As part of its successful transformation from photographic film manufacturer to a company contributing to resolving social challenges with advanced technologies in a wide-range of business fields, Fujifilm Australia has expanded its range of cutting-edge Artificial Intelligence (AI) solutions into the healthcare industry – one of its core businesses. Fujifilm's latest foray into the AI sphere is in partnership with Australian-based medical imaging specialists annalise.ai Ryuichi Matoba, CEO of Fujifilm Australia Pty Ltd. said, "Fujifilm has been supplying X-ray film to the healthcare industry since 1936, shortly after the foundation of Fujifilm in Japan. Since then, Fujifilm has expanded its Medical Systems business with some significant milestones along the way, such as developing Fuji Computed Radiography, applying the world's first digital method for digitising X-ray images. In short, Fujifilm wants to create a society where people can live healthily for longer and Fujifilm's AI technology and our new partnership with annalise.ai As part of its successful transformation from photographic film manufacturer to a company contributing to resolving social challenges with advanced technologies in a wide-range of business fields, Fujifilm Australia has now further expanded its range of cutting-edge Artificial Intelligence (AI) solutions into the healthcare industry – one of its core businesses. Fujifilm's latest foray into the AI sphere is in partnership with Australian-based medical imaging specialists annalise.ai Fuji Computed Radiography, invented by Fujifilm. Ryuichi Matoba, CEO of Fujifilm Australia Pty Ltd. said, "Fujifilm has been supplying X-ray film to the healthcare industry since 1936, shortly after the foundation of Fujifilm in Japan.

Chest radiography, also known as a chest X-ray (CXR), is a widely used imaging test for the screening, diagnosis, and monitoring of various cardiothoracic disorders. According to estimates, CXRs make up around 20% of all imaging exams, with millions of CXRs performed in the United States alone each year. Despite its widespread use, CXR interpretation is subjective and prone to wide interobserver inconsistencies. This can lead to missed findings, which can have serious implications, as 19% of early lung cancers that present as nodules on CXRs are missed. To address this issue, researchers from Massachusetts General Hospital and Harvard Medical School, Qure.ai, and CARPL conducted a study to evaluate the frequency of missed findings in CXRs and the potential of artificial intelligence (AI) to reduce missed findings.

Deep learning is a type of artificial intelligence that has the ability to analyze and understand large amounts of data. One of the areas of research that has been explored is the use of deep learning to decode human thoughts. This is a complex and challenging task, and while researchers have made significant progress in understanding how the brain works, there is still much that is not known. The human brain is an incredibly complex organ, and our understanding of how it works is still limited. However, scientists have been able to make some progress in understanding how the brain produces and processes information.

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Abstract: Fast and accurate dose predictions are one of the bottlenecks in treatment planning for microbeam radiation therapy (MRT). In this paper, we propose a machine learning (ML) model based on a 3D U-Net. Our approach predicts separately the large doses of the narrow high intensity synchrotron microbeams and the lower valley doses between them. For this purpose, a concept of macro peak doses and macro valley doses is introduced, describing the respective doses not on a microscopic level but as macroscopic quantities in larger voxels. The ML model is trained to mimic full Monte Carlo (MC) data.

A precision health company developing medical imaging tools to improve the diagnosis of metabolic diseases and cancer, Perspectum, has completed the first close of its Series C funding round. The company's $36 million investment round was led by Oppenheimer Holdings. With this, the total funding raised by the company accounts for $120 million. Perspectum is working to expand its footprint across the country and grow its customer base. In addition to scaling up its US operations, Perspectum will use the new funding to accelerate its product pipeline for multiorgan inflammatory conditions and oncology.

The Silicon Valley model is to accelerate the process of modernising the health sector, or the health industry as they call it, by digitising its processes, practices and services. The new medical order is based on the application of data science models based on algorithms that build the predictive foundations of Hippocratic truth. Never before have we had the conditions to penetrate human beings as we do now, as we lacked knowledge of the data chains related to organic processes and did not have the arsenal of devices based on digital technologies applied to human health. It is in the field of medicine that we expect to see the greatest impacts and benefits of artificial intelligence (AI), along with devices from the internet of things (IoT). It is a given, as the big tech giants known as Gafam (Google, Amazon, Facebook, Apple and Microsoft) and technoscience built a global consensus that AI should enable medical research to push the current boundaries of human health knowledge and practice.

AI Medtech company WAYCEN (CEO Kyungnam Kim) has won four CES innovation awards among medical AI companies for the first time ahead of "CES 2023", the world's largest electronics trade show, which will be held in Las Vegas, USA, in next January. According to CES 2023 awards results announced by the Consumer Technology Association (CTA) on February 16, WAYCEN has won four innovation awards for WAYMED Cough, WAYMED Endo PRO and WAYMED EBUS. With two awards each in the areas of digital healthcare and software & mobile apps, it demonstrated the technological leadership of K-medtech. Winning these awards means that WAYCEN has been recognized worldwide for its technical expertise in real-time image analysis technology and medical big data analysis technology. Winning two awards exclusively for the products using real-time image analysis technology is intended to highlight innovations in WAYCEN's medical AI solution range.

THURSDAY, Jan. 5, 2023 (HealthDay News) -- For patients with Lynch syndrome (LS), artificial intelligence (AI)-assisted colonoscopy is promising for detecting adenomas, especially flat adenomas, according to a study published online Dec. 26 in the United European Gastroenterology Journal. Robert Hüneburg, M.D., from the National Center for Hereditary Tumor Syndromes at University Hospital Bonn in Germany, and colleagues examined the diagnostic performance of AI-assisted colonoscopy compared with high-definition white-light endoscopy (HD-WLE) in adult patients with LS, with a pathogenic germline variant (MLH1, MHS2, MHS6) and at least one previous colonoscopy (interval, 10 to 36 months). A total of 96 patients were included in the analysis. The researchers found that adenomas were detected in 12 of 46 and 18 of 50 patients in the HD-WLE and AI arms, respectively (26.1 versus 36.0 percent). Detection of flat adenomas (Paris classification 0 to IIb) was increased significantly with use of AI-assisted colonoscopy (numbers of detected flat adenomas: 17 of 30 versus four of 20).

Abstract: We present a Machine Learning (ML) study case to illustrate the challenges of clinical translation for a real-time AI-empowered echocardiography system with data of ICU patients in LMICs. Such ML case study includes data preparation, curation and labelling from 2D Ultrasound videos of 31 ICU patients in LMICs and model selection, validation and deployment of three thinner neural networks to classify apical four-chamber view. Results of the ML heuristics showed the promising implementation, validation and application of thinner networks to classify 4CV with limited datasets. We conclude this work mentioning the need for (a) datasets to improve diversity of demographics, diseases, and (b) the need of further investigations of thinner models to be run and implemented in low-cost hardware to be clinically translated in the ICU in LMICs. Abstract: The ability to jointly learn from multiple modalities, such as text, audio, and visual data, is a defining feature of intelligent systems.