computer-aided diagnosis ) – provides a possible diagnosis / nature of the lesion , or malignancy risk . It uses algorithms that take into account image features , location of lesion and radiomics ( textural analysis by computer that is not detected by the human eye ) to assess the risk of malignancy or the likely diagnosis . It is being used for assessing risk of malignancy in breast and lung lesions .
• Computer-aided segmentation – used for drawing around structures on the images . It can be used for drawing around tumours or even around normal structures for radiotherapy planning . It can also be used for display of angiograms from a CT or MRI study .
• Computer-aided quantification – this form of AI will count the number , size and volume of abnormalities such as lung nodules on CT . It can be used for assessing percentage involvement of lungs in interstitial lung diseases , quantification of bone age and degree of vessel stenosis . It can provide an emphysema index , osteoporosis score and calcium score .
• Computer-aided prediction – can be used for predicting onset of stroke , prognosis of disease , survival and response to therapy . By quantifying the shape volume and texture of the hippocampus , AI maybe used for predict the risk of dementia .
Diagnostic accuracy of AI algorithms It is really important for radiologists and patients to know how much we really rely on computer AI ? Computer vision AI is a diagnostic tool . Like any diagnostic test , AI algorithms have both sensitivity and specificity . No algorithm will claim to be 100 % accurate . Hence , it is really important , when doctors take decisions on management of the patient , or radiologists issue a narrative actionable report , that they are aware of the limitations ( sensitivity and specificity of the algorithm used defining risk of malignancy or tentative diagnosis , for example , 95 % sensitive and 80 % specific ). Hence , when the output from computer vision AI is sent to PACS , or enterprise viewer , it is mandatory that the sensitivity and specificity of the algorithm used are displayed along with the images and AI markers . Display of sensitivity and specificity of AI algorithms for the front line doctors and reporting radiologist is essential for patient safety .
The accuracy of these algorithms is very much dependent on the type of algorithms used , and also the acquisition parameters applied by the modality . If the algorithm is to be accurate , it is really important that the acquisition parameters are standardised prior to application of the algorithm . Hence , many of the computer vision AI algorithms need to be integrated with the modality . For example , if there is a requirement for lung nodule detection on CT chest protocol , the scanner should automatically reset the acquisition parameters to optimise lung nodule detection .
Artificially intelligent machines and scanners Current X-ray machines , CT scan and MRI scanners produce images only and will be replaced by intelligent machines and scanners in the future . All digital radiography machines will apply a chest shadow algorithm and produce
Computer vision will aid radiologists in producing narrative and actionable reports
15 HHE 2018 | hospitalhealthcare . com images along with markers for shadows . The sensitivity and specificity of the marker detection will be displayed along with the images . Similarly , digital radiography machines have fracture analysis . The sensitivity and specificity of the output will be sent to PACS along with fracture markers . Likewise , CT scanners will have lung nodule detection algorithms applied . CT scanners will also output the emphysema index , osteoporosis index and calcium score along with their sensitivity and specificity for calculation for each of these algorithms .
Artificially intelligent PACS Currently PACS is only capable of displaying images as sent by modalities . However , in the future , they will also be expected to display CAD markers . PACS vendors will also apply some computer vision algorithms . These include image fusion or co-registration . This will enable radiologists to fuse images from different modalities while automatically modifying the zoom , slice thickness and the acquisition parameters . PACS vendors may apply computer aided segmentation algorithms for anomalies such as liver metastases ( for comparison and follow-up ).
Future PACS reading workflow Currently radiologists are not supported by computer vision AI . However , the rapid pace of development is afoot and the radiologist reporting workflow is going to change . Currently radiologist read images without any CAD markers . In the future , many forms of detection markers along with quantification and classification of anomalies will support radiologists reading workflow . We will need to learn how to issue narrative actionable reports in the context of AI , while understanding the sensitivity and specificity of the algorithms applied . We will need to be able to discount anomalies when the computer gets it wrong . We will need to provide more human understandable narrative report from the scientific jargon provided by computer algorithms . Once again radiologists working patterns are about to change enormously . Computer vision will help radiologist detect subtle masses on mammograms , detect lung nodules on CT , fractures on plain X-ray , shadows on chest X-ray , etc . Computer vision will also provide a malignancy index for breast lesions and lung nodules . Outputs such as emphysema index , osteoporosis score and calcium score will be generated by CT scanners .
Conclusions Computer vision will aid radiologists in producing narrative and actionable reports . These computer vision algorithms will provide great decision support for radiologist reporting . It is envisaged that the modalities such as digital radiography machines , CT scanners and MRI scanners will output intelligent information along with images . Intelligent machines will replace the current machines . It is also envisaged that current PACS systems will be replaced by intelligent PACS systems . Radiologists will learn to understand these computer-generated scientific outputs to produce human readable actionable reports to support patient management decisions .