Machine learning in medical imaging: automated feature extraction in diagnostics

Machine Learning (ML) algorithms focused on medical image analysis have revolutionized the diagnosis process. Now doctors can detect more diseases than previously. For example, more genetic illnesses can be recognized that were not diagnosed before. Other conditions, such as Asperger’s or Parkinson’s, can be detected better by ML-based facial recognition solutions.
ML-based algorithms that analyze medical images will positively impact different diagnostic fields. Let’s talk about some most popular areas:

• Pathology. Microscopy and cytology are used for the early diagnosis of bladder cancer.

• Dermatology. A pigmented lesion can be evaluated for diagnosing melanoma.

• Ophthalmology. Early stages of diabetic retinopathy and cardiovascular disease can be diagnosed by examining the retinal vessel.

• Radiology. ML focused on medical imaging is immensely useful in radiology as well. Algorithms allow a physician to visualize minor changes in cells and recognize unhealthy cells. It offers a possibility to detect early-stage cancer. In some cases, unnecessary tests can be avoided. For example, the American Cancer Society statistics from 2017 show at least one false-positive finding in the results of about 50% of the women who get annual mammograms over ten years. Besides the apparent side effects such as anxiety and physical discomfort, it often leads to additional tests that are not needed.

A new quality of medical treatment

Using ML methods by analyzing medical images, a human physician can significantly improve the quality of medical care.
There are many possibilities of how automated and high-precision feature extraction can support healthcare organizations in everyday life. For example:

Personalized healthcare

Medical treatment can be personalized and matched to individual characteristics. Physicians can develop a more effective treatment with a complete picture of the patient’s medical data. Now doctors can choose diagnoses or avoid risks not only after analyzing each patient’s medical history but also by using prognosticate abilities of ML.

Analyze lifestyle and behavior for preventing illnesses

ML algorithms can support physicians in giving better advice about behavior and lifestyle to improve patients’ health.

Cut down drug production costs

ML can make pharmaceutical drugs more affordable by reducing production time and costs.

Optimize the clinical trial process

Clinical trials can become cheaper but still be accurate. Algorithms can find clinical trial candidates by comparing data from medical history records. ML solutions can monitor the clinical trial process, track results, etc.

Allow robotic surgery

Medical imaging is beneficial in the field of robotic surgery as well. It allows doctors to operate more precisely and minimize risks.

Predict and model epidemics

ML algorithms can soon suggest the regions where epidemic outbreaks are possible by comparing information taken from satellites, social media, and other information sources.

Medical image analysis solutions ready to use

While diagnostic confidence never reaches 100%, combining machines plus physicians reliably enhances system performance. The computerized module for detecting abnormalities in marked regions of medical images is essential. For example, applications for detecting lung diseases are a new machine learning solution that can help improve the diagnostic process. With this solution, doctors can recognize a lot of conditions very quickly.

Doctors can make diagnosis more accessible, faster, and more precise with these applications. The solution offers better visibility of infected areas because they are highlighted.

Related: Benefits of implementing AI in medical imaging

These programs help identify a particular disease more quickly, suggesting diagnoses from the database. The algorithm calculates the probability of each condition in percentages. Doctors can share the received data with colleagues to discuss the diagnosis over a network.

Modern solutions for the detection of lung diseases are a necessary assistant when many patients need medical help immediately, and time for each patient has significantly decreased. Because doctors spend less time on diagnostics, they can focus on the decision-making and treatment of patients.

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