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Editorial
. 2022 Sep;23(9):847-852.
doi: 10.3348/kjr.2022.0193. Epub 2022 Jun 20.

Successful Implementation of an Artificial Intelligence-Based Computer-Aided Detection System for Chest Radiography in Daily Clinical Practice

Seungsoo Lee  1 , Hyun Joo Shin  1 , Sungwon Kim  2 , Eun-Kyung Kim  3
Affiliations
Editorial

Successful Implementation of an Artificial Intelligence-Based Computer-Aided Detection System for Chest Radiography in Daily Clinical Practice

Seungsoo Lee et al. Korean J Radiol. 2022 Sep.
No abstract available

Keywords: Artificial intelligence; Computer aided detection; Picture archiving and communication system.

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Conflict of interest statement

The authors have no potential conflicts of interest to disclose.

Figures

Fig. 1
Fig. 1. Chest radiographs analyzed with AI-CAD version 2.
A. Chest radiography of a 52-year-old male who visited the outpatient clinic of the cardiology department for a follow-up of arrhythmia. B. AI-CAD presenting a focal abnormal lesion in the right lung with a total abnormality score of 43%. Note that the color heatmap does not have any annotation or separate abnormality score on version 2. C. On chest CT scan, irregular shaped part-solid nodule was noted in the right lower lobe. The pathology confirmed adenocarcinoma, stage lA3, after surgical resection. The patient is now routinely followed for resectable lung cancer. AI-CAD = artificial intelligence-based computer-aided diagnosis
Fig. 2
Fig. 2. Chest radiographs analyzed with AI-CAD version 3.
A. Chest radiograph of an 88-year-old male admitted to the intensive care unit for traumatic brain injury. B. AI-CAD showing the left Ptx with an abnormality score of 98% depicted with a contour map. Emergency thoracotomy was performed immediately after the image was taken. Note the separate contour map with abbreviations; Csn, Ndl, and right PEf on version 3. AI-CAD = artificial intelligence-based computer-aided diagnosis, Csn = consolidation, Ndl = nodule, PEf = pleural effusion, Ptx = pneumothorax
Fig. 3
Fig. 3. Examples of triage with the advanced search function on the worklist and an actual related clinical case.
A. Radiologists can arrange the worklist to show chest radiography in order of high abnormality scores and with this triage, can read cases of higher priority first. Note scores of abnormal findings (CSN, ATL, NDL, PTX, PPM) following the highest abnormality score. B. Based on the abnormality score, the radiologist could immediately report an unexpected PPM from the chest radiograph of a 74-year-old male admitted to the psychiatric ward due to obsessive compulsive disorder. The radiologist was able to sort the worklist to show radiographies not ordered by the department of pulmonology or thoracic surgery, and found an image ordered by the psychiatrist that was high on the worklist with an abnormality score of 97.4%. This abnormality score was due to the PPM detected by AI-CAD. The critical value report was sent to the clinician who had not detected this abnormality and the patient ended up undergoing emergency surgery after an additional CT scan, which showed unexpected gastric ulcer perforation. AI-CAD = artificial intelligence-based computer-aided diagnosis, ATL = atelectasis, CSN = consolidation, NDL = nodule, PPM = pneumoperitoneum, PTX = pneumothorax
See this image and copyright information in PMC

References

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