AI-support for the detection of intracranial large vessel occlusions: One-year prospective evaluation.

Purpose: Few studies have evaluated real-world performance of radiological AI-tools in clinical practice. Over one-year, we prospectively evaluated the use of AI software to support the detection of intracranial large vessel occlusions (LVO) on CT angiography (CTA). Method: Quantitative measures (user log-in attempts, AI standalone performance) and qualitative data (user surveys) were reviewed by a key-user group at three timepoints. A total of 491 CTA studies of 460 patients were included for analysis. Results: The overall accuracy of the AI-tool for LVO detection and localization was 87.6%, sensitivity 69.1% and specificity 91.2%. Out of 81 LVOs, 31 of 34 (91%) M1 occlusions were detected correctly, 19 of 38 (50%) M2 occlusions, and 6 of 9 (67%) ICA occlusions. The product was considered user-friendly. The diagnostic confidence of the users for LVO detection remained the same over the year. The last measured net promotor score was -56%. The use of the AI-tool fluctuated over the year with a declining trend. Conclusions: Our pragmatic approach of evaluating the AI-tool used in clinical practice, helped us to monitor the usage, to estimate the perceived added value by the users of the AI-tool, and to make an informed decision about the continuation of the use of the AI-tool.

[Detection of lung nodules. New opportunities in chest radiography]. / Detektion pulmonaler Rundherde. Neue Möglichkeiten der Thoraxradiographie.

BACKGROUND: Chest radiography still represents the most commonly performed X-ray examination because it is readily available, requires low radiation doses and is relatively inexpensive. However, as previously published, many initially undetected lung nodules are retrospectively visible in chest radiographs. STANDARD RADIOLOGICAL METHODS: The great improvements in detector technology with the increasing dose efficiency and improved contrast resolution provide a better image quality and reduced dose needs. METHODICAL INNOVATIONS: The dual energy acquisition technique and advanced image processing methods (e.g. digital bone subtraction and temporal subtraction) reduce the anatomical background noise by reduction of overlapping structures in chest radiography. Computer-aided detection (CAD) schemes increase the awareness of radiologists for suspicious areas. RESULTS: The advanced image processing methods show clear improvements for the detection of pulmonary lung nodules in chest radiography and strengthen the role of this method in comparison to 3D acquisition techniques, such as computed tomography (CT). ASSESSMENT: Many of these methods will probably be integrated into standard clinical treatment in the near future. Digital software solutions offer advantages as they can be easily incorporated into radiology departments and are often more affordable as compared to hardware solutions.

New methods for using computer-aided detection information for the detection of lung nodules on chest radiographs.

OBJECTIVE: To investigate two new methods of using computer-aided detection (CAD) system information for the detection of lung nodules on chest radiographs. We evaluated an interactive CAD application and an independent combination of radiologists and CAD scores. METHODS: 300 posteroanterior and lateral digital chest radiographs were selected, including 111 with a solitary pulmonary nodule (average diameter, 16 mm). Both nodule and control cases were verified by CT. Six radiologists and six residents reviewed the chest radiographs without CAD and with CAD (ClearRead +Detect™ 5.2; Riverain Technologies, Miamisburg, OH) in two reading sessions. The CAD system was used in an interactive manner; CAD marks, accompanied by a score of suspicion, remained hidden unless the location was queried by the radiologist. Jackknife alternative free response receiver operating characteristics multireader multicase analysis was used to measure detection performance. Area under the curve (AUC) and partial AUC (pAUC) between a specificity of 80% and 100% served as the measure for detection performance. We also evaluated the results of a weighted combination of CAD scores and reader scores, at the location of reader findings. RESULTS: AUC for the observers without CAD was 0.824. No significant improvement was seen with interactive use of CAD (AUC = 0.834; p = 0.15). Independent combination significantly improved detection performance (AUC = 0.834; p = 0.006). pAUCs without and with interactive CAD were similar (0.128), but improved with independent combination (0.137). CONCLUSION: Interactive CAD did not improve reader performance for the detection of lung nodules on chest radiographs. Independent combination of reader and CAD scores improved the detection performance of lung nodules. ADVANCES IN KNOWLEDGE: (1) Interactive use of currently available CAD software did not improve the radiologists' detection performance of lung nodules on chest radiographs. (2) Independently combining the interpretations of the radiologist and the CAD system improved detection of lung nodules on chest radiographs.

Chest radiography: new technological developments and their applications.

Digital chest radiography is still the most common radiological examination. With the upcoming three-dimensional (3D) acquisition techniques the value of radiography seems to diminish. But because radiography is inexpensive, readily available, and requires very little dose, it is still being used for the first-line detection of many cardiothoracic diseases. In the last decades major technical developments of this 2D technique are being achieved. First, hardware developments of digital radiography have improved the contrast to noise, dose efficacy, throughput, and workflow. Dual energy acquisition techniques reduce anatomical noise by splitting a chest radiograph into a soft tissue image and a bone image. Second, advanced processing methods are developed to enable and improve detection of many kinds of disease. Digital bone subtraction by a software algorithm mimics the soft tissue image normally acquired with dedicated hardware. Temporal subtraction aims to rule out anatomical structures clotting the image, by subtracting a current radiograph with a previous radiograph. Finally, computer-aided detection systems help radiologists for the detection of various kinds of disease such as pulmonary nodules or tuberculosis.