What Student Need to Know about Imaging through Artificial Intelligence In Radio Diagnosis.
Radiology is an appealing specialty to many medical students for various reasons, including the lifestyle of diagnostic radiology and diverse sub-specialty options for diagnostic and interventional radiology. Machine learning applications were utilized in screening exams such as mammography, chest CT, and colonography. In addition to those detection tasks, some applications also aided in determining whether a lesion was cancerous. Machine learning software will serve as a very experienced clinical assistant, augmenting the doctor and making workflow more efficient in radiology.
In radiomics, AI can foster the analysis of the features and help in the correlation with other omics data. Imaging biobanks would become a necessary infrastructure to organise and share the image data from which AI models can be trained. AI can be used as an optimising tool to assist the technologist and radiologist in choosing a personalised patient’s protocol, tracking the patient’s dose parameters, providing an estimate of the radiation risks. AI can also aid the reporting workflow and help the linking between words, images, and quantitative data. Finally, AI coupled with CDS can improve the decision process and thereby optimise clinical and radiological workflow.
The use of artificial intelligence (AI) in diagnostic medical imaging is undergoing extensive evaluation. AI has shown impressive accuracy and sensitivity in the identification of imaging abnormalities and promises to enhance tissue-based detection and characterization. However, with improved sensitivity emerges an important drawback, namely, the detection of subtle changes of indeterminate significance.
AI algorithms not only spot details too subtle for the human eye to see. They can also develop entirely new ways of interpreting medical images, sometimes in ways humans do not understand. The numerous researchers, start-up companies and scanner manufacturers designing AI programs hope they can improve the accuracy and timeliness of diagnoses, provide better treatment in developing countries and remote regions that lack radiologists, reveal new links between biology and disease, and even help to predict how soon a person will die.
Identification of subtle structural and functional cardiac abnormalities with important clinical correlation could also be accomplished by AI techniques, such as convolutional neural networks, when applied to echocardiography, the most common form of cardiovascular imaging.
When a radiologist calls up a chest computed tomography (CT) scan to read, the AI will review the image and identify potential findings immediately — from the image and also by combing through the patient history related to the particular anatomy scanned. If the exam order is for chest pain, the AI system will call up:
• All the relevant data and prior exams specific to prior cardiac history;
• Pharmacy information regarding drugs specific to COPD, heart failure, coronary disease and anticoagulants;
• Prior imaging exams from any modality of the chest that may aid in diagnosis;
• Prior reports for that imaging;
• Prior thoracic or cardiac procedures;
• Recent lab results; and
• Any pathology reports that relate to specimens collected from the thorax.
Outside the traditional radiology activities of lesion detection and characterisation, and assessment of response to treatment, AI is likely to impact other areas of radiologists’ and other healthcare professionals’ work. Examples include:
Radiomics: extraction of features from diagnostic images, the final product of which is a quantitative feature/parameter, measurable and mineable from images. A Radiomics analysis can extract over 400 features from a region of interest in a CT, MRI, or PET study, and correlate these features with each other and other data, far beyond the capability of the human eye or brain to appreciate. Such features may be used to predict prognosis and response to treatment. AI can support analysis of radiomics features and help in the correlation between radiomics and other data (proteomics, genomics, liquid biopsy) by building patients’ signatures.
Imaging biobanks: the constantly enlarging memory capacity of computers permits storage of large amounts of data. In radiology, the need to store native images and big data derived from quantitative imaging represents the main cause of PACS overload. Quantitative imaging can produce imaging biomarkers that can be stored and organised in large imaging biobanks (potentially using data from many institutions and locations), available to be processed, analysed, and used to predict the risk of disease in large population studies and treatment response. Large biobanks also have the potential to become the repository of digital patients that can be used by AI to perform simulations of disease development and progression. Moreover, imaging biobanks would become a necessary infrastructure to organise and share the image data from which AI models can be trained.
by British BioMedicine Institute
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