Artificial Intelligence for Disease Diagnosis, Prognosis, Cancer and Prevention is going to be discussed in 14IHNPUCG. Register your slots now to know about it.
Cancer
has a low median survival rate and is an aggressive disease.
Ironically, because of the high rates of recurrence and mortality, the
treatment process is drawn out and expensive. To increase the patient's
survival rate, accurate early diagnosis and prognosis prediction of cancer are
crucial. Numerous scientists have applied computational
techniques like multivariate statistical analysis to analyse the prognosis
of the disease as a result of advancements in statistics and computer
engineering over the years. The accuracy of such analyses is significantly
higher than that of empirical predictions. Additionally, as applications for
artificial intelligence (AI), particularly machine learning and deep learning,
have become more widespread in clinical cancer
research in recent years, the accuracy of cancer prediction has increased
significantly. This article summarises the benefits of AI for cancer diagnosis
and prognosis after reviewing the relevant literature. We look at how AI can
help with cancer diagnosis and prognosis, focusing on its unmatched
accuracy—which is higher than that of conventional statistical oncology
applications. We also show how these techniques are progressing the discipline.
Finally, potential and difficulties in the application of AI in medicine are
examined. As a result, this essay offers a fresh viewpoint on how AI technology
might help advance cancer
detection and prognosis,
as well as further advance human health.
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AI used to predict cancer
Over the
past few decades, professionals, paramedics, and
carers from all areas have been asked to estimate cancer prognoses based on
their respective expertise. The advent of the digital data era has made
clinicians aware of the necessity to embrace AI technologies
like DL and ML.They contend that it is challenging to predict how cancer will
develop because of the intricate and extensive nature of statistical analysis. Health-care experts
are also concerned about the risk that a patient may contract a disease, can
have a tumor
recurrence after treatment, or die. These considerations have a substantial
influence on treatment options and results. In actuality, a substantial corpus
of research on clinical cancer focuses on determining prognosis or
predicting patient response to therapy. More effective medicines can be given
to patients with more accurate prognoses; in fact, these treatment alternatives
frequently include personalising or individualised care for every patient. In
order to anticipate cancer, AI can analyse and comprehend "multi-factor"
data from various patient assessments and provide more detailed information
about the patient's survival, prognosis, and disease progression projections.
Enshaei et al. explored a variety of tactics, combining classifiers with
conventional logistic regression analytic techniques to show how AI may be used
to provide ovarian cancer patients with forecasting and predicting information.
Artificial intelligence-based
algorithms
have been demonstrated to be able to analyse unstructured data and accurately
predict the likelihood that patients would contract various illnesses,
including cancer.47 Accurate agnostic AI algorithms can affect cancer screening
recommendation outcomes and improve risk stratification criteria.48-52 For
instance, a synthetic "neural network model" for "colorectal
cancer risk stratification" showed the highest degree of accuracy compared
to "current screening guidelines."
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Which Cancer Types Can Be
Predicted Easily?
Given
that there are numerous varieties of cancer and that cancer is a
hereditary disorder,54,55 it is not surprising that developments in AI have
benefited oncology in particular. For instance, it has been demonstrated that
"DNA methylation analysis" in cancer can help with classification and
prognosis.56 More than 70% of malignancies that have been labelled by humans
can be reclassified using the "machine-determined DNA methylation"
technique, which could have a significant impact on the prognosis and treatment
options.57 According to a study, MethylationEPIC (850 k) and Illumina HumanMethylation450
had the highest classification accuracy of 93% for 82 different types of
"brain tumours."58 The authors claimed even greater accuracy than
pathologists.
Conventional Cancer Diagnosis and
Treatment Methods
Traditionally,
a patient seeks medical
attention from a doctor when they have symptoms like hard lumps on their bodies
or strange patterns on their skin. The clinic compiles the patient's clinical
history, screening exams, and medical imaging as the initial step in the
cancer-detection process. The screening test looks for people who have a
particular cancer
or pre-cancer but have not yet displayed any symptoms in order to quickly refer
them for further testing and treatment if necessary. Several scan modalities
can be used to do a pre-stage analysis.
This is
carried out as a preventative measure to avoid cancer in a
high-risk population being discovered too late. After a questionable discovery,
tissue samples from the affected area are collected and examined in a lab. For
additional information on the findings, medical professionals are consulted.
They compile, synthesise, and analyse the pertinent data while also
recommending a diagnosis. The appropriate course of therapy is suggested and
the patient is advised of the current diagnosis and prognosis. With
possibilities for speedy diagnosis and
the capacity to learn from mistakes, this procedure is advantageous for
patients as well as the healthcare system.
Nevertheless, this procedure has room for error and is adaptable based on the medical
speciality.
Data Repositories for Cancer
The term
"digital
health" refers to the application of digital transformation to the
healthcare sector and includes software, data, technology, and services.
Radically interoperable data and AI, according to Deloitte Insights, offer
consumer- and prevention-focused healthcare. Data accessibility is crucial for
data-driven AI research, and the scarcity of sufficient data to conduct studies
often frustrates researchers.
Cancer
researchers are always creating new clinical trials
in an effort to learn how to improve cancer care, therapy, and prevention. Many
institutions provide online lists of open clinical trials to help participants
find research studies that might be appropriate for them. For finding a cancer clinical
trial, the following resources, listings, and searchable databases are
helpful.
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& trends. This conference is for you if you or your coworkers have any
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