Using AI to Detect Lung Cancer from CT Scans

Medical scientists have beamed that to detect, diagnose, and treat a patient with cancer successfully, access to the right tools is essential. For instance, in England, lung cancer which happens to be one of the significance causes of death with an annual 1.6 million deaths attributed to it, is most times detected at a late stage living majority of these patients untreatable.
To diagnose lung cancer, physicians depend on the segmentation of lesions on the lungs by making use of a combination of PET and CT scans.  These are primarily used to determine the functional attributes of a lesion in addition to its anatomical characteristics and structure.
Poland-based Future Processing which happens to be one of the numerous members of NVIDIA’s inception program is working really hard to ensure it simplifies the use of these tools, thereby making the diagnosis process more economical, accurate, reliable, and accessible.
Furthermore, its medical imaging solutions business sector has collaborated with the medical imaging experts, research institutes and clinics all over the world to design and develop software that will enhance and adequately analyze images.
Dynamic contrast enhanced imaging, and the analysis of computed technology (CT), happens to be their significant areas of concentrations. The company’s research in this area is likely to lead to the increased utilization of CT scans in the discovery and examining of lung cancer.

A Picture Expresses A Thousand Words

Future Processing is working so hard to determine the best solution to the get rid of the need for the unification of PET and CT scans. This will enable doctors to make diagnosis and analysis based on CT scans.
By making use of the convolutional neural networks, they have been able to demonstrate that diagnoses from CT scans alone can be made efficient and accurate.
According to Dr. Jakub Nalepa, a senior research scientist at Future Processing, “Before the segmentation of active lesions there is the need for the co-registration of PET and CT sequences within a time-absorbing procedure.” He further stated that they are have been able to present a research analysis using CT scans, where they were able to demonstrate segmentation of a single image within a couple of minutes.
NVIDIA Tesla GPU accelerators are responsible for powering the acceleration in speed segments and is capable of making a tremendous difference for both doctors and patients. Furthermore, radiologists can save a tremendous amount of time and measure lesion progress by automatically segmenting the lesions.
It would be a huge relief for medical sites as they will be able to take care of their patients directly using only CT scanners and without the interception of PET scanners. This is pretty more economical for medical personals, as a CT scan costs between the sums of $1,200 to $3,200, while a PET scan which costs $3,000 to $6,000. Asides that, it also offers a better experience for patients, who will only undergo one scan.

Nalepa and his team of researchers have proven their approach to be responsible for the reduction of false positives, from 90.14 percent to 6.6 percent. They hope to develop a lasting solution to the lung cancer in the nearest future.

New Algorithms are Able to Diagnose Disease as Accurately as Expert Physicians

Sebastian Thrun and colleagues at Stanford University earlier this year demonstrated the capability of ‘deep learning” algorithm; this deep learning algorithm was capable of diagnosing potentially cancerous skin lesions as efficient as a board-certified dermatologist.
Professionals have beamed medical images, such like x-rays, MRIs, and photography; to being an almost a perfect match for strengthening deep-learning software, which in the pasts has led to the breakthrough in recognizing faces and objects.
A good number of companies are already in pursuit. Alphabet life science arm, merged with Nikon last December to design an algorithm for detecting the causes of blindness in diabetics. However, the radiology field has been titled the “Silicon Valley of medicine” owing to the numerous numbers of detailed images generated by it.

Black-box medicine
Though the prediction made by the Thrun team were highly accurate, it wasn’t sure exactly which of the features of a mole, the deep-learning program uses to classify it as cancerous or benign. The outcome is the medical version of what is termed deep learning “black box” issues.
But unlike the traditional vision software, where rules are defined by a programmer, for instance a stop sign, it has eight sides and in deep learning, the algorithm searches the rules itself, but often do not leave an audit trail to explain its decision.
A scholar from the University of Michigan, Nicholson Price, who focused on health law, says “In the case of black-box medicine, doctors do not know what is happening because nobody does its heritable opaque.”
Yet he says that, that may not stand as a severe hindrance in health care; it relates deep learning to drugs whose benefit comes from unknown means. Lithium is an example, the biochemical and mechanical effect of mood is yet to be elucidated, but the drugs are still approved for the treatment of bipolar disorder. The mechanisms behind the most widely used medicine aspirin weren’t understood until after 70-years.
Price has stated several times that the black-box issues won't be a problem with the U.S. Food and Drug Administration; and in the aspect of approving new drugs and as well regulating software if its purpose is to prevent or treat disease.
The FDA says in a statement that for over twenty years it has approved numbers of image analysis application which relies on a variety of patterns recognition, in machine learning and as well computer vision techniques. More software powered by deep learning has been confirmed by the agency and noted that companies are allowed to keep details of their algorithm confidential.
The FDA has already approved and given the freedom to at least one deep-learning algorithm. In January the FDA open for the sale of software developed by a privately held medical imaging company resided in San Francisco. The algorithm, “Deep Ventricle” analyzes the MRI images if the interior contour of the hearts chambers and as well calculated the volume of blood a patients heart could contain or pump. The calculation was completed in less than thirty seconds, and Arteries says whereas conventional methods typically would take an hour.
FDA expected Arteries to do further testing to make sure his algorithm result was on par with those generated by physicians. John Axerio-Cities said he needed to prove the statistics that Arteries algorithm followed whatever its intended use is, or what the marketing claims to say its doing.

Big demand
The team led by Thrun a former vice president of Google who also works on driverless cars, n training their software fed it 129,405 images of skin conditions evaluated by experts. These covered the number of 2,032 various diseases and included 1,942 images of confirmed skin cancers.
The software eventually was capable of outperforming 21 dermatologists in identifying mole which was potentially cancerous.
Robert Novoa, a Stanford dermatologist and an author of the study, has said that when most dermatologist sees the potential of this technology, they will embrace it. Robert Novoa and some other team members refuse to say if its in their plan to commercialize the software
Allan Halpern a Memorial Sloan Kettering dermatologist and president of the international society for digital imaging of skin, he has debunked the worries that doctors would soon be out of a job, “I think the threat is the opposite,” he said. The algorithm could dramatically increase the demand for dermatologist services in the society.
Halpern as well let us know that it’s because a positive screening test still needs a biopsy, and deep-learning software could find a role in primary care offices, but if it be that it was made available as a population-wide screening test or through online app, their won't be enough dermatologists to attend or follow up on the leads.
Axerio-Cities says companies will be tempted to offer deep-learning tools directly to their customers, for example people may scan their moles to see if they actually need to visit a doctor, like some non-Al cell phone apps, like MOLE MAPPER, can be used to track suspicious moles in the body, an as well record any changes over time.
However, Halpern says that customers may likely be not ready to deal with a diagnostic system that might notify them of a mole, or telling them a mole has 5 percent chance, or 50 percent chance of being cancer.

Google's Deep Learning Algorithm Is Already Better at Diagnosing Diabetic Retinopathy than Many Physicians

One of the major breakthroughs in Health Care and Artificial Intelligence world happened recently when Google successfully trained a Neural Network with 120,000 retinal images to detect diabetic retinopathy.

Generally speaking, Diabetic Retinopathy (DR) is one of the most common causes of increasing cases of blindness in the world. Studies reveal that high blood sugar level can damage the retinal blood vessels that can further cause irreversible blindness. Hence, early detection of this disease was need of the hour. But the sad fact is that physicians were not able to recognize the symptoms at an early stage. Hence, Google decided to use Machine Learning algorithms to solve this trouble. The major target was to identify disease symptoms before they cross the critical boundaries so that an early diagnosis can be carried out.

Google trained a convolution neural network for image classification and to achieve the top accuracy they used a huge set of sample data. The training dataset contained 128175 retinal images collected from EyePACS in the US and three different eye hospitals of India. These images were graded by 3-7 licensed ophthalmologists. After training the Deep Learning Neural Network, two validation tests were performed. The first test was performed with 9963 images collected from EyePACS and the second dataset of 1748 images was taken from Messidor-2. The performance of this machine learning algorithm was later compared with the ophthalmologists and the results were observed to be quite efficient.

The scientists at Google targeted two parameters for this application, they were sensitivity and specificity. The ultimate results after training provide around 96-97% sensitivity and 93% specificity.These results show that artificial intelligence suggests a way to utilize medical solutions in the much creative way. Deep learning is a bright concept for the world of technologies as it can ensure early detection of diseases so that vision of the person can be saved on time. This revolutionary research shows that AI can perform much better as compared to most eye pathologists. Moreover, most corners of the world don’t even have experienced medical health professionals to treat patients, in such situation; the automatic detection is a great solution for people suffering from various diseases. The great news is that ability of Deep Learning is not just limited to detect DR rather it can show impactful results for many other medical health issues as well.

Clinical Trial Involving 10,000 Chest X-rays that were Successfully Analyzed by AI

The MOU formulates the fundamental cohabitation for the first enormous commercial deployment of Artificial Intelligence learning technology in China.  This was carried out after a successful clinical trial involving over 10,000 chest x-rays that were evaluated by the AI.

Over the last couple of years, the private health check industry which makes use of AI, has emanated in China as a vital healthcare management system, granting access to routine check-ups as well as diagnostic services along with medical data motility in the absence of a general practitioner network. Recent revolution has resulted in the current expansion of private healthcare services in China, with roughly about 300 million people using these private healthcare services. With the growing market demand, analyzing chest x-rays using Artificial Intelligence has grown tremendously with approximately 300 million people making use of this health care technology. It is estimated that the market is expected to grow to an estimated RMB80 billion per year (USD 11 billion). A good number of patients who receive their annual chest x-rays as well as basic check-up services, are at liberty to choose from MRIs, CT studies, genetic testing, and AI.

According to Sally Daub, C.E.O, Enlitic,” Having an in-depth knowledge of the various diagnostic techniques used in analyzing and supporting diagnostic solutions across an emerging network will definitely drive a huge amount of efficiencies in the healthcare delivery. It will also provide ideas on how to ameliorate early detection, disease monitoring and treatment planning. We are so excited at the numerous benefits associated with the use of this large-scale deployment to this millennium age”. 
The use of Artificial Intelligence will enable Paiyipai to leverage insights from millions of clinical problems to issue out world-leading radiological diagnostic services over a wide range of patient network.
 

“The scale of the Chinese Health Check market lends itself to the deployment of Artificial Intelligence technology, owing to enormous patient volumes and the deficiency of practicing radiologists. Enlitic technology will hence take measures to provide users with enormous competitive advantage whilst changing the economics of the market,” beamed Andrew Harrison, Managing Director of Capitol Health Limited.

In April 2017, Enlitic technology collaborated with Chinese medical data analysis and storage firm Paiyipai to implement its deep-learning technology for diagnostic imaging in China.
Enlitic aims at using this software at private “health centers” according to the memorandum of understanding. The deal was therefore sealed, after a clinical trial involving about ten thousand chest x-rays. These x-rays, where also analyzed by Enlitic’s patient triage platform.

Many thanks to the growth of private healthcare services in China which has led to a significant growth in the use of health-check services.  Over 300 million people currently make use of health-check services which is approximately worth over $11.6 billion annually. A good number of these patients receive annual chest x-rays in addition to fundamental checkup services.

About Enlitic
Enlitic, Inc. ("Enlitic") is a medical company aimed at remodeling diagnostic healthcare services. It numerous outstanding algorithms were formulated from the scratch by distinguished data scientists, machine learning practitioners, and medical experts. These scientists formulated this algorithm by combining medical images, texts, and other data. It aims at accelerating pharmaceutical research; clinical efficiencies ameliorate diagnosis accuracy, speed and patient outcomes as well as increase drug effectiveness. Enlitic is situated in San Francisco, California, United States.

About Paiyipai
Paiyipai formally known as Beijing Hao Yun Dao Information & Technology Co. Ltd, is a technology service company and one of the leading companies in China responsible for the analysis of individuals laboratory medical test results as well as the storage and distribution of user medical records.
Dr. Han Xiaohong, founder and CEO of health check industry, is one of the distinguished shareholders of Paiyipai. Dr Han also founded Beijing Ciming Health Checkup Management Group in 2002 and merged it with health check in a bid to compact the various health ailments of the Chinese. In 2015, they merged group was stated as the largest health-check operator in mainland China, taking care of the various health needs of over 15 million patients yearly.
With the tremendous growth in health technology, Paiyipai was in 2016, awarded the "Zhongguancun High-Tech and National High-Tech Enterprise" award.  Zhongguancun is the first high-tech park situated in China. During the past two decades, it has attracted over 20,000 tech enterprises. These tech enterprises include Lenovo and Baidu.

About Capitol
Capitol Health is one of the leading distinguished providers of diagnostic imaging. Its head office is in Melbourne, Victoria, Australia. The company owns and runs health care clinics in Victoria and New South Wales. It focuses on delivering a community-based efficient and effective infrastructure for radiologists and medical personnel to offer outstanding, efficient and accurate healthcare services to patients. It also has an investment in diagnostic imaging AI in collaboration with Enlitic, Inc United States and the Enlitic’s Deep Learning Services in radiology China. The company partnered with CITIC Pharmaceutical and Xiamen Zhouxin Medical Image Co. Ltd to provide consulting services to a network of privately owned hospitals.