Question

What is artificial intelligence? Describe two types of AI and explain the potential relevance of each to health care. Identify the potential relevance of at least one type of AI to global health

Answer 1

Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. AI is an interdisciplinary science with multiple approaches, but advancements in machine learning and deep learning are creating a paradigm shift in virtually every sector of the tech industry.

Artificial intelligence in breast cancer

Breast Cancer is a major cause of concern worldwide. It is the most frequently diagnosed cancer in women and can also occur in men. Breast cancer arises when cells grow and multiply uncontrollably, which produces a tumor or a neoplasm. In women with breast cancer who are younger than 50 yrs of age, chemotherapy increases their 15-yr survival rate by 10%; in older women, the increase is 3%.

Early detection of breast cancer is of utmost importance. There is a growing number of breast cancer patients throughout the world and there is a need for new techniques in diagnosis-related with such type of patients and prediction of cancer in its different forms. This can lead to a decrease in the rate of mortality.

At an early stage, cancer can be detected with the help of screening tests. The most effective tool for detecting breast cancer in its earliest and most treatable stage is Mammography. Furthermore, this test allows the detection of other pathologies and may suggest the nature such as normal, benign, or malignant. The American Cancer Society recommends cancer screening guidelines for most adults. The introduction of digital mammography is considered the most important improvement in breast imaging.

A growing area of research relates to the use of techniques from Artificial Intelligence applied to the processing of information necessary for the medical diagnosis.

Artificial intelligence (AI) is the subfield of computer science which is exhibited by machines or software and becoming popular as it has enhanced human life in many areas. AI developed systems that reliably interpret mammogram data, intuitively translate patient charts into diagnostic information which accurately predicts breast cancer risk.

Techniques that depend on the principle of intelligent systems such as neural networks, nearest neighbor methods, computer-aided design (CAD) algorithms, fuzzy logic approach, decision trees, and linear programming methods. Currently, the Curemetrix algorithm, iT Bra, NLP (Natural language processing) software, Genes to systems breast cancer database (G2SBC), and Triple-negative breast cancer database intelligent systems are used for breast cancer detection.

Computer-aided diagnosis (CAD) systems use computer technologies to detect irregularity in mammograms and these results are used by radiologists for diagnosis which plays an important role. The CAD performance can change from one condition because some lesions are more difficult to detect than others, this is because they have similar characteristics to normal mammary tissue.

Higher breast density usually indicates a higher possibility for the presence of malignant tissue. A human observer can distinguish different structures very well without the information of their overall brightness. In automatic breast density classification, it is important to decide which parameters give the best division between categories.

A smart wearable device iTBra has been developed by Cycardia Health for monthly breast scanning. This can be an important artificial intelligent technique for early breast cancer detection.

Cyrcadia Health is a US-based medical biosensor company that utilizes predictive analytics to accomplish early breast cancer detection through personalized wearable devices. The Cyrcadia breast patches are fitted with sensors that track temperature changes in variance over time believed to be a circadian cellular change caused by a reduced PER1 (period estrogen receptor) and PER2 protein expression in the presence of breast cancer. When tissue is healthy, the Cyrcadia predictive algorithmic output displays an irregular variant metabolic pattern. In the case of cancer, the varied pattern of metabolic activity becomes compressed or demonstrates more of a flat line profile in tissue infused cancers.

Automated Breast Density Software ( i Reveal ) accurately estimates percent breast density (PBD), area of dense tissue, total breast area, and irrespective of the (FDA-approved) imaging sensor.

iReveal identifies the risk of potentially masking cancer, then maps the percentage of the fibro glandular tissue to a density category corresponding to the BI-RADS (Breast Imaging Reporting and Data System) standards.

Curemetrix algorithm that detects what direction and projection of breast are in the cause was developed to create a unique “breast health score” for each image that may lead to improved medical image analysis and anomaly quantification. It also creates a unique “breast health score” for each image that may lead to improved medical image analysis and anomaly quantification. G2SBC a resource that integrates data about genes transcripts and protein altered in breast cancer cells. This database represents a systemic biology oriented data integration approach devoted to breast cancer.

Artificial neural network (ANN) is one of the best artificial intelligence techniques for common data mining tasks nonlinear statistical data modeling tools.

ANN is used for classification between cancerous and noncancerous images. Their ability to learn from historical examples, analyze non-linear data, handle imprecise information, and generalize enabling application of the model to independent data has made them a very attractive analytical tool in healthcare. Fuzzy logic is used to predict survival in patients with breast cancer. An intelligent method to assist in the diagnosis and second opinion of breast cancer, used for processing and sorting data obtained from smears of breast mass obtained by fine needle aspirate.

Natural language processing (NLP) software algorithms for mammographic imaging characteristics and mammogram reports provide an automated means to aid in data extraction and analysis for clinical decision support systems.

It is very important to continue the development of these methods which gives the right direction for research in near diagnosis of breast cancer and provides the medical experts with a second opinion thus remove the need for biopsy, excision and reduce the unnecessary expenditure.

Management of Alzheimer’s disease with artificial intelligence

Alzheimer’s disease (AD) is the most common neurodegenerative disorder to date, with no cure or preventive therapy. Histopathological hallmarks of AD include the deposition of β-amyloid plaques and the formation of neurofibrillary tangles in the brain. AD is the leading cause of dementia, characterized by substantial memory loss, impairment of multiple cognitive functions, and behavioral changes which affects a large population worldwide.

The application of artificial intelligence (AI) to this incurable disease may help in early detection. Use of various automated systems and tools like Brain-computer interfaces (BCIs), Arterial spin labeling-magnetic resonance imaging (ASL-MRI), Electroencephalogram (EEG), Positron emission tomography (PET), Single-photon emission computed tomography (SPECT) scans and various algorithms helps to minimize errors, early detection, and control disease progression.

The use of different AI algorithms in brain MRI scans sets a path to distinguish between the early stages of Alzheimer’s disease. Brain-computer interfaces (BCIs) help AD patients to convey basic thoughts by sending commands from the brain to an external device. Arterial spin labeling (ASL) imaging is promising functional biomarker that creates perfusion maps which recognizes blood perfusion pattern in various regions of brain assisting detection of different stages of AD. MRI coupled with ASL can intercept or slow disease advancement from subjective cognitive decline to mild cognitive impairment to AD. Uses of automated machine learning methods have potential use in the management of AD.

Electroencephalograms (EEG) have been demonstrated as a reliable tool in AD research and diagnosis. It is simple, non-invasive, and potentially mobile brain imaging technology. EEG has high temporal resolution and may, therefore, contain crucial information about abnormal brain dynamics in AD patients. Three major effects of AD on EEG have been observed: slowing of the EEG, reduced complexity of the EEG signals, and perturbations in EEG synchrony. AD seems to affect different frequency bands in EEG by specific ways like decrease of power in higher frequencies (alpha and beta, 8 Hz–30 Hz) and increase of power in low frequencies (delta and band, 0.5 Hz–8 Hz) is associated with in Mild cognitive impairment (MCI) patients as compared to healthy age control groups. Besides, scientific studies show promising results in the detection of the early stages of AD such as Mild cognitive impairment (MCI).

Positron emission tomography (PET) scans is a functional neuroimaging technique that provides information about physiological and biological processes in the brain. PET scan measures the cerebral metabolic rate for glucose, which is progressive and correlated with AD. Fluorodeoxyglucose (FDG) is a widely used PET metabolic tracer in AD. FDG PET provides a promising biomarker of disease progression, in AD intake of glucose, and FDG becomes impaired in the brain. PET has been used to detect people at risk for AD even before the symptoms start which serves as an effective tool for early diagnosis. Single-photon emission computed tomography (SPECT) is a molecular imaging technique is able to get an impression of the regional cerebral blood flow. The majority of SPECT studies find that the pattern of hypoperfusion in the temporal and parietal cortex regions. These SPECT perfusion differences occur in brain regions of normal and disease condition helps early diagnosis of AD. Early detection of AD by artificial intelligence helps in early initiation of the treatment for AD, which slowdowns the disease progression, improves patient’s quality of life, and further reduces the economic burden involved in healthcare management.