Relationship Between Cognitive Function, Oral Conditions and Systemic Metabolic Function in the Elderly.

Systemic metabolic disorders, including lifestyle-related diseases, are known risk factors for dementia. Furthermore, oral diseases such as periodontal disease and tooth decay are also associated with systemic metabolic disorders such as lifestyle-related diseases, and have also been reported to be indicators of risk factors for developing dementia. In this study, we investigated the relationship between cognitive function, oral conditions and systemic metabolic function in the elderly. We investigated the number of healthy teeth, the number of prosthetic teeth fitted, the number of missing prosthetic teeth, etc., in 41 elderly patients (69.7 ± 5.6 years old). Cognitive function was evaluated by the Mini Mental State Examination (MMSE). We also estimated MMSE scores for each subject using deep learning-based assessment of MMSE scores. This deep learning method enables the estimation of the MMSE score based on basic blood test data from medical examinations and reflects the systemic metabolic state including lifestyle-related diseases. The estimated MMSE score correlated negatively with age (r = -0.381), correlated positively with the number of healthy teeth (r = 0.37), and correlated negatively with the number of missing prosthetic teeth (r = -0.39). This relationship was not found in the measured MMSE scores. A negative correlation (r = -0.36) was found between age and the current number of teeth and a positive correlation (r = 0.37) was found between age and the number of missing prosthetic teeth. A positive correlation was found between the number of teeth requiring prosthesis and lifestyle-related diseases. The deep learning-based estimation method of cognitive function clearly demonstrated the close relationship between oral health condition, systemic metabolic function and the risk of cognitive impairment. It was determined that the smaller the number of existing teeth and the larger the number of missing prosthetic teeth, the higher is the risk of cognitive impairment. Systemic metabolic function is presumed to affect oral health and cognitive function. Interestingly, no such relationship was found in the measured MMSE scores. There are two possible reasons for this. The first is that MMSE is a subjective test and is less accurate in assessing cognitive function. The second is that because the MMSE estimated based on blood data using deep learning is calculated based on the metabolic function, it has a stronger correlation with the oral health condition affected by the metabolic function. In conclusion, oral health condition may predict cognitive impairment in the elderly.

Integration of wearable devices and deep learning: New possibilities for health management and disease prevention.

In recent years, the market for wearable devices has been rapidly growing, with much of the demand for health management. These devices are equipped with numerous sensors that detect inertial measurements, electrocardiograms, photoplethysmography signals, and more. Utilizing the collected data enables the monitoring and analysis of the user's health status in real time. With the proliferation of wearable devices, research on applications such as human activity recognition, anomaly detection, and disease prediction has advanced by combining these devices with deep learning technology. Analyzing heart rate variability and activity data, for example, enables the early detection of an abnormal health status and prompt, appropriate medical interventions. Much of the current research focuses on short-term predictions, but adopting a long-term perspective is essential for further development of wearable devices and deep learning. Continuously recording user behavior, anomalies, and physical information and collecting and analyzing data over an extended period will enable more accurate disease predictions and lifestyle guidance based on individual habits and physical conditions. Achieving this requires the integration of wearable devices with medical records. A system needs to be created to integrate data collected by wearable devices with medical records such as electronic health records in collaboration with medical facilities like hospitals and clinics. Overcoming this challenge will enable optimal health management and disease prediction for each user, leading to a higher quality of life.

Predictive deep learning models for cognitive risk using accessible data.

The early detection of mild cognitive impairment (MCI) is crucial to preventing the progression of dementia. However, it necessitates that patients voluntarily undergo cognitive function tests, which may be too late if symptoms are only recognized once they become apparent. Recent advances in deep learning have improved model performance, leading to applied research in various predictive problems. Studies attempting to estimate dementia and the risk of MCI based on readily available data are being conducted, with the hope of facilitating the early detection of MCI. The data used for these predictions vary widely, including facial imagery, voice recordings, blood tests, and inertial information during walking. Deep learning models that make predictions based on these data sources have been proposed. This article summarizes recent research efforts to predict the risk of dementia using easily accessible data. As research progresses and more accurate predictions become feasible, simple tests could be incorporated into daily life to monitor one's personal health status and to facilitate an early intervention.

The latest policies, practices, and hotspots in research in conjunction with the aging of Japan's population.

Population aging is a global concern, and Japan currently has the world's highest proportion of an aging population. In 2020, the population age 65 and over accounted for 10% of the global population, while this proportion was 29% in Japan, and it is expected to reach 38.4% in 2065. The average life expectancy in Japan in 2022 was 81.05 for males and 87.09 for females. At the same time, Japan's healthy life expectancy continues to increase, and it is increasing at a faster rate than the average life expectancy, with males expected to live 72.68 years and females expected to live 75.38 years in 2019. This is causing the social role of elderly people in Japan to constantly change. The Japanese Government continues to adjust its policy orientation, to improve the health level and social participation of the elderly, improve the accessibility of long-term nursing services and the treatment of nursing professionals, and improve the pension system. By 2025, one-fifth of people in Japan are expected to suffer from dementia. Japan has implemented a series of policies to create a dementia-inclusive and less risky society. The proportion of the population ages 65 and over living alone in Japan increased from 4.3% among males and from 11.2% among females in 1980 to 15.0% among males and 22.1% among females in 2020, representing a sustained increase. Changes in the composition of the population have prompted sustained attention to the personalization and diversification of elderly care. At the same time, Japanese researchers continue to utilize scientific and information technology to innovate elderly care products, improve the efficiency of elderly care, and provide intelligent elderly care.

Comprehensive assessment and treatment strategies for dysphagia in the elderly population: Current status and prospects.

As the population ages, the prevalence of dysphagia among older adults is a growing concern. Age-related declines in physiological function, coupled with neurological disorders and structural changes in the pharynx associated with aging, can result in weakened tongue propulsion, a prolonged reaction time of the submental muscles, delayed closure of the laryngeal vestibule, and delayed opening of the upper esophageal sphincter (UES), increasing the risk of dysphagia. Dysphagia impacts the physical health of the elderly, leading to serious complications such as dehydration, aspiration pneumonia, malnutrition, and even life-threatening conditions, and it also detrimentally affects their psychological and social well-being. There is a significant correlation between frailty, sarcopenia, and dysphagia in the elderly population. Therefore, older adults should be screened for dysphagia to identify both frailty and sarcopenia. A reasonable diagnostic approach for dysphagia involves screening, clinical assessment, and instrumental diagnosis. In terms of treatment, multidisciplinary collaboration, rehabilitation training, and the utilization of new technologies are essential. Future research will continue to concentrate on these areas to enhance the diagnosis and treatment of dysphagia, with the ultimate aim of enhancing the quality of life of the elderly population.

Recent deep learning models for dementia as point-of-care testing: Potential for early detection.

Deep learning has been intensively researched over the last decade, yielding several new models for natural language processing, images, speech and time series processing that have dramatically improved performance. This wave of technological developments in deep learning is also spreading to medicine. The effective use of deep learning in medicine is concentrated in diagnostic imaging-related applications, but deep learning has the potential to lead to early detection and prevention of diseases. Physical aspects of disease that went unnoticed can now be used in diagnosis with deep learning. In particular, deep learning models for the early detection of dementia have been proposed to predict cognitive function based on various information such as blood test results, speech, and the appearance of the face, where the effects of dementia can be seen. Deep learning is a useful diagnostic tool, as it has the potential to detect diseases early based on trivial aspects before clear signs of disease appear. The ability to easily make a simple diagnosis based on information such as blood test results, voice, pictures of the body, and lifestyle is a method suited to point-of-cate testing, which requires immediate testing at the desired time and place. Over the past few years, the process of predicting disease can now be visualized using deep learning, providing insights into new methods of diagnosis.

New possibilities for medical support systems utilizing artificial intelligence (AI) and data platforms.

In Japan, there is a growing initiative to construct centralized databases and platforms that can aggregate and manage a vast range of medical, health, and caregiving data for research and analysis. Recent advancements in artificial intelligence (AI), particularly in general-purpose models like the Segment Anything model and Chat GPT, promise significant progress towards utilizing such data-rich platforms effectively for healthcare. Traditionally, AI has displayed superior performance by learning specific images or languages, but now it is advancing towards creating models capable of learning universal traits from images and languages by training on extensive datasets. The challenge lies in the fact that these general-purpose models are trained on data that does not sufficiently incorporate medical information, making their direct application to healthcare difficult. However, the introduction of data platforms can potentially solve this problem. This would lead to the development of universally applicable models to process medical images and AI assistants that can support both doctors and patients. These medical AI assistants can function as a "sub-doctor" with extensive knowledge, assisting in comprehensive analysis of symptoms, early detection of rare diseases, and more. They can also serve as an intermediary between the doctor and the patient, helping to simplify consultations and enhance patient understanding of medical conditions and treatments. By bridging this gap, the AI assistant can help to reduce doctors' workload, improve the quality of healthcare, and facilitate early detection and prevention in the elderly population.

Improving the sensitivity of liver tumor classification in ultrasound images via a power-law shot noise model.

Power laws have been observed in various fields and help us understand natural phenomena. Power laws have also been observed in ultrasound images. This study used the power spectrum of the signal identified from the reflected ultrasound signal observed in ultrasonography based on the power-law shot noise (PLSN) model. The power spectrum follows a power law, which has a scaling factor that depends on the characteristics of the tissue in the region where the ultrasound wave propagates. To distinguish between a tumor and blood vessels in the liver, we propose a classification model that includes a scaling factor based on ResNet, a deep learning model for image classification. In a task to classify 6 types of tissue - a tumor, the inferior vena cava, the descending aorta, the Gleason sheath, the hepatic vein, and small blood vessels - tumor sensitivity increased 3.8% and the F-score for a tumor improved 2% while precision was maintained. The scaling factor obtained using the PLSN model was validated for classification of liver tumors.

Relaxation of all-case reporting of COVID-19 cases in Japan.

Japan is facing the largest outbreak of COVID-19 in history in 2022. The number of new infections per day surpassed 200,000 for the first time in July and peaked in August. Japan has required the reporting of information on all infected persons, but maintaining this system is difficult. Starting in September 2, 2022, four prefectures have implemented a trial policy to limit the infected that must be reported in order to reduce the burden on medical personnel. The policy obliges medical facilities to report only people with a high-risk infection, but the number of the infected will continue to be counted regardless of whether they have a high-risk or low-risk infection. More prefectures are expected to adopt this policy in the future.

Increasing demand for point-of-care testing and the potential to incorporate the Internet of medical things in an integrated health management system.

As the number of people with COVID-19 increases daily around the world, point-of-care testing (POCT) is gaining attention as a tool that can provide immediate test results and greatly help to deter infection and determine what to do next. POCT has several drawbacks such as a low sensitivity and specificity, but according to studies POCT has increased sensitivity on par with that of polymerase chain reaction testing. The advantage of POCT is that the results can be obtained quickly, regardless of the location. To further enhance its benefits, POCT is being developed and researched in conjunction with the Internet of medical things (IoMT), which allows POCT results to be collected, recorded, and managed over a network. IoMT will be beneficial not only for the use of POCT simply as a testing tool but also for its integration into diagnostic and health management systems. IoMT will enable people to regularly receive their test results in their daily lives and to provide personalized diagnosis and treatment of individual conditions, which will be beneficial in terms of disease prevention and maintenance of health.

Trends in managing COVID-19 from an emerging infectious disease to a common respiratory infectious disease: What are the subsequent impacts on and new challenges for healthcare systems?

Targeting the 9 countries with the highest cumulative number of newly confirmed cases in the past year, we analyzed the case fatality ratio (CFR) among newly confirmed cases and the vaccination rate (two or more doses of vaccine per 100 people) in the United States of America (USA), India, France, Germany, Brazil, the Republic of Korea, Japan, Italy, and the United Kingdom (UK) for the period of 2020-2022. Data reveal a decrease in the CFR among newly confirmed cases since the beginning of 2022, when transmission of the Omicron variant predominates, and an increase in vaccination rates. The Republic of Korea had the lowest CFR among newly confirmed cases (0.093%) in 2022 and the highest vaccination rate (86.27%). Japan had the second highest vaccination rate (83.12%) and a decrease in the CFR among newly confirmed cases of 1.478% in 2020, 1.000% in 2021, and 0.148% in 2022; while the average estimated fatality ratio for seasonal influenza from 2015-2020 was 0.020%. Currently, most countries are now easing COVID-19-related restrictions and are exploring a shift in management of COVID-19 from an emerging infectious disease to a common respiratory infectious disease that can be treated as the equivalent of seasonal or regional influenza. However, compared to influenza, infection with the Omicron variant still has a higher fatality ratio, is more transmissible, and the size of future outbreaks cannot be accurately predicted due to the uncertainty of viral mutation. More importantly, as countries shift their response strategies to COVID-19, there is an urgent need at this time to clarify what the subsequent impacts on healthcare systems and new challenges will be, including the clinical response, the dissemination of scientific information, vaccination campaigns, the creation of future surveillance and response systems, the cost of treatments and vaccinations, and the flexible use of big data in healthcare systems.

An average of nearly 200,000 new infections per day over a six-week period: What is the impact of such a severe COVID-19 pandemic on the healthcare system in Japan?

During a six-week period from July 20 to August 31, 2022, Japan experienced its highest level of COVID-19 infection ever, with an average of nearly 200,000 new infections per day nationwide. Cases requiring inpatient care peaked at 1,993,062. Twenty-seven prefectures (out of 47 prefectures) had an average hospital bed occupancy of 50% or higher, and bed occupancy in Kanagawa in particular reached 98% in mid-August. In Tokyo, bed occupancy by patients with severe COVID-19 reached 57% and peaked at 64% in mid-August. Although the number of new infections per day has decreased since September, hospital bed occupancy, the number of severe cases, and deaths remain high nationwide. Efforts including vaccination campaigns, domestic surveillance, and routine infection control measures based on the varied knowledge that the Japanese public already has should be thoroughly implemented to reduce the number of the infected in order to avoid an increase the number of serious cases and deaths.

Automated liver tumor detection in abdominal ultrasonography with a modified faster region-based convolutional neural networks (Faster R-CNN) architecture.

Background: Although diagnostic ultrasound can non-invasively capture the image of abdominal viscera, diagnosis of the continuous ultrasound liver images to detect a liver tumor effectively and to determine whether the detected is benign or malignant is nontrivial. In order to minimize the gaps in diagnostic accuracy depending on doctor's proficiency, we built an automated system to support the ultrasonography of liver tumors by employing deep learning technologies. Methods: We constructed a neural network model for the automated detection of tumor tissues and blood vessels from the sequential liver ultrasound images. Faster region-based convolutional neural networks (Faster R-CNN) is employed as a base model for the object detection, which can output the detection results in 4 frames per second and enable the system to be particularly suitable for the real time ultrasonography. Moreover, we proposed a new neural network architecture feeding both the current and previous images into Faster R-CNN. For training the models, intraoperative ultrasound images obtained from one hepatocellular carcinoma (HCC) patient were used. The obtained image was a multifaceted observation of the liver and includes one HCC and some blood vessels. We labeled 91 images with the help of a liver specialist. We compared the tumor detection performance of the plain Faster R-CNN model with that of the proposed model. Results: We find that both the models performed well in detecting HCC and blood vessels, after training with 400 epochs using Adam. However, the mean precision of our model reaches 0.549, which is 0.019 better than that of the plain Faster R-CNN, and the mean sensitivity of our model about HCC reaches 0.623±0.385 for 30 scenes of sequential liver ultrasound images, which is also 0.146 better than that of the plain Faster R-CNN model. Conclusions: The comparison between the proposed model and the plain Faster R-CNN model shows that we achieved better accuracy in tumor detection, in terms of the mean precision as well as the mean sensitivity, with the proposed model.

Behavioral changes adopted to constrain COVID-19 in Japan: What are the implications for seasonal influenza prevention and control?

Respiratory disease deaths associated with seasonal influenza are estimated to be 290,000 to 650,000 per year globally. In Japan, seasonal influenza affects more than 10 million people per year, and especially children, the elderly, and patients with underlying medical conditions, and seasonal influenza can cause severe illness. As SARS-CoV-2 continues to spread, the combined risk of concurrent influenza epidemics and the COVID-19 pandemic are a concern. When the status of influenza virus infections during the 2020-2021 flu season was compared to the 2011 to 2020 flu seasons, data indicated the absence of seasonal influenza outbreaks in Japan during the COVID-19 pandemic. The number of flu patients was roughly estimated to be 14,000 nationwide from September 2020 to March 2021, which marks the first sharp decrease since national influenza surveillance started in 1987 in conjunction with National Epidemiological Surveillance of Infectious Diseases (NESID). Moreover, approximately 500 sentinel sites (designated medical facilities) nationwide reported only 112 patients with severe influenza who required hospitalization. Since prevention and control measures amidst the COVID-19 pandemic have become the "new normal", one can reasonably assume that the absence of a seasonal influenza outbreak is related to prevention and control measures implemented in response to the COVID-19 pandemic. Basic infection prevention measures were thoroughly implemented, such as wearing masks, handwashing, and avoiding confined spaces, crowded places, and close-contact settings. More importantly, the behavioral changes adopted to constrain COVID-19 during three declared states of emergency reduced population density and contact with people, including closing schools, asking restaurants to reduce their business hours, teleworking, curbing the flow of people during vacation week, etc. These behavioral changes will serve as a valuable reference to reduce the spread of seasonal influenza in the future.

Infectious disease activity during the COVID-19 epidemic in Japan: Lessons learned from prevention and control measures.

In Japan, the Law Concerning the Prevention of Infectious Diseases and Medical Care for Patients with Infectious Diseases (the "Infectious Diseases Control Law") classifies infectious diseases as category I-V infectious diseases, pandemic influenza, and designated infectious diseases based on their infectivity, severity, and impact on public health. COVID-19 was designated as a designated infectious disease as of February 1, 2020 and then classified under pandemic influenza as of February 13, 2021. According to national reports from sentinel surveillance, some infectious diseases transmitted by droplets, contact, or orally declined during the COVID-19 epidemic in Japan. As of week 22 (June 6, 2021), there were 704 cumulative cases of seasonal influenza, 8,144 cumulative cases of chickenpox, 356 cumulative cases of mycoplasma pneumonia, and 45 cumulative cases of rotavirus gastroenteritis; these numbers were significantly lower than those last year, with 563,487 cumulative cases of seasonal influenza, 31,785 cumulative cases of chickenpox, 3,518 cumulative cases of mycoplasma pneumonia, and 250 cumulative cases of rotavirus gastroenteritis. Similarly, many infectious diseases transmitted by droplets or contact declined in other countries and areas during the COVID-19 pandemic. One can reasonably assume that various measures adopted to control the transmission of COVID-19 have played a role in reducing the spread of other infectious diseases, and especially those transmitted by droplets or contact. Extensive and thorough implementation of personal protective measures and behavioral changes may serve as a valuable reference when identifying ways to reduce the spread of infectious diseases transmitted by droplets or contact in the future.

Overview of the characteristics of and responses to the three waves of COVID-19 in Japan during 2020-2021.

The first case of COVID-19 in Japan was reported on 16 January 2020. The total number of the infected has reached 313,844 and the number of deaths has reached 4,379 as of 16 January 2021. This article reviews the characteristics of and responses to the three waves of COVID-19 in Japan during 2020-2021 in order to provide a reference for the next step in epidemic prevention and control. The Japanese Government declared a state of emergency on 7 April 2020, which suppressed the increase in the number of the infected by curtailing economic activity. The first wave peaked at 701 new cases a day and it decreased to 21 new cases on May 25 when the state of emergency was lifted. However, the number of the infected increased again due to the resumption of economic activity, with a peak of 1,762 new cases a day during the second wave. Although the situation was worse than that during the first wave, the government succeeded in limiting the increase without declaring a state of emergency again, and that may be attributed to a decrease in crowd activities and an increase in the number of inspections. During the third wave, the number of the infected continued to exceed the peak during previous waves for two months. Major factors for this rise include the government's implementation of further policies to encourage certain activities, relaxed immigration restrictions, and people not reducing their level of activity. An even more serious problem is the bed usage for patients with COVID-19; bed usage exceeds 50% not only in major cities but also in various areas. On 7 January 2021, 5,953 new cases were reported a day; this greatly exceeded the previous peak, and the state of emergency was declared again. Although Japan has been preparing its medical system since the first wave, maintaining that system has imposed a large economic burden on medical facilities, hence stronger measures and additional support are urgently needed to combat COVID-19 in the coming few months.

Realizing 5G- and AI-based doctor-to-doctor remote diagnosis: opportunities, challenges, and prospects.

Fifth Generation (5G) mobile communications technology became available in Japan as of the end of March 2020. The Ministry of Internal Affairs and Communications (MIC) is proceeding with a plan to use 5G for a doctor-to-doctor remote diagnosis system. This remote diagnosis offers patients the benefit of receiving advanced medical care without having to travel long distances. The provision of a remote diagnosis will provide elderly patients in rural areas with an earlier diagnosis without burdening patients in Japan where the aging population and the uneven distribution of doctors are increasing. However, the system will increase the burden on specialists by expanding the doctor's catchment area. As a solution to that problem, deep learning-based artificial intelligence (AI) is expected to reduce the burden on doctors. In order to realize 5G- and AI-based real-time diagnostic support, diagnostic imaging using AI and an AI model that provides instructions are required. This is because ultrasonography and endoscopy, which can be used for remote diagnosis, do not acquire data on fixed areas like a CT or MRI scan. The AI model needs to instruct the doctor at the patient's home in order to collect appropriate information in accordance with the patient's symptoms and status. In order to build an interactive AI model, the interactions between doctors who are making a remote diagnosis should be recorded as training data and a 5G-based remote diagnosis system should be created. A remote diagnostic support system incorporating 5G and interactive diagnostic imaging incorporating AI will result in a system that places less of a burden on patients and doctors.

Shifting workstyle to teleworking as a new normal in face of COVID-19: analysis with the model introducing intercity movement and behavioral pattern.

BACKGROUND: Instead of the complete lockdown, since the outbreak of coronavirus disease 2019 (COVID-19), Japan has been trying to control the infection by self-restraint request policy. It seems that the number of infected people has subsided, however, the increasing human activities again in the resumption of economy may lead to the second wave of infections. Here, we analyzed the major factors behind the success control of the first outbreak in Japan and the potential risk of the second wave. METHODS: Employing a localized stochastic transition model, we analyze the real data and the results of simulation in Tokyo from March 1 to July 31. In the model, population is divided into three compartments: susceptible, infected, and removed; and area into three zones: crowded, mid and uncrowded. Different zones have different infection probabilities characterized by the number of people gathered there. The flow of the infection simulation in one day consists of three steps: (I) intercity movement of population, (II) isolating infected people, and (III) zone shifting following group behavioral patterns. RESULTS: The major cause for the success of controlling the first outbreak in Tokyo is demonstrated through our simulation to be the early request of self-restraint as well as the early detection of infected people. Meanwhile, the observation that the increasing human activities again in the resumption of economy will lead to the second wave of infections is also found in the simulation with an extended period. Based on the analysis of intercity movement and behavioral pattern on Tokyo where normally about 2.9 million people come from the surrounding cities to the central area by using the public railway system every day, results showed that turning the workstyle of 55% of working people ranging in age from 20 to 64 years old into teleworking (remote work) may control the spread of infection without significant economic damage. Meanwhile, to keep about 75% of the normal activity level and to advocate the shift to telework are indispensable because a sudden resumption of activity from the lockdown sate can rapidly spread infection. CONCLUSIONS: As a new normal in face of COVID-19 for Tokyo and other cities that with a high population density, shifting the workstyle of 55% of working people to teleworking and to reduce 25% time staying in the high infection risk area could be an effective measure to control the spread of infection while maintaining a certain level of economic activity.

Analysis of COVID-19 infection spread in Japan based on stochastic transition model.

To assess the effectiveness of response strategies of avoiding large gatherings or crowded areas and to predict the spread of COVID-19 infections in Japan, we developed a stochastic transmission model by extending the Susceptible-Infected-Removed (SIR) epidemiological model with an additional modeling of the individual action on whether to stay away from the crowded areas. The population were divided into three compartments: Susceptible, Infected, Removed. Susceptible transitions to Infected every hour with a probability determined by the ratio of Infected and the congestion of area. The total area consists of three zones crowded zone, mid zone and uncrowded zone, with different infection probabilities characterized by the number of people gathered there. The time for each people to spend in the crowded zone is curtailed by 0, 2, 4, 6, 7, and 8 hours, and the time spent in mid zone is extended accordingly. This simulation showed that the number of Infected and Removed will increase rapidly if there is no reduction of the time spent in crowded zone. On the other hand, the stagnant growth of Infected can be observed when the time spent in the crowded zone is reduced to 4 hours, and the growth number of Infected will decrease and the spread of the infection will subside gradually if the time spent in the crowded zone is further cut to 2 hours. In conclusions The infection spread in Japan will be gradually contained by reducing the time spent in the crowded zone to less than 4 hours.

On medical application of neural networks trained with various types of data.

Neural networks have garnered attention over the past few years. A neural network is a typical model of machine learning that is used to identify visual patterns. Neural networks are used to solve a wide variety of problems, including image recognition problems and time series prediction problems. In addition, neural networks have been applied to medicine over the past few years. This paper classifies the ways in which neural networks have been applied to medicine based on the type of data used to train those networks. Applications of neural networks to medicine can be categorized two types: automated diagnosis and physician aids. Considering the number of patients per physician, neural networks could be used to diagnose diseases related to the vascular system, heart, brain, spinal column, head, neck, and tumors/cancer in three fields: vascular and interventional radiology, interventional cardiology, and neuroradiology. Lastly, this paper also considers areas of medicine where neural networks can be effectively applied in the future.