InPen Insulin bolus calculator, Surfacer catheter system, and RESPMETER
March 1, 2022Pediatrics
March 1, 2022Japan
Japan does not hesitate to innovate AI, from dementia diagnosis using natural language processing to various image analysis models in radiology...
Fronteo
Fronteo, a leading provider of artificial intelligence services, and Kyowa Pharmaceutical Industry Co. have reached an alliance agreement on a dementia diagnosis support system. This system is the first to use AI to analyze conversations and diagnose dementia. It is estimated that the prevalence of dementia in Japan will increase, and about 1 in 7 older adults will suffer from some form of cognitive impairment.
The dementia diagnosis support system employs natural language processing from Fronteo’s AI engine concept encoder to analyze a 5 to 10-minute conversation between a patient and a doctor. The model then determines the presence and severity of cognitive impairment. With Japan’s super-aging population, this system represents a breakthrough to decrease doctors’ burden, increase cost efficiency and allow earlier detection of cognitive impairment.(1)
Lpixel
Lpixel, a spin-out venture from the University of Tokyo founded in 2014, is a leader in advanced image analysis software. It also develops systems tailored to life science research, including the medical, pharmaceutical, and agricultural fields. Lpixel operates AI-driven medical diagnostic imaging support technology “EIRL” and cloud service “IMACEL” to improve the efficiency of image analysis.
They also provide the “LP-Series,” which encompasses a wide variety of software to enhance research efficiency and promote the researchers’ availability for hands-on tasks.(2) “EIRL aneurysm” is an image analysis software that uses deep learning to identify suspicious aneurysms from brain MRI. It is the first medical device with a deep-learning function (Sad) approved by Japan’s Pharmaceuticals and Medical Devices Agency (PMDA) for brain MRI. EIRL aneurysm supports the interpretation process by identifying saccular aneurysm structures larger than 2 mm in brain MRI. In a study, the sensitivity of a doctor interpreting the image alone was 68.2% compared to 77.2% when aided by the program, proving valuable to increase diagnostic accuracy. Through compatibility with DICOM, the universal format for image storage and transfer, image analysis can be sent, received and matched with preexisting picture archiving and communication systems. This seamless workflow transfers analysis results directly to the radiologist’s workstation.(3)
Enlitic
Enlitic is a privately held company utilizing artificial intelligence to streamline medical imaging workflows for radiologists. The CEO of Enlitic commented, “Radiologists have one of the hardest jobs in the world. They need to be able to identify thousands of different abnormalities in hundreds of different types of images. Even a single mistake can mean life or death, and yet they’re asked to read under tremendous time pressure in an environment full of distractions.” The company focuses on training AI models that can read different images accurately. By the end of 2021, Enlitic had advanced its algorithms enough to interpret 95% of common CTs and MRIs. This technology provides solutions to healthcare diagnostics, especially in the burdened Japanese healthcare system.(4)
AI Medical Service
AI Medical Service is developing an AI system that supports endoscopists’ diagnosis of digestive tract diseases in the esophagus, stomach, and intestines. A useful application for such a system is the diagnosis of gastric cancers. Gastric cancer is difficult to diagnose in the early stages of the disease. Studies have linked esophagogastroduodenoscopy (EGD) to a false-negative rate as high as 25%. AI Medical Service collaborated on a research paper that details a new method that makes the diagnosis of gastric cancer easier. The paper’s author believes this research marks the first time convolutional neural networks (CNN) have been used to diagnose gastric cancer from endoscopic images. AI Medical Service engineers and collaborators trained the CNN-based diagnostic system on more than 13,000 endoscopic images of gastric cancer cases. AI Medical Service’s technology can correctly diagnose 71 of 77 gastric cancer lesions with a sensitivity of 92%. The authors said: “All the missing lesions are superficially suppressed and differentiated intramucosal carcinomas, and it is difficult for even experienced endoscopists to distinguish them from gastritis.” Despite admitting room for improvement, it is an excellent step toward the early detection of gastrointestinal cancers.(5)
In Japan, a National Cancer Center (NCC) research team collected data from thousands of operations performed by highly experienced physicians. The team collected more than 1,000 video cases, an extensive compilation, to build a massive surgical database in the cloud. Video of procedures, tasks, and movements by surgeons, and the phenomena they encountered during surgery, have been tagged, classified, and stored for instant search and use. The team believes that this database could revolutionize surgery, lead to innovative solutions, and increase cost efficiency by bringing together human intelligence and machine intelligence. Allowing students to access the database can provide invaluable training and advice.
The database could also be used by surgeons to improve operating procedures and inspire the development of new surgical devices, surgical evaluation, and support systems. Because of its promising advances, more than 40 healthcare facilities across Japan have endorsed the project.(6) Japanese researchers have recently developed an artificial intelligence (AI) system to detect early stage stomach cancer .This system has the potential to make a profound impact on healthcare in Japan, where incidence of stomach cancer is exceptionally high. A group of Japanese researchers have recently used an artificial intelligence (AI) system to detect early stage stomach cancer at the same efficacy of veteran physicians. These researchers tested the system with 100 images of normal stomach tissue, and 100 images of cancerous stomach tissue, and found that it was capable of detecting cancer from an endoscopic image in a matter of 0.004 seconds. Riken and the National Cancer Center report that the system correctly diagnosed cancer with an accuracy rate of 80% and recognized normal stomach tissue 95% of the time. The institute claims that these rates are on par with those of highly trained doctors, and plan to implement this AI into clinical settings in the form of a supplemental diagnostic device.(7)
Japan Society of Ultrasonics in Medicine and Japanese Society of Echocardiography have begun a joint multicenter study to collect labeled echocardiographic data from 2018 onward. With this dataset, an effective AI model could be developed in the near future. Compared with computed tomography and magnetic resonance imaging, there is an issue of high observer variation in the interpretation of echocardiograms.
Therefore, AI can help minimize the observer variation and provide accurate diagnosis in the field of echocardiography. AI in cardiovascular imaging can now automate tasks performed by humans, such as image segmentation and measurement of cardiac structural and functional parameters. The next phase of AI advancement would be to create algorithms that discover clinically important insights. Cardiologists should soon be able to harness AI as a powerful diagnostic tool.(8)
Fronteo
Fronteo, a leading provider of artificial intelligence services, and Kyowa Pharmaceutical Industry Co. have reached an alliance agreement on a dementia diagnosis support system. This system is the first to use AI to analyze conversations and diagnose dementia.
It is estimated that the prevalence of dementia in Japan will increase, and about 1 in 7 older adults will suffer from some form of cognitive impairment. The dementia diagnosis support system employs natural language processing from Fronteo’s AI engine concept encoder to analyze a 5 to 10-minute conversation between a patient and a doctor. The model then determines the presence and severity of cognitive impairment. With Japan’s super-aging population, this system represents a breakthrough to decrease doctors’ burden, increase cost efficiency and allow earlier detection of cognitive impairment.(1)
Lpixel
Lpixel, a spin-out venture from the University of Tokyo founded in 2014, is a leader in advanced image analysis software. It also develops systems tailored to life science research, including the medical, pharmaceutical, and agricultural fields.
Lpixel operates AI-driven medical diagnostic imaging support technology “EIRL” and cloud service “IMACEL” to improve the efficiency of image analysis. They also provide the “LP-Series,” which encompasses a wide variety of software to enhance research efficiency and promote the researchers’ availability for hands-on tasks.(2) “EIRL aneurysm” is an image analysis software that uses deep learning to identify suspicious aneurysms from brain MRI. It is the first medical device with a deep-learning function (Sad) approved by Japan’s Pharmaceuticals and Medical Devices Agency (PMDA) for brain MRI.
EIRL aneurysm supports the interpretation process by identifying saccular aneurysm structures larger than 2 mm in brain MRI. In a study, the sensitivity of a doctor interpreting the image alone was 68.2% compared to 77.2% when aided by the program, proving valuable to increase diagnostic accuracy. Through compatibility with DICOM, the universal format for image storage and transfer, image analysis can be sent, received and matched with preexisting picture archiving and communication systems. This seamless workflow transfers analysis results directly to the radiologist’s workstation.(3)
Enlitic
Enlitic is a privately held company utilizing artificial intelligence to streamline medical imaging workflows for radiologists. The CEO of Enlitic commented, “Radiologists have one of the hardest jobs in the world. They need to be able to identify thousands of different abnormalities in hundreds of different types of images. Even a single mistake can mean life or death, and yet they’re asked to read under tremendous time pressure in an environment full of distractions.”
The company focuses on training AI models that can read different images accurately. By the end of 2021, Enlitic had advanced its algorithms enough to interpret 95% of common CTs and MRIs. This technology provides solutions to healthcare diagnostics, especially in the burdened Japanese healthcare system.(4)
AI Medical Service
AI Medical Service is developing an AI system that supports endoscopists’ diagnosis of digestive tract diseases in the esophagus, stomach, and intestines. A useful application for such a system is the diagnosis of gastric cancers. Gastric cancer is difficult to diagnose in the early stages of the disease. Studies have linked esophagogastroduodenoscopy (EGD) to a false-negative rate as high as 25%. AI Medical Service collaborated on a research paper that details a new method that makes the diagnosis of gastric cancer easier. The paper’s author believes this research marks the first time convolutional neural networks (CNN) have been used to diagnose gastric cancer from endoscopic images.
AI Medical Service engineers and collaborators trained the CNN-based diagnostic system on more than 13,000 endoscopic images of gastric cancer cases. AI Medical Service’s technology can correctly diagnose 71 of 77 gastric cancer lesions with a sensitivity of 92%. The authors said: “All the missing lesions are superficially suppressed and differentiated intramucosal carcinomas, and it is difficult for even experienced endoscopists to distinguish them from gastritis.” Despite admitting room for improvement, it is an excellent step toward the early detection of gastrointestinal cancers.(5)
In Japan, a National Cancer Center (NCC) research team collected data from thousands of operations performed by highly experienced physicians. The team collected more than 1,000 video cases, an extensive compilation, to build a massive surgical database in the cloud. Video of procedures, tasks, and movements by surgeons, and the phenomena they encountered during surgery, have been tagged, classified, and stored for instant search and use.
The team believes that this database could revolutionize surgery, lead to innovative solutions, and increase cost efficiency by bringing together human intelligence and machine intelligence. Allowing students to access the database can provide invaluable training and advice. The database could also be used by surgeons to improve operating procedures and inspire the development of new surgical devices, surgical evaluation, and support systems. Because of its promising advances, more than 40 healthcare facilities across Japan have endorsed the project.(6) Japanese researchers have recently developed an artificial intelligence (AI) system to detect early stage stomach cancer .This system has the potential to make a profound impact on healthcare in Japan, where incidence of stomach cancer is exceptionally high. A group of Japanese researchers have recently used an artificial intelligence (AI) system to detect early stage stomach cancer at the same efficacy of veteran physicians. These researchers tested the system with 100 images of normal stomach tissue, and 100 images of cancerous stomach tissue, and found that it was capable of detecting cancer from an endoscopic image in a matter of 0.004 seconds. Riken and the National Cancer Center report that the system correctly diagnosed cancer with an accuracy rate of 80% and recognized normal stomach tissue 95% of the time. The institute claims that these rates are on par with those of highly trained doctors, and plan to implement this AI into clinical settings in the form of a supplemental diagnostic device.(7)
Japan Society of Ultrasonics in Medicine and Japanese Society of Echocardiography have begun a joint multicenter study to collect labeled echocardiographic data from 2018 onward.
With this dataset, an effective AI model could be developed in the near future. Compared with computed tomography and magnetic resonance imaging, there is an issue of high observer variation in the interpretation of echocardiograms. Therefore, AI can help minimize the observer variation and provide accurate diagnosis in the field of echocardiography. AI in cardiovascular imaging can now automate tasks performed by humans, such as image segmentation and measurement of cardiac structural and functional parameters. The next phase of AI advancement would be to create algorithms that discover clinically important insights. Cardiologists should soon be able to harness AI as a powerful diagnostic tool.(8)
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