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What is the latest application example of medical AI? Doctor commentary [2020 version]
By TOKYO analytica-April 15, 2019

 

In recent years, the use of artificial intelligence (AI) has advanced rapidly in so many fields that there is no day without seeing new news about AI. The background lies in the development of Deep Learning technology, but of course the use of AI in medicine is no exception. On the other hand, medical care is a special area that directly deals with human life, so the effect of AI is a little different from other fields. This time, we will broadly explain AI in the context of medical care, what is AI in the first place, what kind of benefits it will bring, what kind of problems will be born at the same time, and the latest medical AI trends to watch I would like to introduce

1. What is AI in healthcare?
What is AI in the first place?
AI (Artificial Intelligence) literally means "artificial intelligence," but its definition varies from researcher to researcher. Roughly speaking, it is a program that reproduces the logical thinking that human beings have done on a computer. Until now, it was a very limited "intelligence" that could only give an answer under certain rules, but due to the advanced development of deep learning technology from around 2010, the program is autonomous from the given data. It became possible to build a judgment standard by learning to. This means that AI can build and classify its own criteria, even those that could not show clear criteria to humanity. What brought this system to medical treatment is the so-called “medical AI”. In medical care, even if we focus solely on the disease, many judgments such as onset risk evaluation, disease diagnosis, treatment selection, prognosis evaluation, etc. are required, but the judgment is complicated by the difference in the situation for each individual. Usually very difficult If a criterion is constructed based on a large amount of accumulated patient data and, for example, an optimal treatment method is presented for each individual, the benefits provided by medical AI will be extremely large for both patients and medical personnel. I can imagine that.

What is Deep Learning?
Deep learning is the core of recent AI technology development, but it is difficult to catch up with all of the new technologies that are being released literally day by day. Here, I will give a brief explanation of these basic terms and examples of their use in medicine.

There is an algorithm called a neural network (which shows the procedure) that has been studied since the mid-20th century. This is a model of cranial nerve cells in humans and other living organisms, and has a structure in which the layer structure such as the input layer, the hidden layer, and the output layer are connected by edges. By giving a function to each layer (called an activation function) and giving weights to edges, a very complicated model for classifying input values ​​and outputting an answer is realized. This algorithm with many hidden layers (deep) is called deep learning. By the way, machine learning refers to "iteratively learning from given data and finding appropriate rules", and deep learning is also included in this. More specifically, let's say that you want to build an algorithm that predicts the onset of diabetes one year later from the current blood glucose level, blood pressure, and body weight. By inputting 3 points of blood glucose level, blood pressure, and body weight from a database that collects a lot of patient data, and setting whether or not diabetes was developed one year later as an output, edge weights etc. are determined through repeated learning. To do. The resulting optimal algorithm, given these three points, can predict whether a person will develop diabetes one year later. Such learning of the relationship between input and output is called supervised learning, which is frequently used in AI development in medicine.

In general machine learning, the feature amount (three points of blood glucose level, blood pressure, and body weight in the above example) is arbitrarily selected and input, whereas in deep learning, the feature amount that is most useful for determining the output. Even it can be automatically extracted and learned. Convolutional neural network (CNN), which is known as a typical deep learning model, gives an image itself as an input, but it is not necessary to specify a concrete feature quantity. For example, an algorithm developed by Mayo Clinic in the United States (past article) identifies asymptomatic left ventricular dysfunction from the electrocardiogram waveform image itself. Of course, it is also possible to extract features from the electrocardiogram waveform (height/width of the waveform, etc.) and construct an equivalent algorithm, but the input information is limited by selecting any item (in the statistical field, "information Is discarded"), and it is highly possible that the accuracy of the CNN algorithm will not be exceeded. Currently, this CNN plays a leading role in the development of diagnostic imaging AI.

Medical AI is powerful but not universal
Artificial intelligence seems to imitate human intelligence, a substitute for brain function, but in many cases this is overstated. To put it simply, AI at the moment is just something that can identify something specific. That is, if the algorithm can identify the blood vessel from the image, it will be returned as "blood vessel" if the image of the artery is shown, and "not blood vessel" if the image of hair is shown. Without making any changes to this algorithm, we cannot even distinguish between a newborn and an adult. Therefore, almost all AI, which has been popular around the world, just extracts "specific and very limited functions" of human beings and reproduces them on a computer, which is not all-purpose.

However, this reproduced function is so powerful that it sometimes greatly exceeds the discrimination ability of humankind. For example, it has been reported that an algorithm for identifying pediatric diseases from electronic medical record records exceeded the diagnostic accuracy of pediatricians for multiple diseases including influenza (past article). The results of this research have been published in the prestigious academic journal Nature Medicine and have attracted attention. Also, because AI can capture even minute changes that cannot be discerned by the human eye, it is highly compatible with medical images such as malignant tumor diagnosis, and technological development in the field of radiology is remarkable (past article). In addition, AI has the advantage of continuing to deliver stable results. Particularly in Japan, there is a harsh situation where doctors' judgments are blunted, such as overload of work due to lack of personnel, frequent calls at night and on holidays, and shifts to regular work. AI support that maintains a certain level of output accuracy could be a form of insurance for doctors.

2. What medical fields can AI utilize?

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Next, I would like to look at medical fields that can utilize AI. Medical treatment is roughly divided into three steps: “prevention” to prevent the onset of disease, “diagnosis” to identify people who already have the disease, and “treatment” to improve the turning point of people with a diagnosis. AI is not only able to contribute to all of these three major steps, but is also being used in medical systems, including medical insurance systems and medical provision systems, and is expected to be used in all medical fields. ing. Behind this is the lack of human resources associated with the high degree of expertise of medical professionals, and many countries are trying to find a way out for AI.

Utilizing AI in diagnostic imaging
Radiology, especially the process of diagnosing diseases from medical images is known as the most important application of medical AI. The number of medical images that require interpretation at each facility is increasing due to the development of medical peripheral technology and the aging of society, but the number of radiologists accompanying it is not sufficient. AI can directly reduce the work load by helping the radiologist's interpretation. However, it is important to know that the number of AI devices currently approved as legitimate medical devices is very limited. In other words, even if AI diagnoses that "it has a malignant tumor," at present, there is almost no progress from diagnosis to treatment without the confirmation of a doctor. This is also a problem with AI, which will be described later, but it is due to lack of verification of the validity of the algorithm and the delay in the development of laws regarding AI. No alternatives have been found.

On the other hand, it is highly likely that AI will completely replace chest X-ray interpretation and ECG analysis in medical examinations in the near future. This is because the image interpretation in the medical examination is the screening, and the definite diagnosis is obtained in the subsequent individual medical examination. False negatives (determining “no disease” despite the presence of a disease) poses a problem in screening, but adjustment of the algorithm can sufficiently avoid this problem. AI, which is capable of producing consistent results with insomnia and rest, will be positively accepted by this type of screening agency in terms of manpower and cost. Practical examples of eye disease screening in the UK and China have been introduced in the past (Past articles 1 and 2).

Utilization of AI in disease diagnosis
With the rapid development of natural language processing technology in recent years, medical record analysis has become more popular. As a result, the disease diagnosis AI based on medical records has greatly improved its accuracy. In the past, we also introduced AI for diagnosis of pediatric diseases (past article), medical chart analysis system by Amazon (past article), so please refer to it. The medical record contains not only the findings of the doctor, but also all test results and prescription records. The longer the patient's medical history, the larger the medical records will be, and it will be difficult for even a good doctor to capture all of them during the limited medical treatment time. From the aspect, the use of AI will be effective. Similarly, there is an example of developing an AI system that integrates not only medical records but also biometric sensors and monitor records (past article). This system at the University of Florida in the United States predicts serious pathological changes and the occurrence of fatal diseases in the intensive care unit (ICU), and it can be said that AI is an effective application example. Furthermore, the development of a method for more easily diagnosing diseases such as sleep apnea syndrome that require specialized tests for diagnosis by using AI is proceeding (past article).

Utilization of AI for various medical problems
Utilization of AI is not limited to actual clinical situations. It is also used for vulture journal issues that undermine the credibility of medical science (past article), for combating doping in sports (past article), and even for efforts to prevent bioterrorism (past article). ..

3. What are the problems and issues of AI in the medical field?

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Lack of AI validation
Many medical researchers continue to be skeptical of AI algorithms (past articles). The opinion is that only the high accuracy of the algorithm is distracted and the essential effectiveness is left behind.

When building an algorithm, generally only one dataset is used. This data set is divided into, for example, 80% and 20%, one side is a learning set for algorithm construction, and the other is a test set. In other words, it is a method of confirming whether the algorithm derived from the learning set exhibits the same accuracy in the test set and concluding that the algorithm is valid if the accuracy is maintained. However, the true validity has not been verified by this method alone. This is because the algorithm is only derived from "a specific population data", and its accuracy may not be maintained if the target population is changed. To give a simple example, the algorithms derived from the British-centered data set are not always effective in the Japanese. In fact, Amazon's proud face recognition AI, Rekognition, is also causing a big turmoil based on the research findings that "I have a bias that I can not distinguish black women well" (see CNN). There is no doubt that precise verification based on conventional medical evidence construction, such as multi-center analysis with different target populations and prospective follow-up studies, is required.

Delay in legislation surrounding medical AI
Over the last five years, the use of AI in medical care has progressed rapidly, but the technological development has been so rapid that necessary legal arrangements have been made

Running late. As a matter of fact, there is not enough discussion about how to handle the diagnostic results shown by medical AI. At the moment, by adding the phrase "always require confirmation from a doctor", it can only be seen as being introduced as a support system in clinical settings. It is a minimum to clarify and comply with the guidelines for the construction of essentially effective algorithms, the requirements for the AI ​​system to be approved as a medical device, and certain restrictions on those without approval. It has been demanded. It is very dangerous that medical AI of unknown effectiveness is widely available on the market without any restrictions.

Medical staff lacks knowledge about AI
It cannot be prescribed without knowing the effect and mechanism of action of the drug. Similarly, medical personnel such as doctors will need some knowledge of AI in the future. The need for AI subjects as a basic education for medical students has begun to be discussed, and an example of Boston University was introduced before (past article). In the future, as AI in healthcare becomes more pervasive and will become (and likely will be) involved in all healthcare processes, it will be very difficult for healthcare professionals to avoid AI.

 

Four. Up-to-date medical AI trend of attention

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Drug discovery by AI
Pharmaceutical giant GlaxoSmithKline and British startup Exscientia announced that they have discovered compounds that could be used to treat chronic obstructive pulmonary disease (COPD) (past article).
This uses an AI-based compound search platform, which could be an epoch-making act in AI drug discovery. Compared with the conventional process, using AI not only makes drug development much more efficient, but it is also effective in exposing targets that have been overlooked even though there is a possibility of drug development. I have already introduced the automatic search technology of synthetic routes by AI, so please refer to it (past article).

In February 2020, Exscientia and Sumitomo Dainippon Pharma announced that they would start phase I clinical trials of new drug candidate compounds developed using AI. The entry of AI-guided compounds into human clinical trials represents a milestone in drug discovery.

Use for diagnostic imaging at death
There is an approach called diagnostic imaging at death. The purpose is to investigate the condition and cause of death at the time of death from CT and MRI images after death (past article). It is not realistic for a radiologist who has been pointed out that there is a shortage of personnel even with biometric images, and forcing him to read images during death imaging. In addition, not many radiologists are good at reading such special areas. Utilization of AI in such situations is considered to be extremely useful. In fact, it has been pointed out that AI-based analysis of post-mortem brain MRI images of patients with neurodegenerative diseases may lead to the development of new treatment methods.

Use for medical devices
A wave of technological innovation is coming to the stethoscope, which is a traditional medical tool. StethoMe's AI stethoscope is known as an EU-certified medical device intended for home use. We are constructing a system in which abnormal breathing sounds of patients are caught early and information is shared with clinicians along with the diagnosis results by AI. StethoMe announced a partnership with major telemedicine providers in Europe in April 2020, and is expected to further expand its market share (past article).

Efforts to replace the blood sampling technique with robots have also begun. A blood sampling robot developed by a research team at Rutgers University, NJ, combines deep learning with infrared and ultrasound imaging to identify blood vessels in tissue. Then perform complex visual tasks such as motion tracking to puncture the blood vessel with the needle. It is expected that robotic vascular access will be equivalent to or superior to human procedures, such as 88.2% success rate of initial puncture even if the blood vessel is in poor condition such that veins do not stand out. article).

"Edge AI" gains importance in medical care
Cloud computing and IoT have rapidly spread in all areas, but in recent years, “edge computing” has attracted attention because data processing is performed at a location (edge) closer to the end of the system. The one that implements an AI model on an edge device close to the site is called "edge AI", and some have completed the processing from learning to inference only on the edge side without using the cloud.

So what are the advantages of introducing edge AI in healthcare? The first point is to improve network connectivity. In other words, compared to a system that performs all processing on a cloud basis, by partially entrusting the edge device with processing of a large amount of on-site data, it is possible to reduce the direct load on the host system and network. Since the stage of healthcare is not limited to urban areas where IT infrastructure is well developed, its usefulness is remarkable even in remote areas where medical depopulation is likely to occur. Further, in telemedicine and robotic surgery, which are expected to grow even more in the future, strict control of latency is indispensable. It is easily assumed that the use of edge AI will be necessary to meet the highly demanded standard.

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Comparative Effectiveness of Acupuncture and Antiarrhythmic Drugs for the Prevention of Cardiac Arrhythmias: A Systematic Review and Meta-analysis of Randomized Controlled Trials

Yanda Li1‡, Hector Barajas-Martinez2‡, Bo Li3†‡, Yonghong Gao4, Zhenpeng Zhang1, Hongcai Shang4, Yanwei Xing1* and Dan Hu2,5*

  • 1Guang'anmen Hospital, Chinese Academy of Chinese Medical Sciences, Beijing, China

  • 2Masonic Medical Research Laboratory, New York, NY, United States

  • 3Xi Yuan Hospital, Chinese Academy of Chinese Medical Sciences, Beijing, China

  • 4Key Laboratory of Chinese Internal Medicine of the Ministry of Education, Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine, Beijing, China

  • 5Department of Cardiology and Cardiovascular Research Institute, Renmin Hospital of Wuhan University, Wuhan, China

Introduction and Objectives: This study was designed to systematically evaluate the effectiveness of acupuncture treatment for arrhythmia compared to existing drug therapy.

Methods: Randomized controlled trials (RCTs) were identified through searches of the MEDLINE, CNKI, Embase, and Cochrane databases (1970 through 2016) and hand searches of cross-references from original articles and reviews. Clinical trials that randomized arrhythmia patients to acupuncture therapy vs. conventional drugs, sham acupuncture, or bed rest were included for analysis.

Results: A total of 13 trials with 797 patients met the criteria for analysis. The results of the meta-analysis showed no statistically significant difference between acupuncture and conventional treatment for paroxysmal supraventricular tachycardia (PSVT) (n = 203; RR, 1.18; 95% CI 0.78–1.79; I2 = 80%; P = 0.44). However, in the ventricular premature beat (VPB) group, it showed a significant benefit of acupuncture plus oral administration of anti-arrhythmic drug (AAD) on response rates compared with the oral administration of AAD (n = 286; RR, 1.15; 95% CI 1.05–1.27; I2 = 0%; P = 0.002). Finally, when compared with the sinus tachycardia (ST) cases without any treatment, acupuncture has benefited these patients (n = 120; MD, 18.80, 95% CI 12.68–24.92; I2 = 81%; P < 0.00001).

Conclusions: In summary, our meta-analysis demonstrates that clinical efficacy of acupuncture is not less than AAD for PSVT. Furthermore, in sub-group analysis, acupuncture with or without AAD, shows a clear benefit in treating VPB and ST. However, more definitive RCTs are warranted to guide clinical practice.

Introduction

The 2013 overall rate of death attributable to cardiovascular diseases was 222.9 per 100,000 Americans. Among cardiovascular diseases, cardiac arrhythmia is one of the most common and serious diseases and is a serious threat to human health (Chen et al., 2013Mozaffarian et al., 2016). Arrhythmia mainly refers to the abnormal of heart impulse frequency, rhythm, origin site, conduction velocity, or the excited order. Arrhythmias include: paroxysmal supraventricular tachycardia (PSVT), sinus tachycardia (ST), ventricular premature beat (VPB), atrial fibrillation (Af), etc., (Burashnikov et al., 2012Mozaffarian et al., 2016).

Commonly used clinical treatments for arrhythmia include drug therapy, surgical intervention, and radiofrequency catheter ablation. However, all antiarrhythmic drugs have proarrhythmic effects and may cause gastrointestinal reactions, central response, hypotension, and other adverse reactions (Camm, 2017). Surgical treatment may cause serious damage and is always complex. Radiofrequency catheter ablation is characterized by a low recurrence rate and fewer complications, but it has an unsatisfactory success rate (Lomuscio et al., 2011).

In recent years, a number of clinical observations indicate that acupuncture might be an effective therapy for cardiac arrhythmias through multiple mechanisms and has the advantages of being simple, safe, inexpensive, and associated with few adverse reactions (Lomuscio et al., 2011). Recent scientific studies have examined the role that acupuncture may have as an effective intervention for cardiac arrhythmias (Kim et al., 2011Lin, 2015). Although plagued by methodological shortcomings, these studies support acupuncture as an effective treatment for atrial flutter, (Xu and Zhang, 2007Lomuscio et al., 2011) PSVT (Wu and Lin, 2006) inappropriate ST, (Xie et al., 2004) and symptomatic PVBs (Yuan and Ai, 2002Liu, 2005Zhong, 2008). Traditional Chinese Medicine (TCM) believes that the normal heartbeat relies on the “heart-qi.” Ample “heart-qi” maintains normal cardiac rhythm (Han and Wang, 2008Andrew and Mehmet, 2014).

This study was designed to systematically evaluate the use of acupuncture for arrhythmia treatments, and to evaluate the effectiveness of acupuncture treatment for arrhythmia compared to existing drug therapy.

Methods

This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Ethical approval was not necessary for this review study.

Search Strategy

Studies were identified through a computerized literature search of MEDLINE, Embase, the Cochrane library and CNKI from their inception through January 2016. The search strategy terms used were as follows: (Acupuncture OR Acupuncture Therapy OR Therapy, Acupuncture) AND (Arrhythmias, Cardiac OR Cardiac Dysrhythmia OR Dysrhythmia, Cardiac OR Cardiac Arrhythmia OR Cardiac Arrhythmias OR Arrhythmia OR Arrhythmia) AND (randomized controlled trial OR controlled clinical trial OR randomized OR placebo OR clinical trials as topic OR randomly OR trial).

Selection Criteria

RCTs were included with blind method or not and without language restriction. Trials were included if they enrolled patients with various types of cardiac arrhythmia and randomly allocated patients to receive acupuncture treatment or commonly used clinical treatments. Studies were excluded if they meet any of the following conditions: (1) Post-hoc analysis or studies without reliable outcome data; (2) Non-randomized controlled trials; (3) Auricular needling or ear acupuncture were chosen.

Observed Outcome Measures

Validity was calculated as follows: In the analysis evaluating the effectiveness of acupuncture for PSVT, Reduction of heart rate (HR) and restoration to normal sinus rhythm (NSR) were set as the outcome measures. In the analysis assessing the effectiveness of acupuncture for VPB, symptom reduction and HR reduction and % reduction of VBP on ECG or holter were set as the outcome measures. And in the analysis assessing the effect of a single acupuncture treatment in patients with ST, Reduction of HR was set as the outcome measure.

Statistical Analysis

Based on the different types of arrhythmia, we divided all of the studies into PSVT, VPB, and Af groups for assessment. The data were extracted from each of the published studies independently by two investigators. All P-values were two-tailed, with statistical significance set at 0.05, and the confidence intervals were calculated at the 95% level. Analyses were performed using Revman software version 5.3. Publication bias was detected to use funnel plots, with asymmetry suggesting possible publication bias. Publication bias was also assessed by Begg test, Egger test, and the funnel plots made using the STATA software version 12.0 for the meta-analysis. Publication bias was considered to exist if the P-value was <0.05.

Results

Baseline Characteristics

In total, 13 trials with 797 patients were available for analysis (Figure 1, Table 1Yuan and Ai, 2002Zhang and Xu, 2002Li, 2003Xie et al., 2004Liu, 2005Dong, 2006Wu and Lin, 2006Xu and Zhang, 2007Zhong, 2008Li and Guo, 2009Lomuscio et al., 2011Yuan and Zhao, 2012Wang and Zhang, 2013). Three studies evaluated the effectiveness of acupuncture for PSVT (Figure 2A). Five studies assessed the effectiveness of acupuncture plus the oral administration of AAD compared with the oral administration of AAD only for the treatment of VPB (Figure 2B). Two studies tested the effects of acupuncture on Af compared with amiodarone. Two studies assessed the effect of a single acupuncture treatment in patients with ST; both studies showed significant effects on heart rate reduction. The last RCT investigated the effects of acupuncture on patients with premature beat (PB), and the results showed significant effects of acupuncture on reducing the number of PBs. Adverse events (AEs) were reported only in XU's study (Xu and Zhang, 2007), and no AEs were noted in the acupuncture group (Xu and Zhang, 2007).

FIGURE 1

 

Figure 1. Flow diagram of the systematic review.

TABLE 1

 

Table 1. Summary of the included studies on acupuncture for cardiac arrhythmia.

FIGURE 2

 

Figure 2. Meta-analysis of the effectiveness of acupuncture treatment compared with existing therapy for several kinds of arrhythmias. (A) Acupuncture vs. conventional drug therapy for PSVT; (B) acupuncture therapy plus the oral administration of AAD vs. the oral administration of AAD alone for VPB; (C) acupuncture vs. control treatment (neither acupuncture nor any other anti-arrhythmic treatment) for ST.

Acupuncture vs. Conventional Drug Therapy for Paroxysmal Supraventricular Tachycardia

The results of the meta-analysis showed no statistically significant difference in the effectiveness of acupuncture therapy compared with conventional drug therapy for PSVT [n = 203; relative ratio (RR), 1.18, 95% confidence interval (CI) 0.78–1.79; I2 = 80%; P = 0.44; Figure 2A].

Acupuncture + Drug vs. Conventional Drug Therapy for Ventricular Premature Beat (VPB)

The results of the meta-analysis of these five RCTs showed a statistically significant benefit of acupuncture plus the oral administration of AAD on the response rate compared with the oral administration of AAD alone for VPB (n = 286; RR, 1.15, 95% CI 1.05–1.27; I2 = 0%; P = 0.002, Figure 2B).

Acupuncture vs. Control Treatment (Neither Acupuncture nor Any Other Anti-arrhythmic Treatment) for Sinus Tachycardia

The results of the meta-analysis of these two RCTs showed a statistically significant benefit of acupuncture on the response rate compared with the control treatment (neither acupuncture nor any other anti-arrhythmic treatment) for ST (n = 120; MD, 18.80, 95% CI 12.68–24.92; I2 = 81%; P < 0.00001, Figure 2C).

The Risk of Bias

A review of the authors' judgments about each risk of bias item presented as percentages across all of the included studies. The quality of the selected studies was assessed according to the Cochrane criteria (Figures 34Higgins and Altman, 2008).

FIGURE 3

 

Figure 3. The risk of bias. A review of the authors' judgments about each risk of bias items are presented as percentages across all of the included studies. The quality of the selected studies was assessed according to the Cochrane criteria.

FIGURE 4

 

Figure 4. The risk of bias summary. A review of the authors' judgments about the risk of bias is included in each study.

Publication Bias

Funnel plot and Egger's test of publication bias were made for each analysis. However, as the number of included studies was small, the result might not be completely accuracy (Figure 5, Table 2).

FIGURE 5

 

Figure 5. Funnel plot for publication bias [(A) PSVT; (B) VPB].

TABLE 2

 

Table 2. Egger's test of publication bias for PSVT and VPB.

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MAI is assisting Beijing University of Chinese Medicine (BUCM) to develop an online teaching portal for institutional use so that they may upload videos that they record using our VR-based acupuncture training solution (AcuMap). The goal of this initiative is to provide an e-learning portal to increase access to teaching and learning for educators and students within the institution.

In addition, BUCM is currently integrating this acupuncture training solution into the curriculum to teach students, within an immersive VR learning environment, how to more effectively identify and simulate needle insertion into high risk and dangerous acupoints.

Our acupuncture training solution was awarded as the sole national acupuncture training program in China.

We also welcome any requests for custom projects not listed in the project categories above. Please contact us to learn more.

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onverts 2D patient medical images (MRI, CT scans) into 3D virtual images in less than 30 seconds. This technology allows clinicians to engage patients with their Digital Twin for improved patient education and shared decision making processes leading to better treatment plans.

CHOOSE DIGITWIN IF YOU ARE

– A clinician

Better connect with and educate patients so as to more efficiently reach shared decisions and formulate appropriate treatment plans.

– A clinic or wellness center

Attract patients who wish to spend more time with their health providers to learn about their health conditions in detail and take part in treatment decisions.

Key Features

Fast conversion

Convert patient diagnostic images (e.g., MRI, CT scans) into 3D virtual images in less than 30 seconds

Hassle-free interface

Our platform allows clinicians to observe what the patient is seeing and how the patient is interacting with his or her Digital Twin in real time

Image fusion

Image fusion technology to improve image quality and reduce randomness and redundancy

Measuring tools

Precision measuring tools to measure dimensions of regions of interest in patient images

Annotation tools

For better understanding of certain regions of interest

Sharing

Instant sharing of medical knowledge and patient information among clinicians in real time

 

Digital Twin Case Study at LANDSEED Healthcare Center

from MAI

Still curious about the best way to engage with patients? Start your virtual journey today!

Revolutionizing the future of acupuncture training

WHAT IS ACUMAP

Visualize meridian pathways and acupoints which are otherwise invisible to the naked eye

Leveraging proprietary BodyMap technology, AcuMap allows users to overlay meridian pathways and acupoints against human anatomical systems of the body, and also simulate acupuncture techniques on various acupoints with instant haptic feedback and interactive visualization tools including depth information and angle insertion readings.

CHOOSE ACUMAP IF YOU ARE

An educator of acupuncture

Overcome limitations of teaching with traditional acupuncture manikins, conduct more effective dry needling training.

A practitioner of acupuncture

Refine specific needling procedures prior to initiating treatment on actual patients.

A researcher

Conduct research studies to assess how VR- and technology-based solutions affect cognitive behaviors and information retention.

An institution

Attract students looking for technology-based learning solutions to enhance traditional teaching methods. Reach more students remotely so that those not in physical attendance may still participate in learning.

Key Features

300+ Acupoints

Visualization of 300+ acupoint locations along 14 Traditional Chinese Medicine meridian pathways

14 Meridians

Comparison of 14 Traditional Chinese Medicine meridian pathways against 12 main body systems

Haptic Simulation

During acupuncture needling simulations, users receive light haptic feedback when piercing through the skin and strong haptic feedback when hitting bone

 

Question Bank

Question bank of 3000 anatomy quiz questions and categorized result feedback to assess understanding

 

Instant Magnification

Instant magnification and visualization tools during simulation training, with depth and angle of instrument insertion, e.g., syringe, displayed in real time

 

Multi-Player

Multi-player use with instant sharing of medical knowledge between instructors and students in real time

Still curious about the best way to learn acupuncture? Start your virtual journey today!

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