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The Global Machine Learning in Orthopedics Market Report evaluates various economic facts of the companies such as shares, profit margins and pricing structures to understand the financial terms effectively. Some significant facts such as local consumption, import and export have been scrutinized and presented clearly to provide a better understanding to the readers. Furthermore, it focuses on-demand supply chain to understand the requirement from various global clients along with some significant features.

Our industry professionals are working relentlessly to understand, assemble and timely deliver assessment on impact of COVID-19 disaster on many corporations and their clients to help them in taking excellent business decisions.

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The report is scrutinized with various aspects of the existing industries such as types, size, technology, application and end-users. Different exploratory techniques such as, qualitative and quantitative analysis have been used to give data accurately. For better understanding of the customers, it uses effective graphical presentation techniques, such as graphs, charts, tables as well as pictures. Across the globe, some significant global regions such as North America, Latin America, Asia-Pacific, Europe, and India have been considered to study the different specifications of productivity, manufacturing base and raw materials.

In order to obtain the most optimal solutions for improving the performance of industries, effective sales approaches have been highlighted. The internal and external factors responsible for driving or restraining the growth of the industries have been covered to know the upstream and downstream of the businesses. The turning point of the industries has been presented by giving effective approaches to discover global customers massively. Different models for the evaluation of the risks and challenges are listed, which helps to find the desired solutions for improving the performance of the industries.

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Key players in global Machine Learning in OrthopedicsRSIP Vision, OM1s Chief Technology, Medicrea, Sparta Science, Spentys, myrecovery.ai, ImageBiopsy Lab, Articulate Labs, AlgoSurg Inc., OrthoFeed

Global Machine Learning in Orthopedics Market Segmentation:

Market segmentation, by product types:Type 1Type 2

Market segmentation, by applications:Application 1Application 2

Based on Region:

Market Event Factors Analysis:

Market driver

Key questions answered in Global Machine Learning in Orthopedics Market Report:

The years considered to estimate the market size in this study are as follows:

In the end the Global Machine Learning in Orthopedics Market Report delivers conclusion which includes Research Findings, Market Size Estimation, Market Share, Consumer Needs/Customer Preference Change, Data Source. These factors will increase business overall.

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Table of Contents:

Part 1:Executive Summary

Part 2:Scope of the Report

Part 3:Research Methodology

Part 4:Market Landscape

Part 5:Pipeline Analysis

Part 6:Market Sizing

Part 7:Five Forces Analysis

Part 8:Market Segmentation

Part 9:Customer Landscape

Part 10:Regional Landscape

Part 11:Decision Framework

Part 12:Drivers and Challenges

Part 13:Market Trends

Part 14:Vendor Landscape

Part 15:Vendor Analysis

Part 16:Appendix

Customization of the Report:

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About Us:

HealthCare Intelligence Markets Reports provides market intelligence & consulting services to a global clientele spread over 145 countries. Being a B2B firm, we help businesses to meet the challenges of an ever-evolving market with unbridled confidence. We craft customized and syndicated market research reports that help market players to build game-changing strategies. Besides, we also provide upcoming trends & future market prospects in our reports pertaining to Drug development, Clinical & healthcare IT industries. Our intelligence enables our clients to make decisions with which in turn proves a game-changer for them. We constantly strive to serve our clients better by directly allowing them sessions with our research analysts so the report is at par with their expectations.

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Excerpt from:
Machine Learning in Orthopedics Market Research Forecasts 2021-2028 by Type, Application and Top Key Vendors KSU | The Sentinel Newspaper - KSU | The...

Machine learning seems to be picking up steam as one of the buzzwords to look out for this decade.

Among the U.S. and Japan-based I.T. professionals surveyed in 2017, three-fourths said they were already using machine learning for cybersecurity. Most were also confident that the cyberattacks on their businesses within the past year used machine learning. Despite its increasing use, machine learning remains an ambiguous concept among more than half of the respondents.

Regardless, data has become the new black gold in recent years, according to some experts. The entrepreneur in this data-driven economy relies on information derived from collected data to make more informed decisions. It wouldnt be surprising for a business to invest heavily in software and other solutions built on sophisticated neural networks.

Creating such networks is no easy task. Whether feed-forward or recurrent, a neural network must be capable of learning as it feeds on more data. It also has to learn new things in a period measured in days, if not seconds. By contrast, the human brain takes years for something to become second nature to a person.

Central to this effort is the machine learning engineer. It has grown to become the most in-demand profession in the U.S., with related job opportunities spiking by 344% in 2019. Heres an in-depth look into the role of a machine learning engineer and the reasons for the jobs increase in demand.

To say that a machine learning engineers job is similar to a computer programmer is a dichotomy. While performing programming to an extent, a machine learning engineers task is to develop the machine to perform tasks without being explicitly told.

Computer programming takes rules and data, and then turning them into solutions. Meanwhile, machine learning takes solutions and data, and then turning them into rules. Furthermore, computer programming can develop a general-use calculator, while machine learning can develop one for a specific niche.

Machine learning engineers work closely with data scientists and software engineers. They create control models using data that are derived from the models defined by data scientists, allowing the machine to understand commands. From there, the software engineer designs the user interface from which the machine will operate.

The final product is software, like cnvrg MLOps, combining best practices from DevOps, software development and I.T. operations, and machine learning engineering. Organizations tend to spend more on infrastructure development when a machine learning-ready software can provide a precise estimate on how much they need.

Machine learning engineers have a diverse skill setwith some skills encompassing those found in data scientists and software engineers. Its usual for one to graduate from college and begin working with some skills missing since theyll learn these skills as they move up the career ladder anyway.

The necessary skills for machine learning engineering fall under any of the four categories.

As mentioned earlier, the end product of machine learning engineering is software. Still, its applications are far and widebeyond predicting business trends and auto-filling search terms.

For instance, Stanford Universitys Autonomous Helicopter Program demonstrates the feasibility of teaching an aircraft flight. Researchers installed a system that uses reinforcement learning on a Yamaha R-50 helicopter. It managed to perform stunts a human-crewed helicopter would have difficulty doing, if not impossible to do, continually correcting its course with each pass.

Similar autonomous technology found its way in the drivers seat of Googles self-driving vehicle. Described as on the bleeding edge of artificial intelligence research, the car learns from human behavior on the road to drive. While the technology wont replace human drivers anytime soon, it shows the possibilities machine learning engineering is turning into reality.

Its safe to say that machine learning engineers fill capability gaps among software engineers and data scientists. When these disciplines work together, they create technologies previously thought impractical or impossible. No doubt that theyre paving the way to the future.

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What Is the Role of a Machine Learning Engineer? - TechSpective

Regional Cancer Care Associates, based in New Jersey, has more than 20 locations throughout New Jersey, Connecticut, Maryland, Pennsylvania and the Washingtonarea. Staff realized they needed a risk-stratified list of patients for COVID-19 vulnerability that nurses could manage through phone calls and by coordinatingservices with other providers.

THE PROBLEM

Because of staffing challenges, the list had to identify only the high-risk patients who staff needed to manage first, not the entire population or those patients who could wait a bit longer for nurse outreach.

"Even though we already had an indigenous and independent scoring logic/mechanism for patient risk, this was mainly based on a combination of comorbidities that differentiated it from the usual scoring techniques," explained Lani M. Alison, vice president of quality and value transformation at RCCA.

"Thus," she said, "there was a need to further stratify the risk patients for COVID-19 vulnerability and to establish a patient-centered assessment and outreach."

On another note, staff observed challenges in assigning these patients and a defined patient roster to care coordination executives or support staff, which was hindering a patient-centric outreach approach, Alison added.

PROPOSAL

RCCA turned to artificial intelligence-based health IT vendor Health EC to help address the challenges.

"HealthEC was able to run their machine learning algorithms to identify the patients at highest risk for COVID-19 and therefore focus our care coordination resources," Alison said. "Algorithms re-stratified these patients and assigned a ranking to each patient with an associated risk score."

Lani M. Alison, Regional Cancer Care Associates

The result was a defined patient list that enabled the RCCA team to reach the highest of the high-risk population. The list proved very helpful, and it became an essential part of RCCA's care management documentation platform. It helped focus initial care management calls and increase the effectiveness of the team.

"RCCA also used the list to streamline the COVID-19 huddles and provide this information to practice administrators at each of our sites to help manage patient outreach, mitigate the risk and provide educational information," she said.

MEETING THE CHALLENGE

Data was aggregated from claims, clinical, labs and HIE data sources into the universal data warehouse used by HealthEC. This created a longitudinal, 360-degree view of the patient.

"This single longitudinal view gave us easy access to all the patients' care records and pooled data, including demographics, vitals, diagnosis, etc., from different sources, like the EHR, claim files, CCDAs and ADTs," Alison explained.

"Users were able to have access to patient clinical information without jumping around into different modules. It created a one-stop shop."

HealthEC's Care Connect Pro empowered RCCA staff to stratifyhigh-risk patients (10% of its entire population), not only for COVID-19 risk management, but also for better care management overall, she said.

"Care coordinators, nurses and staff used the CCPro tool to document patient outreach, education material and medication management," she said. "Each patient was assigned a dedicated care coordinator to help mitigate the risk of hospitalization."

Along with the aforementioned clinical data, diagnostic information was added for integrated patient care plans with LabCorp data. This ensured a real-time dynamic flow of information that proved crucial for physicians to design a care pathway or to decide the next milestones of a care plan, she added.

Data received from CRISP theChesapeake Regional Information System for our Patients, the area's HIE was also processed and synchronized into the system to ensure real-time availability of admissions and discharge information.

That is all part of phase one:patient identification. Phase two is interventions and outcomes. This phase requires RCCA staff to:

RESULTS

RCCA reports success with three key metrics.

First, billable transitional care management and chronic care management services now live in some of the practices.

"With targeted patient outreach, patient-specific CCM and TCM, and customized COVID-19 assessments, services were made available to patients after running rigorous risk-stratification protocols to filter out high-risk patients; 10% of the identified entire high-risk population for COVID-19 was validated by the practice by outreach and tele-connections," Alison explained.

Second, improvement in pain and advance care planning measures.

"We had timely interventions to close care gaps," Alison said. "The ACP measure requires patients to report the status of pain within 48 hours. The real-time pain assessments and scores help to close care gaps and ensure the patients are contacted within a specific time interval, 48 hours, to ensure patients' pain was brought to comfortable levels and satisfy the measure compliance."

And third, access to CRISP (Maryland's health information exchange) proved to be a game changer for the provider organization.

"Ease of integration was key," Alison said. "Embedding and onboarding of data from multiple sources, like EHRs, HIEs, claims, CCDAs, etc.,was a big plus to provide caregivers easy access to all types of data in one single place."

ADVICE FOR OTHERS

"Targeted patient outreach using preprocessed and intuitive data sets formed as a result of the summary of various clinical and nonclinical information can help optimize the utilization of staff or resources and thereby ensure better care outcomes and patient satisfaction," Alison advised.

"Inferences from data analytical tools work best in scenarios where data flow is not intermittent but continuous, real-time and unbiased, or deduplicated," she said. "In order to derive definitive insights that can help in decision-making and planning for the organization, the quality and quantity of data inputs is very critical."

Twitter:@SiwickiHealthITEmail the writer:bsiwicki@himss.orgHealthcare IT News is a HIMSS Media publication.

Read more from the original source:
Machine learning helps cancer center with targeted COVID-19 outreach - Healthcare IT News

Immunai is a biotech company using machine learning algorithms that combine single-cell genomics to empower the human immune systems high-resolution profiling. Based out of New York, this company was established merely three years ago, but it is growing at a breakneck pace with the largest dataset in the world for single-cell immunity characteristics. Recently, the startup managed to raise a whopping $60 million in Series A funding. The total number of funds raised now stands at $80 million. With its machine learning algorithms, Immunai has already powered the existing immunotherapies with an enhanced performance level by bettering the analysis of an individuals immunity. It is now ready for a new dawn. With the help of the new funding received, Immunai will delve into the arena of creating new therapies altogether with the help of its vast expanse of data and advanced machine learning algorithms.

The human immune system has been a highly researched topic, and with the onset of the pandemic, the reprogramming of immunity has been under the limelight. To get an in-depth analysis of the same, Immunai makes use of the multiomic approach which helps in the layering analysis of the various types of biological data available. What makes Immunai stand out from the crowd is that it uses and combines the richest data sets. These data sets are procured from the best immunological research organizations from across the globe with machine learning algorithms designed to deliver analytics at a never seen before pace.

Immunai has two great co-founders in its ambit: Noan Solomon and Luis Voloch. Both the founders have extreme knowledge in computer science as well as artificial intelligence. Their efforts right from the beginning were aligned towards the usage of machine learning technology in the field of immunology.Prior to the funding, the main job being done at Immunai was the observation of cells. In contrast, now, they will observe the cells and perturb them to see the aftermath. The machine learning algorithms being used at Immunai allow them to evaluate an approach practically. This makes their model more feasible and influential in the real world.

After successfully understanding the human immune profile, the next step will be to administer new drugs to help fight potential diseases. To understand it better, we can take the example of Google Maps, wherein initially, it takes years to understand the road mapping solely. Similarly, as of now, Immunai is working on understanding the different pathways present in the immune system with the help of machine learning. Once done, the roads and paths underdeveloped or those that havent been built can be given a lending hand. This will eventually lead to a healthier world more armed to fight any disease, even like the pandemic that we are faced with within the current scenario.

One major milestone that any immunotherapy needs to achieve is finding the right immunotherapy for the right patient. This poses a herculean task given the complex structure of the human immune system, but with the advancement of the machine learning models, one can expect to overcome this roadblock soon.

Source:https://www.immunai.com/

Suggested

Link:
This Biotech Company Combines Single Cell Genomics with Machine Learning (ML) Algorithms To Enable High Resolution Profiling of the Immune System -...

The immune system at its core is a complex system of cells, organs, and tissues. These components work in unison to fight infection in the form of microbes. Developing an understanding of how this intricate system works is critical in ensuring that society as a whole has adequate immune health to combat disease and infection.Immunaihas built the largest database for immunology in the world using machine learning and AI to map the entire immune system at a granular and specific level. This data can be leveraged by the healthcare industry to provide better therapeutics that get to market faster. This understanding will also allow biotech companies and pharmaceutical manufacturers to radically personalize therapeutics in the future. Immunai is initially focused on the oncology market but the offering is versatile can be applied to things like autoimmune disorders and infectious diseases like COVID-19.

AlleyWatch caught up with CEO and Cofounder Noam Solomon to learn more about the impact that Immunai is having in the understanding of the immune system, the companys partnerships, experience fundraising during the pandemic, latest funding round, and much, much more

Who were your investors and how much did you raise?

This $60M Series A round was led by Schusterman Family Investments, Duquesne Family Office, Catalio Capital Management, and Dexcel Pharma, with additional participation from existing investors Viola Ventures and TLV Partners.

Tell us about the product or service that Immunai offers.

Immunai is on a mission to reprogram the immune system to advance personalized medicine to better detect, diagnose, and treat disease. To do so, Immunai has generated the largest proprietary database for immunology in the world, known as the Annotated Multi-omic Immune Cell Atlas (AMICA). This platform incorporates variables such as clinical lab metadata (e.g., processing wait time) and batch data (e.g., hospital), and others; then, it leverages machine learning and artificial intelligence to complete the annotation and characterization of immune cells. Immunais team of computational biologists and immunologists work with our partners at pharmaceutical companies to figure out the implications of what Immunai has found, whether its a new therapy, a drug combination, or a diagnostic.

What inspired the start of Immunai?

When I met my cofounder Luis, I was a math postdoc at MIT and Luis was working to apply machine learning to biology. Together, we wanted to bring transfer learning AI methods to what we believe would solve the biggest problem in society today disease.

All disease can be traced back to the immune system. But what we realized is that pharmaceutical companies dont have access to any comprehensive, granular insight into how the immune system works, how it responds to the drugs or therapies theyre developing, and what patients are most likely to benefit. With our scientific cofounders, Ansu Satpathy (assistant professor at Stanford for cancer immunology), Danny Wells (researcher at the Parker Institute for cancer immunotherapy) and Dan Littman (Professor at NYU and HHMI investigator) we realized that with single-cell technologies we would be able to measure and map the immune system with granularity and specificity like never available before.

At Immunai, weve combined the brightest minds across single-cell genomics, data science, and engineering to build the largest proprietary database on immunology in the world. We hope our work will lead to a better understanding of how to overcome the key unsolved problems and bottlenecks in immunotherapy discovery and development. We want to enable the development of more effective therapies and combinations for each patient, accelerate the ability to bring these therapies to market, and ultimately, provide better options for patients at a faster pace than ever before.

How is Immunai different?

No one is doing exactly what were doing. Companies have been trying to understand the immune system for years, but have been limited by traditional bulk sequencing technologies, which dont provide nearly enough data. By analyzing gene expression levels, protein markers, TCR and BCR fragments, and other single-cell omics, weve compiled 10,000 times more data for each immune cell than others before, giving partners a view of the immune system with a full spectrum of color and dimensionality.

Further, our proprietary machine learning and single-cell analysis that we apply to mine AMICA , the worlds largest proprietary Multiomic Immune Cell Atlas, allow us to understand the immune system at scale with unprecedented granularity and consistency. This provides a solution to the prohibitive batch effect problem that our competitors have not been able to solve.

What market does Immunai target and how big is it?

Immunais offering can be applied to multiple disease areas from cancer to autoimmune disorders to infectious diseases like COVID-19. The company is primarily focusing on the oncology market, which is currently set to surpass $469.5 billion by 2026.

Whats your business model?

Immunai partners with biopharmaceutical and biotech companies to answer critical questions like what makes T-cells expand, persist, and penetrate a tumor, which cells are cytotoxic, which cells in a cell therapy drive response, what are the immunological signatures that are more likely to lead to clinical response to different therapies, and more. These partnerships are usually structured as milestone-based collaborations, ranging from prospective clinical trial design and biomarker discovery to earlier target discovery and target validation.

How has COVID-19 impacted your business?

COVID-19 has impacted the way we work and the pace at which we work. Weve asked our employees who are not working in the lab to work from home and have implemented strict social distancing protocols within the lab. In the biopharma world, business is bigger than ever before, so we have many new partnerships in a variety of disease areas, including Immuno-Oncology, Autoimmunity, Neurodegenerative diseases, and infectious diseases .

What was the funding process like?

Fast but complex. It happened over a few very eventful months, with many important partnerships forged and multiple parties involved in the financing round, which all took place during a worldwide pandemic, of course.

What are the biggest challenges that you faced while raising capital?

The financing round happened as we were closing a few important partnerships, so running both responsibilities as CEO was non-trivial. In the middle of it all, life happened, and we had to deal with family health issues, including the fact that my wife and I had caught COVID, but we were both fine, luckily.

But what I didnt expect from the pandemic was being able to raise $60M without meeting the lead investors face to face. This is something that frankly, I didnt expect happening, and definitely didnt expect would happen so fast.

What factors about your business led your investors to write the check?

Our investors have witnessed the accelerated growth of our platform and are aligned with our vision to reprogram immunity. Machine learning crossed with genomics will unlock the mysteries of the immune system and lead to improved therapies. To actually execute on this vision, a world-class team is required, and weve put it together.

What are the milestones you plan to achieve in the next six months?

Were going to use this new financing round to build and improve our platform. With our expansion into functional genomics, well be funding collaborations with partners to answer the most pressing questions in immuno-oncology, cell therapy, infectious disease, and autoimmunity, including key biology driving clinical endpoints and target discovery.

We also plan to invest heavily in growth and double our team of 70 by year-end. We currently have a large lab in New York with 50 scientists working on sequencing and tech development. Were looking to add more people to the team to develop new assets and IP.

We also plan to invest heavily in growth and double our team of 70 by year-end. We currently have a large lab in New York with 50 scientists working on sequencing and tech development. Were looking to add more people to the team to develop new assets and IP.

What advice can you offer companies in New York that do not have a fresh injection of capital in the bank?

Understand the essence of what youre building and bring it to market quickly. Lean Startup is one of the most important business books Ive read; its critical for any business, but particularly for one with a limited runway. Whats the most expeditious experiment you can run to see if your customers actually care about your product.

Where do you see the company going now over the near term?

Were transitioning from observational genomics to functional genomics. Were concentrating on two major projects: improving the ability to target new checkpoints and validate targets for cell therapies. Just in the last year, weve been able to identify new mechanisms of resistance with partners in record time. At this pace, we hope the work well be able to do in the next couple of years will be groundbreaking and life-saving, but its too early to say specifically where well be.

Whats your favorite outdoor dining restaurant in NYC

Cafe Mogador on St Marks.

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Immunai Raises $60M to Decode the Immune System with Machine Learning and AI - AlleyWatch