Artificial Intelligence & Deep Learning for Affordable Medical Diagnosis

“Artificial Intelligence, Machine Learning, Deep Learning- Are capable of diagnosing diseases beyond the human expectations!”

A team of international academics from hospitals in the United Kingdom, the United States, and Switzerland ensures that Artificial Intelligence technology can now be as accurate in detecting diseases from medical images as human health experts.

After doing a review of all the existing scientific literature comparing the performance of AI models and health professionals published between January 2012 and June 2019, it was found that algorithms correctly detected the disease in 87 percent of cases in the image sample, compared to 86 percent achieved by human experts.

Experts are confident that new reporting standards that address specific Artificial Intelligence challenges could improve future studies, allowing greater confidence in the results of future evaluations of this promising technology.

“To the best of our knowledge, this is the first systematic review and meta-analysis on the diagnostic accuracy of healthcare professionals versus deep learning algorithms using medical imaging,” the experts note.

#The use of Artificial Intelligence & Its Subsets in Medical Care

Empowering machine learning applications, deep learning principles, and natural language processing concepts, AI is enabled to leverage the technological advancements in each and every industry vertical.

As we see, Artificial Intelligence can be applied in many different ways, but the best way to understand its potential use in healthcare is to divide its applications into three categories:

#Algorithmic Solutions
#Image Processing
#AI-Powered Tools (for medical practice)
[Prefer Reading: “AI-ML and Data Science: Technology Becomes Ally to Overcome Corona Crisis.”]

1. Algorithmic Solutions:

Today, wherever Artificial Intelligence has embarked its footprints, all those widely used applications are algorithmic which adhere to evidence-based approaches programmed by researchers, scientists, and medical experts.
A simple procedure is followed:
“Integration of data into algorithms is done by humans and data extraction and problem-solving is done by a computer.”

• The algorithms and programs execute the computerized medical records
• Myriad of treatment alternatives are produced to review
• The most appropriate combination is specified as a recommended solution
• Results at the detection phase are rendered in real-time
• Thus clinicians help are provided help in decision-making

2. Image Processing

We can understand that the human eye has tendencies to fail and we can say that for even the best clinicians, expert medical professionals, and proactive researchers. And there are chances of disagreement of personnel over the interpretation of results from their unique ends.

Visual pattern recognition, embedded in the software tools supported by Artificial Intelligence and programmed with the concepts and principles of Machine Learning and Deep Learning have gained power and strength in the Health Image Processing.

These robust software tools have the abilities to preserve tons of structured image data and compare them with the other relevant ones stored applying the heuristic techniques likewise humans and are known to produce 5-10% more perfect and precise outcomes in comparison to an average doctor.

Deep Learning has made a breakthrough in the medical field by its powerful and effective approaches covering most of the diagnostic fields of:

• Radiology (MRI, Mammography)
• Dermatology (Pigmentation therapies, Rash Identification)
• Pathology (Microscopic & Cytological Diagnosis)
• Ophthalmology (Diabetic Retinopathy, Cardiovascular Diseases)
• And many others

3. AI-Powered Tools

Artificial Intelligence and its extended branches offer tremendous and great opportunities and possibilities for the future.

Machines don’t have different minds but every doctor has one with unique thinking and approach. Experts say the question to raise is not on the level of the intelligence of the doctors, rather the need is to stress on:

• The approach a specialist applies to address patient’s problems and,
• The robustness of the healthcare systems that support them

This where technology leads and supersedes humans at some drastic points. Natural Language Processing (NLP) and Deep Learning (DL) are the significant branches of AI which can dramatically help to enhance the performance of physicians and medical personnel at different levels.

NLP, a branch of AI that helps machines understand and interpret human speech and writing. This software can review thousands of complete electronic medical records and elucidate the best steps to evaluate and administer treatment to patients with different diseases.

Deep Learning, a crucial subdivision of the AI technique, makes machines learn human behavior and act significantly in the same way as humans. It involves the use of computers to observe and learn from doctors in their usual performance.

#Contribution of AI and Deep Learning in Detecting Breast Cancer

Did you know?

• In the USA, nearly two million fresh cancer cases are reported in a year.
• One annum witnesses half a million cancer deaths as reported.
• Breast Cancer takes the lead amongst all.
• In 2020, estimated cancer patients would be around 1.8 million and the cancer deaths to be seen in the US would account to 606,520.

AI is spreading its reach almost in every industry and sphere transforming global productivity, lifestyles, working patterns, and much more where no Artificial Intelligence and its descendants are not behind in improving the accuracy to detect breast cancer.

Deep Learning 3D models have helped radiologists to interpret the DBT (Digital Breast Tomosynthesis) images closely with delineated precision, thus, resulting in reduced reading times (almost to half).
“The accuracy of DL Systems depends on the size of the datasets to be processed!”

Under the assistance of AI, deep learning reduces the diagnosis time of the cancer findings from one minute to 30 seconds where a decline time in testing can incline the number of patients to be diagnosed.

With enhanced efficacies, robust technology seems to raise its bar and break its own records in the clinical practices by increasing the level of reading times and lowering the count of false-positive rates.

Moreover, scientists have been given an upper edge over microscopic evaluation of tissue samples that helps to detect the formulation and diagnosis of cancer. Augmented Reality Microscope (ARM) is backed up by Artificial Intelligence and provides valuable information over the tested sample in real-time.

Researchers have demonstrated the comprehensive use of ARM and have favored its relevance and accuracy in determining cases with metastatic breast cancer and prostate cancer, delivering results in real-time

[Prefer Reading: ”What People with Cancer should Know?”]

#Artificial Intelligence Aiding Kidney Care

Kidney related diseases are a huge burden on our economy bringing high mortality as well as morbidity. Many scientists and big market leaders are striving ways to leverage the power of AI technology, taking the best advantage of its subsets (Machine Learning, Deep Learning, etc.)

AI took a step towards Nephrology and succeeds in rendering the best results in detecting severe Kidney diseases.

DeepMinds Machine Learning Model is able to predict nearly 90% of major kidney disease cases. Deep Minds with association with VA (The Department of Veterans Affairs ) developed an ML tool that can forecast acute kidney injuries in patients (around 48 hours in advance).

It was majorly seen that AKI (Acute Kidney Injury) is very hard to detect but with the support of technology, early detection of severe kidney problems has saved patients from progression towards serious kidney consequences.

At the initial stages of testing, AI-enabled systems were trained to predict results over 620,000 data points where it was able to identify 3600 of the AKI predictors. As it required more training over data and extracting more valuable signals from diverse data sets it was to undergo many testing criteria to enhance the percentage of delivery of accurate results.

For inventing a model that is flawless and detects acute kidney disease, taking into account the size of datasets, Deep Learning concepts were embedded to predict the risks of future patient deterioration to a great extent.

Quick and full proof effective treatment of AKI hasn’t prevailed but the developed approach offers extensible opportunities to detect patients at risk within a stipulated time interval, allowing early aid for them.

Based on electronic medical records, 703,782 adult patients were taken, with 172 inpatients and 1062 outpatients. On processing the large data sets, the smart machine was known to predict 90.2% of the acute kidney damage patients who required subsequent dialysis( in a lead interval of 48 hours) and 55.8% were stationary cases.

Along with the successful results, it predicted future trajectories for blood tests in relevancy.

[Prefer Reading: “How Virtual Reality is impacting the Healthcare Industry?”]

Last Say

Technology is advancing with leaps and bounds, in the coming years, you will likely have your first interaction with a medical artificial intelligence (AI) system. Artificial Intelligence is a technique created by humans, unknowingly that they may surpass humans and their intelligence in the medical sphere.

There is a lot to experience beyond these present examples set before us by AI and its subsets, making remarkable contributions to the world in the spectrum of healthcare.