How to reduce the diagnostic errors using AI and Medical Image annotation?

AI bringing revolutionary wave in Medical Imaging

Do you know diagnostic errors are the 3rd key cause for the deaths after heart diseases and cancer? [1] Is medical Image annotation a solution to this problem?

According to The National Academies of Medicine report, 10% to 15% of diagnostic errors occur in the form of missed, incorrect or delayed diagnosis. [2] The study also says that most people will face this problem at least once in their lifetime.

The Difficulty of the diagnostic process

Figure1-Source:http://www.nationalacademies.org/hmd/~/media/Files/Report%20Files/2015/Improving-Diagnosis/DiagnosticError_ReportBrief.pdf
Diagnosis is not a simple process and not at all an endpoint. There are over 10,000 known diseases for which we have 5000 variety of lab tests. The irony is there are a small number of symptoms. And they might have dozens and hundreds of possible explanations. [4]

Diagnostic and Radiology tests help in clarifying the problem, but often takes time. Errors might occur unintentionally and even sometimes it might be difficult for the human eye and intelligence to understand and detect the disease.

One of the most common types of errors is radiological errors which are substantial. Every moment in life is precious, not only for the patients but also for those who are prone to the disease. Early detection can save them from hell lot of trauma that they might undergo.

“A stitch in time saves nine”

Understanding the errors in radiology

Perpetual errors and interpretive are the two types of errors in radiology. Among these perpetual errors are the most common errors. It accounts for 60–80% of radiologist errors. [3] They occur at the initial phases of disease detection when the abnormality is evident on a diagnostic image but left unidentified or misinterpreted unintentionally.

A new wave in radiology and disease detection

AI-driven computer vision models are making a pavement. Machine models are transforming themselves as indispensable in modern medicine. They help Diagnostic radiologists who use medical images to detect, characterise and interpret disease. They play a key role as doctors heavily rely on medical imaging to diagnose, prognosis and managing the traumatic conditions of their patients.

Wow!!……..

A great thanks to AI (Artificial Intelligence)s for making our lives simple and easy. Human and machine are always a good and healthy combination. This combination attempts to solve the world’s most critical problems in the simplest method.

AI with the help of Deep learning trains an algorithm capable of integrating multiple medical image recognition to diagnose diseases like tumours, cancers etc.

Is it that simple with medical image annotation…….

The computer vision models are fed with hundreds of medical images with labelled regions showing the affected region. These images serve as a base for the model that trains the machine to detect diseases using the ML algorithms.

Benefits of medical image annotation in Healthcare.

It helps the model to detect and interpret the changes in medical images

· It helps radiologist to make better decisions. Hence supporting to save time achieve goals with minimal errors and discrepancies.

Tools used for medical image annotation: There are certain platforms like OCLAVI that provides tools that are best for precise annotation.

· Polygon tool for medical image annotation helps in training the machine model to detect diseases like tumours, cancer, infections etc .

Image before annotation
Image after annotating with polygon tool using OCLAVI platform
Image before annotation
Image after annotating with polygon tool using OCLAVI platform

· Dot annotation tool in detecting very minor changes happening in a human body at the very initial stages of the disease.

References:

1. https://www.cnbc.com/2018/02/22/medical-errors-third-leading-cause-of-death-in-america.html

2. https://psnet.ahrq.gov/primers/primer/12

3.https://pubs.rsna.org/doi/full/10.1148/rg.2015150023https://www.improvediagnosis.org/page/Diagnosis

4. http://www.nationalacademies.org

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