Tag: Cancer Diagnosis

Predictive Analytics in Cancer Diagnosis
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Predictive Analytics in Cancer Diagnosis

Introduction

GLOBOCON 2020, one of the key cancer surveillance projects of the International Agency for Research on Cancer (IARC), published recent statistics of global cancer epidemiology. According to this report, 19,292,789 new cancer cases were reported in 2020 i.e., a two-fold increase in the number of cases as reported in 2018. For over 19 million cases of cancer reported, 9,958,133 cancer-related deaths were reported in the same year.  As per the estimates of the International Agency for Research on Cancer (IARC),  every 1 person in 5 persons is likely to develop cancer during their lifetime. In this article we are going to discuss how Predictive Analytics can play a major role in changing Cancer Statistics.

Cancer Statistics: 2020

 

Males    Females

Population

3,929,973,836

3,864,824,712

Number of new cancer cases

10,065,305

9,227,484

Number of cancer deaths

5,528,8104,429,323
5 year prevalent cases24,828,480

4,429,323

Top 5 most cancers excluding non-melanoma skin cancer Lung, Prostrate, Colorectum, Stomach, Liver

Breast, Lung, Colorectum, Prostrate, Stomach

Data taken from GLOBOCON 2020

Estimated Number of Cases Worldwide

These alarming and constantly rising figures have refocused the attention of medical scientists on the early screening and diagnosis of cancers using Predictive Analytics. Because cancer mortality and morbidity can be reduced by early detection and treatment of cancer.

American Cancer Society (ACS) issues updated guidelines and guidances related to an early screening of cancers to assist in making well informed decisions about the tests for early detection of some of the most prevalent cancers (breast cancer, colon and rectal cancer, cervical cancer, endometrial cancer, lung cancer, and prostate cancer). The early detection of precancerous lesions and cancers is broadly divided into three categories:

Early cancer diagnosis

Cancers respond very well to the treatment only if diagnosed early that, in turn, increases the chances of cancer survival. As per WHO guidance, early diagnosis is a three-step process that must be integrated and provided in a timely manner.

  1. Awareness of cancers and accessing care as early as possible
  2. Clinical evaluation of cancers, appropriate diagnosis and staging of cancers
  3. Access to the right treatment at the right stage.

Screening of cancers

Screening identifies specific markers of cancers that are suggestive of particular cancer. For example, visual inspection with Acetic Acid (VIA) test can be used for early screening of cervical cancers in women. Cervical lesions turn white for a few minutes after application of acetic acid.

However, the early diagnosis and screenings of cancer suffer from drawbacks like false positives, false negatives, and overdiagnosis which may lead to more invasive tests and procedures. To overcome this problem, scientists are using the power of Predictive Analytics based on Artificial Intelligence and Machine Learning.

Introduction to Artificial Intelligence (AI)

Artificial Intelligence (AI) is a great tool for Predictive Analytics and it is defined, in Webster’s dictionary, as a branch of computer science dealing with the simulation of intelligent behaviour in computers. In other words, it is the capability of machine to imitate intelligent human behaviour.

One of the early pioneers of Artificial Intelligence, Alan Turings, published an article in 1950 entitled “Computing Machinery and Intelligence.” It introduced the so-called, Turing test, to determine if a computer can exhibit the same level of intelligence as demonstrated by humans. The term “Artificial Intelligence” was coined by John McCarthy at the Artificial Intelligence (AI) conference at Dartmouth College in 1956. It was Allen Newell, J.C. Shaw, and Herbert Simon who introduced the first AI-based software program namely, The logic Theorist.

Majority of Artificial Intelligence (AI) applications use Machine Learning (ML) algorithms to find patterns in the datasets. These patterns are used to predict the future outcomes.

The basic framework of Artificial Intelligence (AI) consists of three main steps:

  1. Collecting input data
  2. Deciphering the relationship between input data
  3. Identifying unique features of sample data

Introduction to Machine Learning (ML)

Machine learning (ML) is also another tool for Predictive Analytics and is defined in Webster’s dictionary as the process by which a computer is able to improve its own performance by continuously incorporating new data into an existing statistical model. It allows the system to reprogram itself as more data is added and eventually increasing the accuracy of the task assigned.

In the case of Machine learning (ML), it’s an iterative process so that the predictability of the system is improved each time. Most Machine Learning (ML) algorithms are mathematical equations in which sample data is plotted to observed variables, termed as features, and the outcomes termed as labels. The labels and features are used for the classification of different ML tools and techniques. Based on the label type, Machine Learning (ML) algorithms can be categorised into:

Supervised Learning

Unsupervised Learning

Reinforcement Learning

In supervised learning, models are trained based on labelled datasets. For the purpose of prediction, the model needs to map the input variables with the output variables using a know mathematical function. Supervised learning can be used for understanding, Classification and Regression problems.

In unsupervised learning, data patterns are found in the un-labelled data and the endpoint of the unsupervised learning is to find characteristics patterns in the data. Unsupervised learning is used for identifying Clustering and Association in datasets.

Reinforcement learning is the ‘learning’ by interacting with the environment. A reinforcement learning algorithm makes decisions based on its past experiences and also by making new explorations.

The PCA part of MagicPCA 1.0.0. is an unsupervised Machine Learning Approach, whereas the SIMCA part of it is a Supervised Classification Technique.

Rising Interest in Biomedical Research

In the initial years, the journey of AI was not so easy, as can be seen in the period of 1974-1980, which is known as AI winter. During this period the field experienced its low in terms of researcher’s interests and government funding. Today, after decades of advances in data management and superfast computers, and renewed interests of government and corporate bodies, it is a practical reality and finds its applications in a wide variety of fields like e-commerce, medical sciences, cybersecurity, agriculture, space science, automobile industry, etc.

As the phrase “Data Science is everywhere” picked up, biomedical researchers started delving into Artificial Intelligence (AI) and Machine Learning (ML) to look for a better solution through Predictive Analytics. One inspiring story of Regina Barzilay, a renowned professor of Artificial Intelligence (AI) and a breast cancer survivor, portrays how her diagnosis of breast cancer reshaped her research interests. She hypothesized that AI and ML tools can extract more clinical information that helps clinicians make knowledgeable decisions. She collected data from medical reports and developed Machine Learning algorithms to interpret the radio diagnostic images for clinicians. One of the models developed by her has also been implemented in clinical practice that helps radiologists to read diagnostic images very well.

Current scenario: Predictive Analytics in Cancer Diagnosis

The concept of AI/ML has long been employed as Predictive Analytics tool in the radiodiagnosis of precancerous lesions and tumours.

The AI system reads the images generated by various radiological techniques like MRI, PET scan, etc., and processes the information contained in them to assist clinicians make conscious decisions on the diagnosis and progression of the cancers.

Breast Cancer Diagnosis with QuantX

The FDA’s Center for Devices and Radiological Health (CDRH) has approved the first AI-based breast cancer diagnosis system for Predictive Analytics. QuantX was developed by Qlarity Imaging (Paragon Biosciences LLC). QuantX is described as a computer-aided (CAD) diagnosis software system that assists radiologists in the assessment and characterization of breast anomalies using Magnetic Resonance Imaging (MRI) data. The software automatically registers images and segmentations (T1, T2, FLAIR, etc.), and analyses user-directed regions of interest (ROI). QuantX extracts this data from the ROI to provide computer-aided analytics based on morphological and contrast enhancement characteristics. These imaging analytics are then used by an artificial intelligence algorithm to get a single value, known as QI score, which is analysed relative to the reference database. The QI score is based on the machine learning algorithm that is generated from a training subset of features calculated on segmented lesions.

Cervical Cancer Diagnosis with CAD

National Cancer Institute (NCI) has also developed a computer aided program (CAD) that analyses digital images taken of women’s cervix and identify potentially precancerous changes that require immediate medical attention. This Artificial Intelligence-based approach is called Automated Visual Evaluation (AVE). A large set of data, around 60000 cervical images, was generated using the precancerous and cancerous lesions to develop a machine learning algorithm. This algorithm recognizes patterns in visual images that lead to precancerous lesions in cervical cancers. The algorithm-based visualization of images has been reported to provide better insight into precancerous lesions, with a reported accuracy of 0.9, than routine screening tests.

Lung Cancer Diagnosis with Deep Learning Technique

NCI funded researchers of New York University used Deep Learning (DL) algorithms to identify gene mutations from pathophysiological images of lung tumors using Predictive Analytics. The pathophysiological images of lung tumours were collected from the Cancer Genome Atlas and used to build an algorithm that can predict specific gene mutations by visual inspections of the pathophysiological images. This method can very accurately predict the different types of lung cancers and the corresponding gene mutations from the analyses of the images.

Thyroid Cancer with Deep Convoluted Neural Network

Deep Convoluted Neural Network (DCNN) models were used to develop an accurate diagnostic tool for thyroid cancers by analysing images from ultrasonography. 1,31,731 ultrasound images from 17,627 patients with thyroid cancer and 1,80,668 images from 25,325 controls were collected from the thyroid imaging database of Tianjin Cancer Hospital. Those ultrasound images were modelled into a DCNN algorithm. The DCNN model showed similar sensitivity and improved specificity in identifying patients with thyroid cancer compared with a group of skilled radiologists.

AI/ML for Personalized Medicines

Researchers at Aalto University, University of Helsinki and the University of Turku developed a machine learning algorithm that can accurately predict how combinations of different antineoplastic drugs can kill various types of cancerous cells. This algorithm was obtained from data collected from a study that investigated the association between different drugs and their effectiveness in treating cancers. The model developed was found to show associations between different combination of drugs and cancer cells with high accuracy; the correlation coefficient of the model fitted was reported to be 0.9. This AI model can help cancer researchers to prioritize which combination of drugs to choose from a plethora of options for further research investigation. This depicts how AI and ML can be used for the development of personalized medicines.

Future challenges of AI/ML in cancer diagnosis

Data Science is shaping the future of the health care industry like never before. There has been a spurt of growing interests in AI and ML for the diagnosis of precancerous lesions and surveillance of cancerous lesions. The researchers are exploring to develop AI algorithms that help in the diagnosis of many other cancers. However, each type of cancer behaves differently and the consequent changes would be a significant challenge for the algorithms. Machine learning tools can overcome these challenges by training algorithm of these subtle changes. This would drastically improve decision making for clinicians.

One of the biggest challenges of the Artificial Intelligence today is the acceptance of the technology in the real world, particularly related to medical diagnoses of terminally ill patients where decision making plays a critical role in the longevity of the patient. The AI black box problem augments this problem further. AI black box refers to the fact that programmers can see input and output data only but how does an algorithm work is not known.

Regulatory aspects of AI/ML in cancer diagnosis

In 2019, US FDA publishing a discussion paper entitled “Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) – Discussion Paper and Request for Feedback.” The intention of FDA was to develop a regulatory framework for the medical software by issuing draft guidance on the Predetermined Change Control Plan outlined in the discussion paper. The Predetermined Change Control Plan mapped out a regulatory premarket review for AI/ML-based SaMD modifications.

In 2021, FDA published a draft guidance document entitled “Artificial Intelligence and Machine Learning (AI/ML) Software as a Medical Device Action Plan.” The FDA encouraged the development of the harmonized Good Machine Learning Practices of AI/ML-based SaMD through the participation of industrial and other stakeholders in consensus standards development efforts. This guidance was built upon the October 2020 Patient Engagement Advisory Committee (PEAC) meeting focused on patient trust in AI/ML technologies.

The FDA supports regulatory science efforts on the development of methodology for the evaluation and improvement of machine learning algorithms, including for the identification and elimination of bias, and on the robustness and resilience of these algorithms to withstand changing clinical inputs and conditions.

Conclusion

The employment of Predictive Analytics in cancer diagnosis has answered major challenges experienced in cancer diagnosis and treatment. It can help early screening of precancerous lesions and avert the mortality rate in cancer patients. AI/ ML provides accurate detection and prognosis of cancers, thereby reducing the incidents of false positives, false negatives and overdiagnosis. These techniques can also be used to track the prognosis of the cancers in the case of immunotherapies and radiotherapies. The AI/ ML has also potential applications in the development of personalized medicines by developing specific therapies for each specific cancers.

By detecting cancers early and accurately, prognosis of cancer treatment would be greatly improved. The early detections of cancers will have a huge impact on the cost-saving of the complicated cancer treatments. This could also have a huge impact on the cancer survival rates as the mortality rates could be drastically decreased on early detection of the cancers.

If you are struggling to make use of cancer data and need help to develop Machine Learning Models, then feel free to reach out to us. At Let’s Excel Analytics Solutions, we help our clients by developing cloud-based software solutions for predictive analytics using Machine Learning.

Predictive Data Science in Food and Beverages
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Predictive Data Science in Food and Beverages

Introduction: Predictive Data Science

Predictive data science is no longer limited only to data scientists and engineers. Interested to explore what you can do in the food & beverage industry? Read this article to know how your competitors are leveraging predictive data science to improve their operations. Progress happens with each step taken. With the market becoming more competitive than ever, everyone is eager to find a breakthrough solution. According to a news report on CISION PR Newswire, the global food and beverages market reached a value of nearly $5,943.6 billion in 2019, having increased at a compound annual growth rate (CAGR) of 5.7% since 2015. The market is expected to grow at a CAGR of 6.1% from 2019 and reach $7,525.7 billion in 2023. Meaning that there are massive amounts of data just waiting to be analysed and processed for meaningful insights using predictive data science. Let us now see what data science and analytics can do for the food and beverage industry.

Predictive Data Science in Restaurant Industry:


When talking about the benefits of predictive data science in food, we cannot leave out restaurants. Restaurant owners do not seem to realize the tremendous amounts of data that is generated from their customers. Therefore, there are chances that they miss opportunities to decrease costs and improve customer experience. With the explicit and precise implementation of data science, restaurant owners can obtain real-time analysis of their customers’ data and make the required improvements. For instance, owners and founders can point out their highest selling or most expensive items, the quality of food offered, and more. Based on this data, they can make informed choices, and also fix their mistakes.

Predicting shelf life:


Each type of food has its own shelf-life, causing it to expire over time. However, there are certain items of consumption that only grow better with time. For instance, wine gets better with time, but fresh produce will expire. Different items of food and drink have different shelf lives and managing all of them independently is a major challenge for this industry. The procedure for dealing with wine is very different compared to the procedure for dealing with expired products. But, by incorporating predictive data science into the picture, data engineers can predict the shelf life of produce, thus ensuring pre-emptive action is taken to reduce the amount of waste and saving money and time.


Sentiment Analysis:


Social media, review websites and food delivery apps have allowed the food industry to do something that was not possible in the past, i.e., sentiment analysis. Using NLP, organizations can analyse their social media channels and discover patterns and trends in the data. This will allow them to discover the most popular foods and beverages of any season. It also allows them to discover the popular foods during special occasions and other festivities. Brands, restaurants, and organizations can, in turn, be more receptive to people’s demand and act accordingly. Google analytics can be a helpful medium in this case.


Better Supply Chain Transparency:


Let us now look at another example of how data analytics can benefit growers, transporters, processors, and food retailers:

  • Information about the weather is also entered into the database. Inputs about precipitation and temperatures can be automated.
  • The farmers enter the test results of the soil, along with the planting and harvesting data into a database used by a particular software program.
  • The logistics company that transports the farmer’s crop from the farm to the processing mill, inputs the start and end times for the trip in the database.
  • The food processor enters the start and stop times for various stages of the processing, sorting, washing, packaging, and placing in cold storage can all be tracked with automated sensors.
  • The product is then monitored from the processor to the retailer. Any delays that could cause the food to spoil can be easily identified.
  • At the destination, the vendor can record the quality of the food when it arrives,
  • Customer feedback on social media can also be added to the collected data, to provide further insight to the food supply chain.

The entire supply chain can access this information. If there arises any problems, changes can be made to this process to prevent a further recurrence. Moreover, retailers can choose to accept or reject the shipment based on this data. The software performs an analysis of the data and provides intelligent and accurate conclusions to all parties involved in the supply chain. Analytics software takes the help of a large number of sources for making its analysis, including social media. Both structured and unstructured data is used for the analysis. This collection of data is known as big data.


Measuring Critical Quality Attributes:


There are certain primary attributes against which the food and beverage industry measures the quality of its products. These attributes can be a great asset in marketing them – for example, the alcohol concentration in beer. However, conventional methods of measuring primary attributes are time-consuming. In case of beer, the alcohol level is measured using a method known as near-infrared spectroscopy. This method, however, is time-consuming and delays the production process. Predictive Data science and analytics allows organizations to explore other methods that are faster and more cost-effective, like the Orthogonal Partial Least Square Regression and multiple regression models to measure alcohol content and colour.


Better Health Management:


Consumers wish the food industry to be more transparent. The leading firms of the multi-billion-dollar beef industry realised this when they gathered for Beef Australia 2018, a convention that sees over 90,000 visitors. Consumers expect restaurants and organisations to be more forthright with them. They expect to be completely aware of how the food was produced, how the livestock was treated and what chemicals, if any, were used in the food. They want to be completely aware of what they are consuming. Data science and analytics helps incorporate transparency within these supply chains, so that organizations can be more honest with their customers. Transparency also assists in solving problems related to logistics and supply. For instance, it becomes easier to track contaminated food supplies to their storage locations, thus eliminating the chances of spread of food-borne diseases.
Predictive data science and analytics allows organizations to protect food health and prevent cross-contamination. Geographical data, along with satellite data and remote sensing techniques, allows data analysts to ascertain changes. This information, along with data on temperature, soil property, and vicinity to urban areas, can predict which part of the farm is likely to be infected with pathogens, and take immediate action beforehand. Another excellent example is when cities are short on food inspectors – data analytics can analyse historical data on 13 key variables to help pinpoint the riskiest establishments, making better use of limited food inspectors.

Predictive Data Science for Food Innovations


Organizations need to keep pace with the changing demands of the consumers. With the fluctuation in their tastes according to season, time of day, weather, mood, etc, it becomes crucial for the organizations to take the assistance of predictive data science. This data is then converted into meaningful information which aids in making important decisions, as well as to improve sales and overall performances.

Food Marketing


Predictive data science also assists businesses in improving their marketing campaigns, developing creative and high demand products, and empowering firms to stay updated over their competition’s growth rate, control quality as well as assess decisions regarding purchasing and prices. The data also helps businesses keep track of certain crucial factors, like the quality of their products, by gauging if the composition of the product has been altered in any way.

Conclusion


Predictive data science and analytics has definitely brought about a positive growth in certain industries, including the food and beverage industry. This industry is prone to its fair share of difficulties. With the ever-growing population, consumers are always looking to choose the best option that they can get. Since the consumer is the key, organizations need to make decisions revolving around the consumers’ tastes. Data science enables businesses to derive conclusions about which option will be best suited for the consumers. It allows organizations to collect and analyse data and derive at interesting patterns and trends over a period of time. The technology can also be used to conceive several creative solutions to problems plaguing the industry while bringing positive developments to food and beverage.

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