence across various industries.

In conclusion, computer vision is a foundational element of modern robotics, enabling machines to perceive, understand, and interact with their environments in ways that were once the stuff of science fiction. As the field continues to evolve, computer vision will play an even more critical role in shaping the future of robotics, driving innovation across a wide range of industries and fundamentally transforming how people live and work.

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THE EVOLUTION OF GENERATIVE AI: CAPABILITIES, FUTURE, AND IMPLICATIONS FOR CLINICAL RESEARCH

What is generative AI?

Generative AI, in simple terms, is a type of artificial intelligence that can create things on its own, like writing text, generating images, or even composing music. It’s like having a computer program that can come up with new ideas and create content without human input. Generative AI learns from existing data and uses that knowledge to generate new and creative content, making it useful in various fields, from writing stories to designing artwork.

The evolution of generative AI
The early days

The initial forms of generative AI were very basic. It could barely suggest some random words and generate single sentences based on traditional machine learning algorithms which were not very useful. The main challenge was to make these systems ‘learn’ like a human does. Examples are the phone text and Gmail.

Deep learning and neural networks

The innovation in AI has advanced rapidly with the advent of deep learning and neural networks, which mimic the human brain’s structure and functions. This allowed AI models to ‘learn’ from data much more efficiently.

GPT and beyond

Fast-forward to today, and we have mind-bogglingly advanced models like GPT (Generative Pre-trained Transformer). These models can write articles, hold a conversation, keep contextual understanding, provide multilingual support, and even undertake logical reasoning.
Capabilities

So, what can modern generative AI do?

Content creation: Write articles, stories, or even generate artwork.
Data simulation: Generate realistic datasets for testing.
Personal assistants: Help in automating tasks and answering queries.
Language translation: Translate languages with high accuracy.
Write code: It can write new code, optimize the existing code, debug code.
Create data visualizations: Helps in creating the visualizations for the given dataset.
The future ahead

As promising as Generative AI is, it remains in its infancy, and we’ve barely scratched the surface. It’s very similar to exploring the capabilities of the human brain; we have yet to discover its full potential. Unlocking meaningful and consistent responses from Generative AI requires asking the right questions or providing the proper prompts. Organizations worldwide are recognizing the importance of Generative AI and the pivotal role of prompt engineers. These specialized individuals possess the necessary skills to interact with AI effectively, ensuring relevant and accurate responses.

With this exciting landscape in mind, let’s contemplate what the future holds:Ethical considerations: As AI gets better at generating content, questions about misinformation and data privacy will become more crucial.

Collaboration with humans: AI will work alongside humans to create even more sophisticated content.
Adaptability: Future AI will adapt to individual user needs more efficiently.
Role of generative AI in clinical Research

Generative AI holds the potential to revolutionize the healthcare industry, especially in the realms of clinical research and trials. Some areas where it can make a significant impact include:

Conclusion

Generative AI has come a long way from its humble beginnings. With its ever-expanding capabilities, it promises a future where machines can aid human creativity and problem-solving in unprecedented ways. The potential applications in clinical research and trials are particularly exciting, promising faster, more efficient, and more ethical healthcare solutions.

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Tuesday, September 24, 2024

How to Use Machine Learning for Weather Predictions

Machine learning (ML) is the latest buzz. In fact, it has been a buzz for a year or two from now. Everyone and everywhere are talking about its capabilities. Now, it is at the forefront when ChatGPT and other generative AI tools are helping the mass in drafting emails and solving homework. It is also at the forefront to sophisticated image models that generate photos and edit photos as well. In fact, it is at the forefront of technological innovation. Most of the attention today has been on the consumer-focused applications and amid such a scenario a quieter revolution has been unfolding in a more unexpected area. It is weather forecasting.

Rise of ML in Weather Forecasting

Weather forecasting relied heavily on numerical models in traditional methods. The models used mathematical equations to predict future weather conditions. ML today has emerged differently and it is promising of course. The ML tools are enhancing weather forecasts. ML techniques have been explored for Earth system modeling at the European Centre for Medium-Range Weather Forecasts (ECMWF). It has been particularly used in neural networks to integrate satellite observations better.

ECMWF has been experimenting with ML-based weather forecasting since 2018. The initial attempts were modest and basically used simple models and low-resolution data. The early models witnessed some success, but were nowhere near as accurate as the sophisticated numerical models that ECMWF already used. Hence, the consensus was that ML could be an interesting research tool. It was unlikely to replace traditional methods in near future.

Rapid Evolution

The landscape changed rapidly between February 2022 and April 2023. Several papers from large tech giants including NVIDIA, Huawei and Google DeepMind demonstrated some improvements. Gradually, the new models started to rival the skill of ECMWF’s traditional models.

Efficiency is catchy and remarkable about the ML models. The models require just a single GPU and thereafter can produce a forecast in just about minute of time. It also uses just a fraction of the energy that traditional models consume.

How ML Models Work

The current generation of ML models for weather forecasting relies heavily on traditional numerical models for training and validation data. ECMWF’s Integrated Forecasting System (IFS) is a good example here. It is used to create the datasets that these ML models learn from.

ML models are starting to show impressive results despite reliance on traditional data. For instance, Huawei’s Pangu-Weather and NVIDIA’s FourCastNet have been able to match the performance of ECMWF’s IFS in several cases. The ML models have proven to be competitive with traditional forecasting methods when evaluated with metrics like root mean square error (RMSE) and anomaly correlation coefficient (ACC).

Challenges of ML-Based Forecasting

The ML-based weather forecasting is not free from challenges. The challenges need to be addressed and one main issue is that the ML models are trained to minimize errors across all types of weather conditions. This can sometimes lead to overly smoothed predictions. Hence, the ML models can be generally accurate, but they may not always capture extreme weather events with the same intensity as traditional models. For example, in the case of tropical cyclone Freddy in 2023, Pangu-Weather accurately predicted the path of the storm, but simultaneously failed to fully capture the intensity of the cyclone-related winds.

The limitation is not a flaw in the ML models. It is a result of the way the models are trained. Researchers are experimenting with new training methods to encourage the models to make more extreme predictions.

Future of Weather Forecasting

The potential benefits of ML-based weather forecasting are enormous despite the challenges. The models are highly efficient and could also enable the creation of high-resolution weather ensembles with hundreds of members.

Moreover, the ML models can easily be integrated with traditional forecasting methods. The models can be used in providing rapid initial forecasts and hence may also be refined by using more detailed numerical models. The hybrid approach may even help in validating and improving both types of models.

Embracing the Future

ML is evolving continuously and it is becoming clear that it has the potential to revolutionize weather forecasting. However, it does not mean that the traditional models are becoming obsolete. Meteorological centers have access to vast amounts of data and this makes them ideally perfect to lead the development of new ML-based forecasting techniques.

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Monday, September 23, 2024

Machine learning early warning system reduces non-palliative deaths in general medicine unit

Background

Estimating, preventing, and reacting to the clinical deterioration of hospitalized individuals is critical to increasing patient safety. Unidentified clinical deterioration is the primary cause of unnecessary admissions to the intensive care unit (ICU), resulting in prolonged stays and increased fatality. Despite the extensive usage of prediction tools, the evidence for their usefulness is inconsistent.

A Kaiser Permanente study of 19 hospitals in Northern California revealed that an automated risk estimation model with remote nurse monitoring and on-the-ground actions by quick response teams reduced 30-day mortality by 16%. However, the technological and clinical characteristics of advanced early alert systems that might enhance clinical outcomes are unknown.

About the study

The present study investigated whether CHARTwatch could improve patient deterioration-related clinical outcomes.

The program predicts patient deterioration by using real-time data from electronic medical records. The time-aware multivariate adaptive regression spline (MARS) technique considered risk score projections from past encounters, changes in risk ratings since previous assessments, and time-series summaries.

The model communicated to nurses and physicians via texts and email, and it included a clinical route for the high-risk patient category, such as physician evaluation within an hour, increased vital sign monitoring, and alerts for palliative care consultations.

Patients admitted to St. Michael’s Hospital’s general internal medicine (GIM) unit received the intervention between 1 November 2020 and 1 June 2022. The pre-interventional period was between 1 November 2016 and 1 June 2020.

Propensity score-based weighting compared intervention recipients to individuals admitted before the intervention. Difference-indifferences assessment compared intervention recipients in the general internal medicine unit and non-recipients in the respiratory, nephrology, and cardiology units.

The primary endpoint was within-hospital mortality from non-palliative care, defined as fatalities that did not result from a recorded palliative care treatment.

Secondary endpoints were palliative deaths, total deaths, and transfers (a composite measure of deaths among palliative care recipients or shifts to inpatient palliative care units), ICU transfer, a composite measure of transfer to ICUs or mortality, and hospital stay length.

The International Classification of Diseases, tenth revision, Canadian version (ICD-10-CA), ascertained patient diagnosis. Researchers retrospectively calculated model predictions for control group patients.

Clinicians received alerts only in the interventional period for GIM unit patients. The study excluded individuals with coronavirus disease 2019 (COVID-19) or influenza and those with preadmission palliative care comorbidities. Logistic regressions estimated propensity scores for the GIM and subspecialty cohorts.

Researchers calculated the relative risk (RR) for analysis, adjusting for study covariates. Poisson regressions compared binary outcomes, and linear models compared continuous outcomes.

Study covariates included age, gender, comorbidities, hospitalizations in the prior six months, hospitalization month, vital signs, homelessness, neighborhood racial and new populations, neighborhood material resources, and admission to the ICU before transfer to subspecialty wards or GIM units.

Results

The analysis comprised 13,649 GIM unit admissions and 8,470 subspecialties unit admissions. In the general internal medicine unit, 482 patients became high risk in the interventional period, and 1,656 patients became high risk during the control period.

Non-palliative mortality was significantly lower during the interventional period than before the intervention among GIM patients (1.60% vs. 2.10%; RR, 0.7) but not among subspecialty unit patients (1.90% vs. 2.10%; RR, 0.9).

Among GIM patients at high risk of deterioration for whom CHARTwatch provided one or more alerts, the non-palliative mortality rates were 7.1% during the interventional period and 10% before the intervention (RR, 0.7).

The team found no significant difference in the subspecialty groups (10% vs. 11%; RR of 0.98). Difference-indifference assessments yielded an RR reduction of 0.8 for mortality from non-palliative care in the general internal medicine unit.

In the held-out testing data, the model demonstrated 53% sensitivity and 31% positive predictive value (PPV) in detecting clinical deterioration during hospitalization (death or transfer to the ICU, step-up care, or palliative care unit).

Compared to the pre-interventional period, the intervention resulted in considerably more antibiotic and corticosteroid prescriptions and increased vital sign monitoring. These data indicate that the intervention was related to enhanced patient monitoring and therapies that might slow deterioration.

Conclusion

The study showed that deploying CHARTwatch for GIM admissions was related to a decreased probability of mortality from non-palliative care compared to the preintervention period.

The results show that early alert systems based on machine learning are potential technologies to improve healthcare outcomes.

However, one should interpret findings cautiously due to the potential unmeasured confounding. Future studies will assess equity-related factors of the intervention and the qualitative perspectives of clinical team members.

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