Correctly diagnosing a disease requires years of training in the field of medicine, and even after passing the training courses, diagnosing the disease is often a difficult and time-consuming process. The demand for experts in many fields exceeds the number of available people, and the lack of experts usually delays the diagnosis of the disease and saving the patient’s life.
Machine learning, especially deep learning algorithms, have recently led to significant improvements in automatic, low-cost and more accessible disease diagnosis.
How do machines learn to recognize?
Machine learning algorithms can learn to examine patterns like doctors. An important difference between the two diagnosis is that algorithms require many examples to learn, and perhaps thousands of examples to train. Since machines are not able to read books, all these samples must be rendered digitally.
Thus, machine learning can be effective in areas where diagnostic information reviewed by a physician is presented digitally. This area can include cases like this:
• Diagnosis of lung cancer or stroke based on the patient’s CT scan.
• Assessing the risk of sudden cardiac arrest or other heart diseases based on the patient’s ECG or MRI images.
• Classification of skin lesions in the recorded images of the patient’s skin.
• Finding indicators related to the complication of Diabetic retinopathy in the images of the patient’s eyes.
Since a lot of data is available in these areas, the recognition skill of AI-based algorithms is also improving. The difference between the diagnosis with these algorithms and the diagnosis of experts is that the algorithm can reach a result in a fraction of a second and its results can be reproduced all over the world. Maybe soon everyone everywhere in the world will be able to achieve the quality of expert diagnosis at low cost.
Advances in artificial intelligence-based diagnosis
The application of machine learning in diagnosis has recently started and more systems are intervening in the integration of multiple data sources including CT scan, MRI, patient data and even manuscripts to evaluate the disease and its progression.
Artificial intelligence is not a substitute for doctors
It is unlikely that AI will replace doctors immediately, but AI-based systems can identify malignant lesions or dangerous heart patterns for experts and allow them to focus on interpreting these signs.
Rapid drug discovery
Drug discovery and delivery is a costly process. Many of the analytical processes required for drug discovery can be made more efficient with the help of machine learning. This method can eliminate the need for years of research and hundreds of millions of Tomans investment.
Artificial intelligence is currently being used successfully in four areas related to drug delivery:
• Identifying the target for intervention.
• Discovering drug options.
• Accelerate clinical trials.
• Finding biomarkers for disease diagnosis.
Identify the goal of the intervention
The first step to drug discovery is to understand the biological root of the disease and its resistance mechanism. After this stage, the desired targets, which are usually proteins, should be identified to help treat the disease. Methods such as the widely available “small hairpin RNA” (shRNA) assay have significantly increased the amount of data that can be evaluated to discover therapeutic pathways, but using old methods is still a challenge to integrate the vast resources and It is considered a variety of data.
Machine learning algorithms can analyze the available data in a simpler way and even learn to automatically identify the desired proteins.
discovery of drug options
At this stage, researchers must find a compound that interacts with the identified molecule. This work requires the examination of a large number of possible compounds to determine their effect on the target, which can be naturally or experimentally presented.
Current softwares are often sloppy and take a lot of time to review, but AI algorithms can be useful at this stage as well. These algorithms predict the durability of a molecule based on its structure and then select the best molecule from among millions of molecules with the least side effects. In this way, considerable time will be saved in providing medicine.
Accelerating clinical trials
Finding the right options for clinical trials is a difficult task, and if these options are chosen incorrectly, they will add significant time and cost to the trial.
Machine learning can speed up the design of clinical trials by automatically identifying appropriate options and ensuring their use for specific groups of participants. Algorithms can separate good and bad options to choose the best one possible.
These algorithms can act as a warning system in clinical trials so that a trial that does not have an effective result is not performed. In this way, researchers can intervene in time.
Finding biomarkers for disease diagnosis
It is possible to treat a patient only when we are sure of the correct diagnosis. Some diagnostic methods are very expensive and require sophisticated laboratory equipment in addition to expert knowledge.
Biomarkers are molecules that exist in human body fluids, especially blood, and provide the necessary certainty about whether a person is sick. These molecules make the process of diagnosing a disease more safe and less expensive.
Biomarkers can also be used to check the progress of the disease so that doctors can choose the right treatment for the disease and evaluate the drug’s performance. But discovering the right biomarkers for a specific disease is a difficult task and requires a long and expensive process of examining tens of thousands of possible molecules.
Artificial intelligence can automate many manual tasks and speed up the work process. Algorithms based on artificial intelligence divide molecules into good and bad categories so that doctors can focus on investigating the best options.
Biomarkers can be used to identify:
• Diagnostic biomarker, to identify the presence of a disease in the patient’s body in time.
• Biomarker of risk, to detect the risk of disease spreading in the patient’s body.
• Pre-diagnosis biomarker, to identify the possibility of a disease spreading.
• Predictive biomarker to identify whether the patient will have a reaction to the drug or not.
Personalization of treatment
Different patients have different reactions to medicines and treatment plans; As a result, personalization of treatment can increase a person’s life span, but identifying the factors that influence the choice of treatment is a very difficult task.
By automating this complex process, machine learning can help discover features that indicate a patient will respond positively to treatment. In fact, algorithms can predict a patient’s likely response to a particular treatment.
These algorithms learn their work by examining similar patients and comparing the treatment and its outcomes. Algorithm prediction results help doctors to design the treatment plan simply and correctly.
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