Does Your Business Really Need an Enterprise Artificial Intelligence

Any technology, taking progress several steps forward, always raises concerns that border on excitement and disappointment at the same time. This trend has not spared artificial intelligence. Even though the technology is not new (the first solutions appeared in the 1960s), the real breakthrough and active use of AI in business appeared only in the 21st century. Computing power, larger datasets, and the rise of open-source software allowed developers to create advanced algorithms.

Nowadays, almost all businesses want AI, regardless of size and tasks. So let’s see if artificial intelligence is really so beneficial. For whom it’s too early to implement it, and who needs it as of yesterday.


AI Application for Business

Artificial intelligence is an imitation of the mental properties of the human brain by computer systems. The algorithm learns itself, becoming more and more perfect. To reach the level of a full-fledged thought process, still enough time must pass (although some experts argue that a machine and a person will equal in intellectual abilities in the next decade).

Project Management For Software Development: The App Solutions Tips

Nevertheless, AI is designed to solve relatively voluminous and straightforward tasks from document flow to primitive communication as support. These AI capabilities alone save businesses around the world thousands and thousands of labor hours. Already, 72% of companies using AI in their work say that it makes doing business easier.


In this regard, there are fears that many people will be left without work. Indeed, according to forecasts, secretaries, accountants, administrators, auditors, repairers in factories, and even general and operational managers can lose their jobs. In contrast, new jobs will receive big data and data analysts specialists, AI and machine learning engineers, software and app developers… 

The World Economic Forum says 85 million jobs will be eliminated by 2025, while 97 million new jobs will appear. So the reformatting of the labor market towards technical specialties is inevitable one way or another.


According to Fortune Business Insights, the global AI market was estimated at $27.23 billion in 2019 and is projected to reach $266.92 billion by 2027, with a 33.2% CAGR over the forecast period.

At the same time, IDG claims that in 2021 the cost of AI and similar systems reaches $57.6 billion. For instance, Netflix spends $1 million annually to develop its recommendation engine. According to company representatives: “The typical Netflix contributor loses interest after about 60-90 seconds of selection, having watched 10 to 20 titles (possibly 3 in detail) on one or two screens.” It’s cheaper to spend money on a good advisor than to lose views.


The PwC’s forecast claims that in 2030 AI can contribute up to 15.7 trillion dollars to the global economy. For comparison, the combined output of China and India in the world economy is currently less. However, the PwC predicts that an attractive, innovative business that has yet to emerge could become a market leader based on AI technology in ten years.

AI is already used by 38% of healthcare providers as computer diagnostics and 52% of telecommunications companies as chatbots. It is not surprising. Consumers are increasingly demanding round-the-clock support and are ready to receive more primitive but instant answers to their questions; that is, they are ready to sacrifice quality to save their time.


Benefits of AI for Business 

Regardless of what field you work in – from law to marketing, from medicine to restaurant business – AI will find an appliance everywhere. Several undeniable benefits of AI will be effective for any business.

  1. Improving customer engagement. Chatbot has already become the most popular way to communicate with consumers. Enterprise artificial intelligence contributes to increased customer satisfaction, leading to lower costs, in particular, on the payroll. Moreover, chatbots have become a real salvation for small businesses, which do not have the opportunity to hire a large staff for support.
  2. Increased brand loyalty. Personalization is the key to the consumer’s heart, as evidenced by the investment of Netflix mentioned above in personalized search. With an individual approach, you will inevitably win the preferences of your customers, making them permanent. But to solve this problem, you need to collect a considerable array of analytics of behavioral factors. AI can solve it. Various studies say that this approach increases conversions from 14 to 30%.
  3. Data security and fraud prevention. Primarily relevant for financial enterprises. AI not only finds weaknesses in security systems but can also determine the characteristic behavior during transactions.
  4. Improving the accuracy of forecasts. Artificial intelligence allows you to avoid the human factor when making decisions, reducing the risk of mistakes. For example, lead scoring analyzes and predicts which leads will be the most promising. Other algorithms help control financial flows and trade. Also, you can be sure of compliance with all requirements, standards, and regulations that your company sets.
  5. Recruiting optimization. By automating the analysis of candidates’ CVs, human bias in preliminary checks is eliminated. In due course, PepsiCo needed to hire 250 people out of 1,500 applicants in two months. AI was drawn into the first round of interviews. Thus, all candidates were interviewed in nine hours. It would take human personnel nine weeks, by contrast. During this time, “live” recruiters could deal with more complex creative tasks. The latter concerns other employees of your company – let them develop while AI does the whole routine for them.

How to Get the most out of AI Benefits 

There will be no benefits at all if enterprise AI software is not implemented efficiently. To prevent this in business processes, it is better to follow a few tips that will allow you to comprehensively approach the implementation of artificial intelligence.

  1. New technologies need new people. Without hiring the appropriate specialists, only with the forces of the old state, you most likely will not succeed. Probably, you will need a whole department, but do not be afraid of such expenses – they pay off significantly. Of course, you can use the already developed AI technologies that other companies offer. Still, sooner or later, almost any business comes to the point when it becomes unprofitable and even unsafe to use third-party services.
  2. Don’t be afraid to expand. The introduction of new technologies should bring benefits and profits to the business. However, to reduce costs, in the end, they will need to be increased first. And it concerns not only the increase in staff but also the expansion of new markets because with AI, it is possible to work with large amounts of information. Accordingly, new expenses cannot be avoided; however, the competent use of AI will very soon turn your expenses into income.
  3. Don’t be afraid to change your motion vectors. Artificial intelligence often helps business owners understand that changing the business model will help them move on with greater efficiency. There is no need to be afraid to change anything because it is to change for the better that you started working with AI, right?


Signs Your Enterprise Needs AI Solutions 

Artificial intelligence is complex, and many businesses still don’t know how to implement and benefit from this technology. Companies around the world are at different stages of AI adoption:

  • Awareness (there is the only talk of introducing AI when planning business processes and strategies)
  • Activation (technology is not yet widely used, only as a test for some pilot projects)
  • Operation (at least one project from start to finish uses AI in its work, a separate budget and team is allocated for this)
  • Consistency (all new projects, products, and services are created using AI, all technical employees of the company are aware of the nuances of work, actively apply technology in their daily routine)

However, not all companies decide to implement AI, even if they see an obvious benefit. To understand if you really need AI, think about the following things.

Data Mining Vs. Predictive Analytics: Know The Difference

Well-established Data Collection 

Determine how much information your employees will have to work with. You won’t be able to endlessly hire new specialists to cover all your database needs. If the costs of implementing AI outweigh the other concerns, then prepare your data to ensure that AI adoption runs as smoothly as possible. This requires:

  1. Keep data up to date. The algorithm will not be able to make accurate predictions and provide relevant analytics if your data, for example, on customer behavioral factors, is not updated. To put it bluntly, you don’t need a smartphone if you still use pigeon posts. Spending on AI should pay off. It can process huge amounts of information and produce specific results, but who will need them if the initial data is outdated.
  2. Check your details for errors. A machine can process a large amount of data faster than you, but at the same time, it can get confused about some elementary thing that a first-grader would easily understand. Where the human brain sees a typo in the same word, the machine sees two different words. Of course, the AI ​​has reached the level where it realizes that you made a mistake (for example, when the search engine suggests that “you must have meant” something completely different). However, the search engine has enough experience to conclude the error, but will your algorithm have enough experience from scratch?
  3. Use a consistent format for storing data. For AI to collect all the information stored in your company and process it correctly, you should contain it in one setup.

How to make your IT project secured?

Particular Business Problem to Solve 

So, you have prepared the technical basis for the AI implementation, and now you need to decide what algorithm can help you in the first place? Perhaps, to solve critical problems, or are you already doing well, but you want it to be even better?

  1. Increase the price of the existing product. As we mentioned earlier, the attractiveness of a product or service increases not only due to the quality of the product in front of competitors but also due to a personalized approach to the client. Are you selling cosmetics? Let your AI match the eyeshadow palette, mascara, or 50 shades of lipstick from the same producer to the one chosen by the client.
  2. Analysis of the current status of the business. The algorithm can help you find weaknesses that you didn’t even know about: logistics, marketing, sales, manufacturing – all these can be bottlenecks. Plan resources and forecast demand correctly with AI technology.
  3. Business process automation. When you have identified and eliminated the problems and perhaps even radically changed the business model, it’s time to think about automating processes and, accordingly, optimizing the staff and retraining for more intelligent work.

Culture of Innovations 

Before implementing AI, make sure that your employees share a philosophy of innovation and progress with you, that they have no fear of not coping and fear for their workplace. New technologies can be quickly, organically, and painlessly introduced only if your company is constantly engaged in them.

  1. Corporate strategy. Don’t innovate for the sake of innovation. You will never make a profit this way. You should not put all products under the auspices of AI at once. Try with small, not very resourceful projects. Then there is no risk that your company will collapse like a house of cards in case of failure.
  2. Metrics. Be sure to define the criteria by which you will measure the success of the implementation of AI to understand when the payback comes.
  3. The right to make mistakes. Yes, this also needs to be incorporated into the business strategy. One of the indisputable advantages of AI is considered to be that it excludes the human factor. However, the machine can malfunction; this is a well-known fact. Do not assume that this risk negates all the other advantages of a smart algorithm. Just take into account that you need to spend money not only on the development but also on the support of the algorithm at first.


For all the attractiveness of AI technology, consider whether you really need it. Do your capacities give reason to implement it? If the amount of information is large and the corporate business strategy and tasks are flexible enough, there is no point in delaying.

The APP Solutions is a web and mobile app development team aware of AI algorithms development and implementation for Enterprises. Suppose you are already ready to introduce AI technologies into your business but cannot decide on a development team. In that case, we are ready for fruitful cooperation and are waiting for you!

What is EHR (electronic health record), and how does it work?

Healthcare and data science are something of a perfect pair. Healthcare operations require insights into patient data to function at a practical level. At the same time, data science is all about getting deep into data and finding all sorts of interesting things. 

The combination of these two resulted in the adoption of Electronic Health Records (EHR) that use a data science toolkit for the benefit of medical procedures.

In addition to this, healthcare is the perfect material for various machine learning algorithms to streamline workflows, modernize database maintenance, and increase the accuracy of results.

In this article, we will explain what EHR is and how machine learning makes it more effective.


What is EHR?

Electronic Health Record (aka EHR) is a digital compendium of all available patient data gathered into one database. 

The information in EHR includes medical history, treatment record data such as diagnoses, medications, treatment plans, immunization dates, allergies, radiology images, laboratory and test results.

  • The adoption of EHR in the industry kickstarted in the late 90s after the enacting and signing of HIPAA (Health Insurance Portability and Accountability Act) in 1996. 
  • However, due to technological limitations, things proceeded slowly. 
  • The technology received a significant boost after the passing of the HITECH (Health Information Technology for Economic and Clinical Health) Act in 2014 which specified the whats, whys, and hows of EHR implementation.

The main goal of implementing EHR is to expand the view of patient care and increase the efficiency of treatment.


In essence, EHR is like a good old patient’s paper chart which expands into a full-blown, interactive, data science dashboard, with real-time updates where you can examine the information and also perform various analytical operations. 

  • Think about it as a sort of Google Account type of thing, where your data is gathered into one place and you can use it for multiple purposes with tools like Office 365 or the likes.

The critical characteristics of Electronic Health Records are:

  1. Availability – EHR data is organized and updated in real-time for further data science operations, such as diagnostics, descriptive analytics, predictive analytics, and, in some cases, even prescriptive analytics. It is available at all times and shared with all required parties involved in a patient’s care – such as laboratories, specialists, medical imaging labs, pharmacies, emergency facilities, etc. 
  2. Security – the information is accessed and transformed by authorized users. All patient data is stored securely by extensive access management protocols, encryption, anonymization, and data loss protection routines.
  3. Workflow optimization – EHR features can automate such routine procedures as recurrent Automate and streamline provider workflow. In addition to this, EHR automation can handle healthcare data processing regulations such as HITECH, HIPAA (USA), and PIPEDA (Canada) by implementing required protocols during data processing.

Electronic Health Records vs. Electronic Medical Record – What’s the Difference?

There is also another type of electronic record system used in healthcare operations – Electronic Medical Records AKA EMR. 

The main difference between EHR and EMR is the focus on different persons involved in medical procedures. 

  • EMR is a digital version of the dataflow in the clinician’s office. It revolves around a specific medical professional and contains treatment data of numerous patients within the specialist’s practice.
  • In contrast, EHR data revolves around the specific patient and his medical history. 

In one way or another, EHR intertwines with numerous Electronic Medical Records within its workflow. There is a turnaround of data going back and forth – medical histories, examination data, test results, time-based data comparison, and so on.

Read a more detailed overview of EHR/EMR differences in the article EHR, EMR and PHR Differences

Considering Developing a Healthcare Mobile App?

Download Free Ebook

How AI/ML fits into Electronic Health Record?

As was previously mentioned, the availability of data is one of the primary benefits of implementing Electronic Health Records into medical proceedings. 

Aside from data being available for medical professionals at all times, the way medical data features in EHR makes it perfectly fitting for various machine learning-fueled data science operations.


Overall, machine learning is a viable option in the following aspects of Electronic Health Record:

  • Data Mining
  • Natural Language Processing 
  • Medical Transcription
  • Document Search
  • Data Analytics
  • Data Visualization
  • Predictive Analytics
  • Privacy and regulatory compliance

Let’s look at them one by one.

Data mining 

Gathering valuable insights is one of the essential requirements for providing efficient medical treatment. One of the challenges that come with gaining insights is that, in order to do that, you need to go through a lot of data. This process takes a lot of time.

With the increasing scope of data generated by medical facilities and its growing complexity – the use of machine learning algorithms to process and analyze information during data mining becomes a necessity. 

Overall, the use cases for Data mining in Electronic Health Record revolve around two approaches with different scopes:

  • Finding data about the patient and his treatment. In this case, ML is used to round up relevant information in the medical history and treatment record to assist further in the decision-making process. 
  • On the other hand, patient-centered data mining is used to assess different types of treatment and outcomes by studying similar cases from the broader EHR database.
  • Data extraction for medical research across multiple EHR/EMR, and also public health datasets. In this case, a machine learning application is used to gather relevant data based on specific terms and outcomes across the EHR database. For example, to determine which types of medication for particular ailments were proven to be active and under what circumstances.
  • On the other hand, the same tools apply for exploratory research that reshapes available data according to specific requirements — for example, examining test result patterns of annual lipid profiles.



Predictive Analytics

EHR is all about data analytics and making it more efficient. One of the most important innovations brought by Electronic Health Record is streamlining the data pipeline for further transformations.

The thing is – EHR machine learning-fueled data processing provides a foundation to identify patterns and detect certain tendencies occurring throughout numerous tests and examinations of a specific patient across multiple health records. 

  • With all patient data and respective reference databases intertwined into a single sprawling system – one can leverage the available data to predict possible outcomes based on existing data. 
  • Predictive analytics assist the doctor’s decision-making process by providing more options while considering possible courses of action.
  • On the other hand, machine learning predictive analytics reduces the time required to pro.  

Predictive analytics models are trained case-by-case on the EHR databases. The accumulation of diverse data allows them to identify common patterns and outliers regarding certain aspects of disease development or a patient’s reaction to different treatment methods.

Let’s take DNA Nanopore Sequencing as an example. 

  • The system combines input data (coming from the patient) with data about the illness and ways of treating it. 
  • The predictive algorithm determines whether a particular match of treatment will result in a positive outcome and to which extent. (you can read more about Nanostream in our case study).

Natural Language Processing

In one way or another, natural language processing is involved in the majority of EHR-related operations. The reason for that is simple: most medical record documentation is in a textual form combined with different graphs and charts to illustrate points.

  • Why not use a simple text search instead? Well, while the structure of the document is more or less uniform across the field, the manner of presentation may vary from specialist to specialist. NLP solution provides more flexibility in that regard.

The main NLP use cases for Electronic Health Record are the following:

  • Document Search – both as part of the broader data mining operation and simply as an internal navigation tool. In this case, the system uses a named-entity recognition model trained on a set of specific terms and designations related to different types of tests and examinations. As a result, doctors can save time on finding relevant information in the vast scopes of data. Depending on the purpose, the search results form via the following methods:
  • By context – locating information within the document – vanilla document search. For example, you can perform a comparison of physical examination reports criteria by criteria.
  • Terms/Topics/Phrases – extracting instances of specific terms used or topics mentioned. For example, a doctor can obtain all blood test results and put them into perspective.
  • Search across multiple documents;
  • One of the most prominent current applications is the Linguamatics I2E platform which also provides data visualization features.
  • Medical transcription – in this case, NLP is used to recognize speech, and subsequently, format it in an appropriate way (for example, break down into segments by context).
  • The speech-to-text component operates with a set of commands like “new line” or “new paragraph.”
  • Nuance Communications make one of the most prominent products of this category. Their tools, Nuance Dragon, augments EHR with a conversational interface that assists with filling data into the record.
  • Report generation – in this case, NLP functions as a form of data visualization in a textual form. These models are trained on existing reports and operate on specific templates (for example, for blood test results). Due to the highly formalized language of the reports, it is relatively easy to train a generative model based on term and phrase collocation and correlation. 
  • In this case, the correct verbiage is analyzed out of the habitual juxtaposition of a particular word with another word or words with a frequency higher than chance (collocation) and the extent to which two or more variables fluctuate together (correlation). 


What solutions can we offer?

Find Out More


Data Visualization

Data visualization is another important aspect of data analytics brought to its full extent with the implementation of Electronic Health Records. 

Visualization is one of the critical components that make Electronic Health Record more effective in terms of accessibility and availability of data for various data science operations. 

  • The thing is – as an electronic health record is basically a giant graph with lots of raw data regarding different aspects of the patient’s state, as such, it is not practical to use it in this state. The role of visualization, in this case, is to make data more accessible and understandable for everyday purposes. That has to be obvious, right?

However, you can’t use the same data visualization template for every EHR. While the framework remains the same, it requires room for customization to visualize patient data on the EHR dashboard adequately. 

The role of machine learning in this operation parallels its role in data mining. However, in the case of data visualization, it is about interpreting data in an accessible form. 

At the current moment, one of the most frequently used visualization libraries in Electronic Health Record is d3. For example, we have used its sunburst and pie charts in the Nanostream project. 


Regulatory compliance, privacy, and patient data confidentiality

Healthcare is an industry that mostly operates with sensitive data through and through. Pretty much every element of healthcare operation, in one way or another, touches certain aspects of privacy and confidentiality. 

The fact is that integrated systems like EHR are vulnerable to breaches, data loss, and other unfortunate things that may happen to data in the digital realm. 

In addition to that, healthcare proceedings are bound by government regulations that detail the ins and outs of personal data gathering, processing, and storing in general, and specifically in the context of healthcare.

Such regulations as the European Union’s GDPR, Canada’s PIPEDA, and United States’ HIPAA describe how to handle sensitive personal data and what the consequences are of its mishandling.

The implementation of EHR makes compliance with these regulations much more convenient as it allows us to automate much of the compliance workflow. Here’s how:

  • Anonymization during data processing – in this case, patient data is prepared for testing, but non-crucial identifiable elements, such as names, are concealed.
  • Access management – EHR structure allows limiting access to patient data only for those involved in a patient’s treatment. 
  • A combination of encryption for data-at-rest and data-in-transit – the goal is to avoid any outside interference into data processing.


In Conclusion

The adoption of electronic health records and the implementation of machine learning elevates healthcare operations to a new level.

On the one hand, it expands the view on patient data and puts it into the broader context of healthcare proceedings.

On the other hand, machine learning-fueled EHR provides doctors with a much more efficient and transparent framework for data science that results in more accurate data and deeper insights into it.

Ready to develop your electronic health records system?

Estimate the project cost

What our clients say 


Doogood – An App For Doing Good

Calmerry Online Therapy Platform

Orb Health – Сare Management As A Virtual Service

BuenoPR – 360° Approach to Health

Medical Imaging Explained

Healthcare is an industry permanently aimed at future technologies. It is one of those sectors eager to embrace emerging tech to see if it can make a difference in its quest to cure diseases and save people’s lives. 

Given the fact that healthcare proceedings are data-heavy by design, it seemed evident that sooner than later machine learning, in all its variety, would find its way to the healthcare industry. 

In that context, medical imaging is one of the most prominent examples of effective deep learning implementation in healthcare operations.

In this article, we will:

  • Explain the basics of medical imaging;
  • Explain how deep learning makes medical imaging more accurate and useful;
  • Describe primary machine learning medical imaging use cases;

What is medical imaging?

The term “medical imaging” (aka “medical image analysis”) is used to describe a wide variety of techniques and processes that create a visualization of the body’s interior in general, and also specific organs or tissues. 

Overall, medical imaging covers such disciplines as:

  • X-ray radiography;
  • magnetic resonance imaging (MRI);
  • ultrasound;
  • endoscopy; 
  • thermography; 
  • medical photography in general and a lot more.

The main goal of medical image analysis is to increase the efficiency of clinical examination and medical intervention – in other words, to look underneath the skin and bone right into the internal organs and discover what’s wrong with them.

  • On the one hand, medical imaging explores the anatomy and physical inner-workings. 
  • On the other hand, medical image analysis helps to identify abnormalities and understand their causes and impact. 

With that out of the way, let’s look at how machine learning and deep learning, in particular, can make medical imaging more efficient.

What solutions can we offer?

Find Out More

Why deep learning is beneficial for medical imaging?

One of the defining features of modern healthcare operation is that it generates immense amounts of data related to a variety of intertwined processes. Amongst different healthcare fields, medical images generate the highest volume of data. And, it grows exponentially because the tools are getting better at capturing data. 

Deep inside that data are valuable insights regarding patient condition, the development of the disease/anomaly, and the progress of the treatment. Each piece contributes to the whole and, it is critical to put it all together into a big picture as accurately as possible. 

However, the scope of data often surpasses the possibilities of traditional analysis. Doctors can’t take into consideration so much data. 

This aspect is a significant problem given the fact that data Interpretation is one of the most crucial factors in such fields as medical image analysis. The other issue with human interpretation is that it is limited and prone to errors due to various factors (including stress, lack of context, and lack of expertise). 

Because of this, deep learning is a natural solution to the problem.

Deep learning applications can process data and extract valuable insights at higher speeds with much more accuracy. This can help doctors to process data and analyze test results more thoroughly. 

The thing is – with that much data at hand, the training of deep learning models is not a big challenge. On the other hand, the implementation of deep learning in healthcare proceedings is an effective way to increase the efficiency of operation and accuracy of results. 

The primary type of deep learning application for medical image analysis is a convolutional neural network (you can read more about them here). CNN uses multiple filters and pooling to recognize and extract different features out of input data. 

How deep learning fits into medical imaging?

The implementation of deep learning into medical image analysis can improve on the main requirements for the proceedings. Here is how: 

  • Provide high accuracy image processing; 
  • Enable input image analysis with an appropriate level of sensibility to certain field-specific aspects (depends on the use case. For example, bone fracture analysis).

Let’s break it down in an understandable term example, an x-ray of bones: 

  • Shallow layers identify broad elements of an input image. In this case – bones. 
  • Deeper layers identify specific aspects – like fractures, their positions, severity, and so now. 

The primary operations handled by deep learning medical imaging applications are as follows:

  • Diagnostic image classification – involves the processing of examination images, comparison of different samples. It is primarily used to identify objects and lesions into specific classes based on local and global information about the object’s appearance and location.
  • Anatomical object localization – includes localization of organs or lesions. The process often involves 3D parsing of an image with the conversion of three-dimensional space into two-dimensional orthogonal planes. 
  • Organ/substructure segmentation – involves identifying a set of pixels that define contour or object of interest. This process allows quantitative analysis related to shape, size, and volume.
  • Lesion segmentation – combines object detection and organ / substructure segmentation.  
  • Spatial alignment – involves the transformation of coordinates from one sample to another. It is mainly used in clinical research.
  • Content-based image retrieval – used for data retrieval and knowledge discovery in large databases. One of the critical tools for navigation in numerous case histories and understanding of rare disorders.
  • Image generation and enhancement – involves image quality improvement, image normalizing (aka cleaning from noise), data completion, and pattern discovery. 
  • Image data and report combination. This case is twofold. On the one hand, report data is used to improve image classification accuracy. On the other hand, image classification data is then further described in text reports.

Now let’s look at how medical image analysis uses deep learning applications.

Deep Learning in Medical Imaging Examples

Deep learning cancer detection

At the time of writing this piece, cancer detection is one of the major applications of deep learning CNNs. This particular use case makes the most out of deep learning implementation in terms of accuracy and speed of operation.

This aspect is a big deal because some forms of cancer, such as melanoma and breast cancer, have a higher degree of curability if diagnosed early. 

On the other hand, deep learning medical image analysis is practical at later stages. 

For instance, it is used to track and analyze the development of metastatic cancer. One of the most prominent deep learning models in this field is LYmph Node Assistant (LYNA) developed at MIT. The LYNA model trained on pathological slides datasets. The model reviews sample slides and recognizes characters of tumors and metastases in a short time span with a 99% rate of accuracy. 

In the case of skin cancer detection, deep learning is applied at the examination stage to identify anomalies and track its development. To do that, it compares sample data with available datasets such as T100000. (You can read more about it in our recent case study).

Breast cancer detection is the other critical use case. In this case, a deep learning neural network is used to compare mammogram images and identify abnormal or anomalous tissues across numerous samples.

Tracking tumor development

One of the most prominent features of convolutional neural networks is its ability to process images with numerous filters to extract as many valuable elements as possible. This feature comes in handy when it comes to tracking the development of the tumor.

One of the main requirements for tracking tumor development is to maintain the continuity of the process i.e., identifying various stages, transition points, and anomalies. 

The training of tumor development tracking CNN requires a relatively small number of clinical trials in comparison with other use cases. 

The resulting data reveal critical features of the tumor with various image classification algorithms. The features include tumor location, area, shape, and also density. 

In addition to that, such CNN can: 

  • track the changes of the tumor over time; 
  • tie this data with the impacting factors (for example, treatment or lack thereof). 

In this case, the system also uses predictive analytics to analyze tumor proliferation. One of the most common methods for this is tumor probability heatmap that classifies the state of the tumor based on the tissue patch overlap.

Considering Developing a Healthcare Mobile App?

Download Free Ebook

Deep learning medical image analysis – MRI image processing acceleration

MRI is one of the most complicated types of medical imaging. The operation is both resource-heavy and time-consuming (which is why it benefits so much from cloud computing). The data contains multiple layers and dimensions that require contextualization for accurate interpretation.

Enter deep learning. The implementation of the convolutional neural network can automate the image segmentation process and streamline its proceedings with a wide array of classification and segmentation algorithms that sift through data and extract as many things of note as required.

The operation of MRI scan alignment takes hours of computing time to complete. The process involves sorting millions of voxels (3D pixels) that constitute anatomical patterns. In addition to this, the same process is required for numerous patients time after time. 

Here’s how deep learning can make it easier. 

  • Image classification and pattern recognition are two cases in which neural networks are at best. 
  • The convolutional neural network can train to identify common anatomical patterns. The data goes through multiple CNN filters that sift through it and identify relevant patterns.
  • As a result, CNN will be capable of spotting anomalies and identify specific indications of different diseases.

The segmentation process may involve 2d/3d convolution kernels that determine the segmentation patterns. 

  • 2D CNN slices the data one-by-one to construct a pattern map;
  • 3D CNN uses voxel data that predicts segmentation maps for volumetric patches.

As such, segmentation is a viable tool for diagnosis and treatment development purposes across multiple fields. In addition to that, it contributes significantly to quantitative studies and computational modeling, both of which are crucial in clinical research.

One of the most prominent implementations of this approach is MIT’s VoxelMorph. This system used several thousand different MRI brain scans as training material. This feature enables the system to identify common patterns of brain structure and also spot any anomalies or other suspicious differences from the norm.

Retinal blood vessel segmentation

Retinal blood vessel segmentation is one of the more onerous medical imaging tasks due to its scale. The thing is – blood vessels take just a couple of pixels contrasting with background pixels, which makes them hard to spot, not to mention analyze at the appropriate level.

Deep learning can make it much more manageable. However, it is one of the cases where deep learning takes more of an assisting role in the process. Such neural networks use Structured Analysis of the Retina, aka STARE dataset. This dataset contains 28 999×960 annotated images. 

Overall, there are two ways deep learning improves retinal blood vessel segmentation operation:

  1. Image enhancement can improve the quality of an image.
  2. Substructure segmentation can correctly identify the blood vessels and determine their state. 

As a result, the implementation of neural networks significantly compresses the time-span of workflow. The system can annotate the samples on its own as it already has the foundational points of reference. Because of that, the specialist can focus on the case-specific operations instead of manually reannotating samples every time. 

Deep learning cardiac assessment

Cardiac assessment for cardiovascular pathologies is one of the most complicated cases that require lots of data to spot the pattern and determine the severity of the problem. 

The other critical factor is time, as cardiovascular pathologies require swift reaction to avoid lethal outcomes and provide effective treatment. 

This is something deep learning can handle with ease. Here’s how. Deep learning fits into the following operations:

  • Blood Flow Quantification – to measure rates and determine features.
  • Perform anomaly detection in the accumulated quantitative data. 
  • Data Visualization of the results. 

The implementation of deep learning in the process increases the accuracy of the analysis and allows doctors to gain much more insight in a shorter time. The speed of delivery can positively impact the course of treatment.

Musculoskeletal Radiograph’s Abnormality Detection 

Bone diseases and injuries are amongst the most common medical causes of severe, long-term pain and disability. As such, they are a high testing ground for various image classification and segmentation CNN use cases.

Let’s take the most common method of bone imaging – X-rays. While the interpretation of images is less of a problem in comparison with other fields, the workload in any given medical facility can be overwhelming for the resident radiologist. 

Enter deep learning: 

  • CNN is used to classify images, determining their features (i.e., bone type, etc.) 
  • After that, the system segments the abnormalities of the input image (for example, fractures, breaks, spurs, etc.).

As a result, the implementation of deep learning CNN can make the radiologist’s job more accessible and effective.

In Conclusion

From the data volume standpoint, medical image analysis is one of the biggest healthcare fields. This alone makes the implementation of machine learning solutions a logical decision. 

The combination is beneficial for both.

  • On one hand, medical imaging gets a streamlined workflow with faster turnaround and higher accuracy of the analysis.
  • On the other hand, such an application contributes to the overall development of neural network technologies and enables their further refinement.

Ready to apply data science in your healthcare organization?

What our clients say 

Related readings:

Calmerry Telemedicine Platform Case Study