Tools and Tactics

Some of these machine-based data analysis tools such as Cell Profiler have now evolved from individualistic feature-based characterisation to analysing multiple features like DNA staining, organelle texture and quality of empty cases-based cell sorting. In a similar way, Deep Variant has evolved from genomic information processing to image-based analysis. Google’s TensorFlow (open platform for deep learning algorithms); Atom wise (visualise molecules into 3D pixels referred to as voxels, provides atom- based interactions); answer ALS (a consortium-based approach to combine genomics, transcriptomics, epigenomics, proteomics, imaging and pluripotent stem cell population to target neuro degenerative diseases) and Contour (clustering of cellular imaging on the basis of trends rather than mortality factor alone) etc., are some remarkable newly evolving Deep Algorithm tools.

The over fitting of the model to its training data, which in turn is huge in size, poses a challenge to find ways to classify data to train it more efficiently. Computers have learned to find the needle (required information) in a haystack (huge database)which we as humans fail to identify and process.

During data analysis earlier, we were keeping experimental variations constant, but now with input tools like adding environment design, multiple controls etc., the perspective of data processing has changed completely. Present elaborate databases are analogous to our DNA in which most part looks repetitive and to know exactly why it is there, critical data screening and processing is required by new tools which further get improved through the acquired information and are better prepared for next generation of data.

Also Read: The Vulnerability of Medical Institutions to Cyber Attacks

Human intelligence is a complex simulation outcome of twelve characteristic features that include perception, resolving problems, learn, reason, abstract, plan,decide, understand, feel, act, create, and finally communicate. The human brain can be compared to a processor which processes these features using eight mental mechanisms which include imagery, concepts, rules, analogy, emotions, actions based on intentions, language, and consciousness. These twenty features (twelve characteristic features and eight mechanisms), taken together present the evaluating benchmarks for current AI. The major flaw in our current AI system compared to human intelligence is its limited ability in terms of abstracting and understanding with no feelings. However, AI is expanding its knowledge and processing in an exponential manner. Human-made AI now understands more than its earlier versions. AI is forecasted to pass through phases of knowledge engineering, machine-learning, and contextual marginalisation. It is like acting by rules, making new rules, learning to act again, to evolve new rules.

A simple AI tool such as a calculator can enable a person to use arithmetic for him/her. AI is now providing solutions in natural language processing, complex data sets for agriculture variables, etc.

However, unlike a calculator, it is yet to be accessible both in terms of tools, data set and skill for the public. Networking of multiple disciplines and protected commercial data with limited accessibility are major hurdles in the process. India is getting digitized in a fast manner. Adding Aadhar *unique identification number* was the major step followed by demonetization. As per CIS (India) of 2018, AI will add 957 billion USD by 2035. Health IT is booming with new start-ups every day, but the challenge is integration under streamlined vision. It is important to understand that any solution is sustainable with a participatory approach as exhibited by the evolution of telecommunication globally. Our basic education system is extremely diverse across the country with varied types of public and private players. The variation in state wise curriculum especially other than language is questionable. We can still choose Biology or Mathematics as separate options at our higher secondary education. This itself kicks out the possibility of emerging computational biologists in the future. It took almost ten years to build the world’s largest neuromorphic (neuron-like) computer Dubbed Spiking Neural Network Architecture (SpiNNaker) at the University of Manchester. This machine rethinks the way a conventional computer works. Its major objective is to support existing partial brain models of the cortex, basal ganglia, etc. It can manage 200 quadrillion tasks simultaneously. Still, we have reached only 1 percent of human brain capability that too with many simplified assumptions in the process. SpiNNaker is more comparable to mouse brain which is 1000x times smaller than the human brain. Although we need to explore more to understand the human brain and how it functions but once it is decoded any universal Turing machine can be turned to mimic human cortex actions. Intelligent and super-intelligent machines are our future and will make our life more convenient. Still, there are numerous challenges and ethical issues during these developments. We are moving towards a stage where time will become the ultimate currency governing all the aspects of human life.

About the author

Dr. Sarita Jaiswal, an ex-research officer at University of Saskatchewan, Canada, is an accomplished Plat Scientist having 15+ years of R&D experience with specialization in cereal and pulse crop biochemistry and genomics. She has been awarded twice for the category of Young Scientist (Indian Society of Plant Physiology and amp; KK Nanda Foundation for Advancement of Plant Sciences).

(Hony) Brigadier Dr Arvind Lal with his keynote address described that only 10% of overall healthcare constituents diagnosis.He discussed the emerging trends in Diagnostics by putting forward four questions

• What is happening in healthcare?
• How will that affect us as pathologists?
• What are the emerging technologies?
• What can you do to better prepare yourself?

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Many trends relate to technological advances & the necessity to increase quality & efficiency. The new factor for the IVD industry, not previously seen, is the influence of educated & informed healthcare consumers. Clinical lab testing is one of the greatest bargains in healthcare but often draws the attention from cost-cutters. Lab professionals also will increasingly capitalize on these emerging opportunities & options.

The whole paradigm of healthcare is shifting and will continue to shift—from technological advances like electronic health records. This will change the historic doctor/patient relationship marking the onset of an era of patient empowerment in which the patient shall become more responsible for maintaining good health.

The Healthcare sector, in India, is at an inflection point and is poised for rapid growth At a CAGR of 21% – we will have an industry of 300 billion US $ by 2020. A combination of demographic and economic factors is expected to drive the growth of the sector.

The main points addressed the need to use the emerging trends in diagnostics such as

• Wellness Health Model – the power of diagnostics to change the focus of healthcare from treating sickness to promoting wellness
• The rise of the corporation – Consolidation & Automation
• Role of PPP in Rural Healthcare – a private-private partnership
• Need for Accreditation – Quality of samples and testing
• Consumer-centric healthcare – Handheld devices and POC (Point of Care) testing
• Growing in Digitisation

Dr. Ravi Gaur wanted the healthcare facilities available in metro cities should reach the remote areas too, that’s where innovations in diagnosis should take us. Dr. Vidur Mahajan’s company validates clinical test with their expertise and team of researches.

“Simple solutions for complex problems” is the lateral thinking behind Pawan Asalapuram who is currently working on eradication of TB in the country by his innovations in diagnosis methods. Use of his product, PCR, TB can be recognized in just 90 mins which usually takes 3 to 6 months.

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Dr. Richa Dayal suggested continuous monitoring devices. She then spoke on the data analysis part, where the study shows that India is researching on the data outsourced instead of self-published data as we lack in data. She demanded the support of policymakers in the area of diagnostics.

Mr. Anoop Shaji, final year undergraduate student He has delivered dozens of award-winning scientific presentations at various international, national scientific meets on his original novel, indigenously developed, potentially promising conceptual models. Anoop Shaji was the only dental student selected among 160 students all over India from all UG streams to be invited as a delegate to first ever Nobel prize series in connection to vibrant Gujarat 2017, which was presided by 9 Nobel laureates and the prime minister of India for conceptually developing a potential breakthrough for brain cancer treatment.

He became the voice of the young innovators in the conference and emphasized on POC (point of care) diagnosis. His current work is on a simple device detection of cancer with just one single drop of blood. Lab on the chip would be a boon in advancements in diagnostics and insisted on continuous efforts on it along with the financial and legal support.