Radiologists Archives - InnoHEALTH magazine

AI and digital health – the key drivers of the next healthcare revolution

InnoHEALTH magazine digital team — Wed, 24 Aug 2022 10:47:12 +0000

Digital health and AI is one of the hot topics that people within and outside of the healthcare industry have been talking about recently.

Technology has been at the heart of the healthcare revolution for years. The evolution of the internet, mobile networks, smart devices, etc., have significantly changed the process of delivering patient care. Gone are the days when people had to wait for hours for a 15-minute doctor consultation. Today, doctor consultation is just a matter of a phone call. Patients can now have physician consultations from the convenience of their homes. And this is just one example of how much technology has transformed healthcare. Digital health and AI is one of the hot topics that people within and outside of the healthcare industry have been talking about recently. This article discusses how AI and digital health is fuelling the next healthcare revolution while redefining healthcare delivery.

What is digital health?

Digital health is a multidisciplinary concept that uses communication and information technologies to help health systems deliver affordable and good-quality care. Digital health also enables healthcare institutions to be efficient and sustainable. Telehealth, mhealth, and wearable devices used to monitor patient status are some of the real-time examples of digital health.

AI or Artificial Intelligence

AI is one of the greatest breakthrough technologies that is changing our everyday life experiences, right from avoiding traffic and online shopping to hospital experiences. While we are already experiencing AI-induced changes in our lives, like personalized movie or music recommendations, and voice-controlled personal assistants such as Alexa and Siri, let us dive in to understand how AI and digital health is transforming the healthcare industry and patient care.

AI can ease the process of detecting invalid claims and accelerate the pace of claim – assessments, processing, approval, and payment through automation.

How AI and digital health is transforming healthcare

1. Optimized workflow scheduling and case prioritization

Workflow management and case prioritization are highly critical in the healthcare domain, as a patient’s life is at stake. The sooner the diagnosis, the earlier the treatment. AI and ML algorithms can aid in optimizing workflow, prioritizing cases, and managing patient flow. Healthcare professionals can navigate hundreds of medical records and identify critical and non-critical cases within seconds using AI and ML algorithms. This will help physicians and radiologists to focus on critical cases on priority and create workflows and patient flows accordingly. Furthermore, AI can also help in automating time-consuming manual tasks like patient data entry, medical claim scrubbing, etc. AI can also facilitate personalized responses to EHR searches.

2. Quick drug discovery

It takes years, or even decades, to synthesize the appropriate drug for an illness. Chickenpox, for instance, was identified in the 1950s. But it took almost four decades to discover the right vaccine for the illness. With AI, drugs for contagious diseases can be discovered and synthesized within months, if not days. A real-time example of how AI can speed up the drug discovery process is the recent pandemic, COVID-19. Almost within a year of the COVID-19 outburst, a relevant vaccine was developed and distributed across the globe with the help of AI.

3. Quantitative imaging analysis and reporting

Have you ever got a different diagnosis from different doctors for the same problem? If yes, then you probably might have thought which one of the diagnoses is correct. Such different diagnosis from different doctors is primarily due to subjective analysis and reporting. Almost all medical images are studied in a subjective manner – based on the radiologist or physician’s personal opinions, point of view, and interpretations – and have often led to diagnostic errors and incorrect/unnecessary treatment. The shortcomings of subjective analysis and reporting can be overcome only through quantitative imaging analysis and objective reporting.

Quantitative imaging analysis, as the name suggests, is the process of measuring all the elements in a medical image and comparing it against anatomical values to identify deformities and determine the severity of illness. Reports generated from quantitative imaging analysis are known as objective reports and hold detailed information about every element of the scanned body part, including their measurements. However, performing quantitative imaging analysis and generating objective reports is a time-consuming task, which is why most health centers are unable to perform them. But AI can help health institutions perform quantitative imaging analysis quickly and accurately.

4. Robot-assisted surgery & Virtual nursing assistant

Robot-assisted surgery enabled by AI can help surgeons to perform complex procedures with increased precision, flexibility, and control as compared to conventional techniques. Robot-assisted surgery is gaining more prominence in healthcare and the market size of robotic surgery is expected to cross $7 billion by 2025. Similar to robot-assisted surgery, AI-powered robots can serve as virtual assistants to provide 24/7 support for chronic conditions, monitor patient status, check medication intake, and schedule doctors’ appointments, just the way a nurse practitioner would do.

5. Fraud detection

AI can help in ensuring the security of patient data, which is highly sensitive and at the heart of providing secured and personalized patient care. Also, AI can ease the process of detecting invalid claims and accelerate the pace of claim – assessments, processing, approval, and payment through automation.

According to a report, AI adoption can help US healthcare providers save nearly USD 150 billion by 2025.

6. Precision medicine

Precision medicine, also known as personalized medicine, is a medical model where healthcare is customized according to the genetics, lifestyle, and environment of a person. Precision medicine is the exact opposite of the one-drug-fits-all medical model. Numerous studies and experiments have been carried out to understand the potential of AI in precision medicine. And in most cases, AI has been able to classify and solve precision medicine problems in aspects like disease detection and prediction, treatment optimization, etc. Most healthcare providers and professionals believe that AI will take precision medicine to the next level and improve the levels of accuracy and prediction in patient outcomes. They also believe that AI can help in making precision medicine affordable and available to people from rural areas.

Summing Up

AI and digital health are not only beneficial for patients but also providers. Many stakeholders and industry leaders posit that the digital health and AI market is growing with a great potential for ROI. According to a report, AI adoption can help US healthcare providers save nearly USD 150 billion by 2025. Not just that, with medical imaging data growing abundantly and projected to double by the next decade, AI will be the only solution to handle the healthcare data explosion.

Studies state that the growth of AI in healthcare will be driven by the increasing volume of data. Matter of fact, numerous health systems have started embracing AI applications not just in patient-facing clinical processes, but also in diagnostic workflows and tasks associated with medical images. According to a KPMG survey, 89% of respondents stated that AI is already enhancing the efficiency of their systems.

Many health tech firms are moving from cancer diagnosis and focusing on creating AI algorithms for other health issues like chronic neck and back pain. Synapsica, for instance, is a leading AI health tech firm that has successfully built AI tools for spine problems and injuries. Using Synapsica’s AI tools, Spindle and SpindleX, radiologists can perform quantitative imaging analysis and generate objective reports within minutes. The healthcare industry is one of those industries that transform almost every decade. The current healthcare industry is highly different from what it was a decade ago and will be highly different from what it is now in the next decade. And AI and digital health will be the key drivers of the next healthcare revolution.

Composed by: “Dr. Cherian is presently associated with AIIMS, New Delhi. He is leading all medical operations, research and product vision for Artificial Intelligence. He has established many startup teams for pan-India operations.”

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Spine problems becoming an all-age problem – How AI is helping

InnoHEALTH magazine digital team — Tue, 05 Apr 2022 09:01:50 +0000

The next endemic likely to affect India is the increasing incidence and prevalence of spine problems.

The next endemic likely to affect India is the increasing incidence and prevalence of spine problems. Ranked #1 for YLD (Years Lived with Disability) in Global Burden of Disease, a survey conducted since 1990 by the World Health Organization (WHO), low back pain and neck pain, are becoming an all-age problem, affecting almost all aspects of a person’s life – right from socializing to productivity loss at work to lack of sleep.

Spine degeneration starts as early as between the age of 20 to 25.

60% of Indian population suffers from spine ailments

Back pain is no longer an age-related problem. It is becoming a problem of lifestyle, affecting people of all ages – from youngsters to the elderly population.

Spine degeneration starts as early as between the age of 20 to 25. It is estimated that every fifth Indian youngster (under the age bracket 20 -30) is suffering from some sort of spine ailment.

Increased usage of smartphones and laptops has augmented the sedentariness in lifestyle, which worsened during the COVID-19 pandemic, as the masses were forced to operate indoors for a prolonged time.

Dr. DV Sharma, a renowned orthopedic surgeon, posits that “ Work from home and online classes is the latest culprit increasing the incidence of back and neck among the Indian population.”

Back pain – a ‘ tough nut to crack ‘

Narrowing down the root cause of back pain is a tough nut to crack for two reasons:

* Spine problems can be the result of a single problem or a combination of problems

* Lack of consistent diagnostic and treatment protocols for spine-related issues

Also, pain is a subjective experience. Consider two people with the same condition, say spinal stenosis – while one might feel the pain to be mild, the other might feel the pain to be severe. Some spine ailments, for instance – disc herniation, are mostly asymptomatic till the condition worsens, making it hard for physicians/radiologists to diagnose spine problems at an early stage.

AI in radiology

The shortage of radiologists is rising at an alarming rate and is one of the biggest challenges affecting the performance of healthcare institutions. Nearly 30k – 50K imaging facilities are currently operating in the sub-continent. But the radiology population is less than 10K. All of which lead to increased diagnostic errors and overworked radiologists. Despite the evolution of healthcare technology, the healthcare industry is still struggling to curtail medical errors. Over 5.2 million medical errors are recorded in India every year.

Communication gaps, missed diagnosis, delayed diagnosis, poor information flow are some of the primary reasons that trigger medical errors and also play a major role in medical malpractice in India. AI-powered radiology can help healthcare institutions worldwide deal with the increasing shortage of radiologists, let alone India. AI can also aid in minimizing medical errors and malpractices, while improving clinical communication and information flow. Many healthtech firms are investing heavily in AI for better radiology. Nearly 94% of Indian healthcare leaders are thinking of investing in AI innovations.

AI for spine problems

Since spine problems are a constellation of symptoms, it requires more time and effort to identify the root cause of the problem. Given the dismal shortage of radiologists and the constantly increasing imaging procedures, radiologists are pressed to work on a case within a tight timeframe. On an average, radiologists are able to spend only 3-4 seconds on an image, which naturally increases the probability of missing out or overlooking details that might be of critical value for treatment or surgical planning. Quick and thorough analysis of an image, without missing out on details, requires a greater potential. It requires intelligence that mimics human intelligence and can outperform human potential in mundane tasks. This is where AI comes into the picture.

Already under the experimental phase and commercially used for some pathologies, AI is proving to be instrumental in diagnosing and generating objective reports for spine ailments.

AI assistant for spine MRIs

AI can not only help in medical imaging analysis but also in recording the findings with objectivity. Already under the experimental phase and commercially used for some pathologies, AI is proving to be instrumental in diagnosing and generating objective reports for spine ailments. For instance, Spindle – an AI-powered smart assistant for spine MRIs, can automatically identify and report age-related degenerative pathologies from MRI scans of the spine. This significantly reduces clinical reporting workload as degenerative abnormalities are present in almost all spine MRI scans.

AI assistant for stress motion x-rays of the spine

Not just MRIs, AI algorithms are currently trained for almost all types of radiology exams – say, CT scans, motion x-rays, etc. SpindleX, for instance, is an AI assistant for stress motion X-rays of the spine that can help in grading the severity of loss of motion segment integrity, measuring baseline angles and abnormal translation/rotation motion of spine segments.

Using SpindleX, radiologists can carefully study a wide range of traumatic injuries in spinal ligaments and create objective reports in just a click. After X-rays, artificial intelligence is the next biggest breakthrough in radiology. With the healthcare industry becoming increasingly dependent on medical images and spine problems growing into a socio-economic problem, AI is likely the last hope for healthcare institutions to provide better patient care. Particularly in India, where 68% of the total population lives in rural areas, AI can improve access to healthcare and imaging facilities, so much so that even the resource-poor regions will have a chance at better patient care.

Composed by: “An alumni of IIM, Ahmedabad, Meenakshi Singh, has profound experience in building data analytics solutions for various industries. Hailing from a computer science background, Meenakshi has worked with some of the most reputed industry players like Google, LoanIQ, Manhattan associates and Goldman Sachs. Currently associated with Synapsica as the CEO, Meenakshi leads efforts for clinical validation and international regulatory compliance of Synapsica products and is also responsible for fundraising and marketing.”

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IIT Kharagpur Develops Diagnostic Tools for Lung Diseases

InnoHEALTH Magazine — Wed, 26 Jun 2019 11:00:13 +0000

IIT Kharagpur deveops diagnostic tools for interstitial lung diseases and lung cancer
Researchers at the Department of Electronics and Electrical Communications, IIT Kharagpur have recently developed a decision support system to diagnose cancerous tumors and other diseased tissues in human lungs. While one system can refer to CT scan images to detect lung nodules and test them for the possibility of malignancy, the second software can detect interstitial lung disease patterns in chest HRCT images. The team has successfully tested both software systems at AIIMS, New Delhi with a success rate higher than 80%.
The advantages of these newly developed systems include their use of non-invasive and comparatively affordable methods of image analysis that would aid the radiologists to identify malignancies by reading growth in the lung nodules. The system can also identify interstitial disease patterns in HRCT images showing the lung tissue texture. Another added benefit is that the reference point is India centric, i.e. the medical images can database used for reference is taken from the Indian patient population. Data has been taken from PGIMER Chandigarh; mainly biopsy cases and foreign database such as LIDC-IDRI and MedGIFTILD have been used.
The malignancy detecting tool detects a lung nodule, segment the nodule and provides a way to modify segmentation, retrieve similar nodules from the database with their report assess the chances of malignancy of the nodule in question based on the retrieval results. The ILD tool is developed by incorporating feedback from expert radiologists to make it easy to use for non-techsavvy clinicians. The software has important modules like automatic segmentation of lung boundary, retrieval of similar segments from the database with their report and assess the probability of the pathological segment to be a particular ILD category based on the data retrieved. The mapping of disease is performed by doctors based on the ILD pattern and clinical inputs.
At present, the lung nodule detection rate and classification rate is 86% and 87%, respectively.The success rate for ILD classification is 84%. The researchers are working to further improve to conduct clinical trials on bigger sample sizes. The report has been published in more than13 International Journals and19 International Conferences in various stages of its development.

SOURCE: www.inshorts.com

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Innovative imaging solutions by Philips

InnoHEALTH Magazine — Fri, 04 May 2018 11:03:21 +0000

As healthcare organizations continue to move toward value-based care, there is a growing need for intelligent solutions to help address the biggest challenges radiologists face in improving outcomes, simplifying data and insights gathering, lowering the cost of healthcare delivery, and enhancing patient and staff satisfaction.

Philips India Ltd, a leading health technology company,never fails to bring something new for the medical fraternity and so it recently announced the launch of its new range of next genera- tion imaging solutions namely Access CT 32 Slice, Ingenia Prodiva 1.5T MRI and Dura Diagnost F30 Digital X-ray which are customised products supported by artificial intelligence to help radiologists bring about better and faster diagnosis with more accuracy and also improved and better patient care and experience. These imaging solutions have user-friendly interfaces which areeasy to operate and take less- er preparation time for patients. Their other positives are high image quality, streamline workflow and reduce variability thus providing better diagnostic outcomes for radiologists. All these Imaging System solutions have their own unique features. Access CT offers flexibility, exceptional image quality, and diagnostic accuracy with lower total cost of ownership. Ingenia Prodiva MR is built on proven dStream digital broadband technology, present in over 2000 installations worldwide which can accelerate patient throughput with a simplified Breeze Workflow. It has an intuitive user interface enhanced by guided and standardized examination procedures helps perform routine MRI exams from day one. DuraDiagnost F30 Digital X-ray provides ease of entry into the world of digital radiography with a price to performance ratio that’s hard to beat. It also helps simplify the path to clinical decision making, improves patients’ outcomes and reduces the radio- diagnosis department’s burden.

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FIRST DIGITAL BREAST TOMOSYNTHESIS

InnoHEALTH Magazine — Tue, 09 Jan 2018 10:43:43 +0000

Breast Cancer has been ranked as the number one cancer among Indian women with age adjusted rate as high as 25.8 per 100,000 women, as per the latest statistics published in the Asia Pacific Journal of Clinical Oncology. Bengaluru has ranked third with age adjusted incidence rate of carcinoma of the breast found as high as 34.4 per 100,000 women. Projections for India suggest the number of breast cancer cases will likely go as high as 1,797,900 by 2020. Though there has been an increase in health check-ups among women recently and we can still see that screening is low on priority even in major cities.

Cytecare hospital is the first hospital in the city to introduce Digital Breast Tomosynthesis (popularly known as 3D-Mammography) for women.

Mammography continues to be the only screening test proven to decrease mortality from breast cancer. However, 2D mammography is less accurate in women with dense breasts for whom cancer may be masked by overlapping breast tissue. When a 2D mammogram detects an area of concern, radiologists further investigate with a diagnostic mammogram, ultrasound or biopsy. Looking at the same breast tissue in 3D, the radiologist may now be able to see that the abnormality on 2D mammogram in fact represents normal breast tissue. This avoids further investigation in large number of patients undergoing routine screening.

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3D mammography or Tomosynthesis is a special kind of mammogram that uses several low dose x-rays obtained at different angles to create multiple images or “sequential slices” that step through the breast tissue and allows radiologists to see more clearly. The breast is positioned and compressed in the same way as for a 2D mammogram but the x-ray tube moves in a circular arc around the breast. It takes less than 10 seconds for the scan. The information from the x-rays is sent to a computer, which produces a focused 3 D digital image of the breast.

There is no organised national or regional breast cancer screening program in India. Naturally, majority of those involved in women’s health are not aware of the available technology and expertise in the field of breast cancer diagnosis. Needless to say, women undergoing screening have little understanding of the tests or the results thereof.

In contrast to the western world, breast cancer incidence is much higher in premenopausal age group (<50 years) in India. As compared to both screen film and Computed Mammograms (older but most widely used technologies), 2D and 3D Digital Mammography has significantly higher accuracy in cancer detection in younger women and in women with dense glandular breasts. This will have large impact on decreasing the number of women undergoing unnecessary recalls or biopsies. It is most important to avoid anxiety in women opting for screening tests. 3D mammography serves as an excellent problem solving tool and reduces misdiagnoses. Contrary to common practice, ultrasound alone has significant limitations as a screening tool and must always be combined with a 2D or 3D Digital Mammogram. The x-ray dose of 3D mammography is comparable to that of a 2D mammogram. Therefore the cumulative dose of the combined screening is still below the FDA regulated limit and found to be safe and effective for patient use.

Incorporating this aspect Cytecare hospital’s breast imaging services are complete with Digital Breast Tomosynthesis (DBT or 3D Mammogram), Full Field Digital Mammogram (FFDM), Stereotactic and Vacuum Assisted Breast Biopsy (VABB).

3D mammography has shown to reduce percentage of missed cancers by 15%. More recent studies show about 30% increased 3D mammography sensitivity and specificity compared to 2D Mammography with a recalls reduction in screening by approximately 40%.

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