# Deep Learning Enhances Radiologists' Detection of Potential Spinal Alignancies in CT Scans
## 4 Discussion  
A complete and comprehensive review of a CT scan is crucial for a patient's health and has a significant impact on decision-making. CT imaging can assess spinal bone metastatic lesions up to 6 months before plain radiographs [19]. However, smaller lesions or those that do not have significant cortical destruction are often underreported or missed during CT image reporting [20]. In a systematic review, [PERSON] et al. reported that radiologists' recognition of incidental vertebral findings (in this case, fractures) was low, with a mean reporting rate of just 27.4% [21]. A major fraction of false negatives are significant bone lesions that could indicate potential malignancy [3]. [PERSON] et al. showed in an internal department analysis that these types of spinal lesions were most frequently misinterpreted on CT images, with 14 out of 16 missed findings being metastatic [22]. False-negative cases are the most common perceptual errors [4], with CT imaging being especially susceptible [22]. Errors made in previous radiology reports can lead to the tendency of radiologists to replicate the error in subsequent reports, which is referred to as 'alliterative bias' [23]. This concept reiterates the importance of some confirmation protocols in medical practice. While double-reading practices significantly impact the quality of radiological reports, clinical workload and staff shortages make a routine human double-reader scheme hard to implement [24; 25]. Computer-aided detection (CAD) systems have supported radiologists in their workflow even before the era of deep-learning tools, with some of the best examples being  
Figure 3: Performance metrics of the six participants on case level, with each participant color coordinated; (**a**) Sensitivity and specificity of reporting potentially malignant spinal lesions without (\(-\)) and with (\(+\)) the aid of an AI tool as a second reader; (**b**) Number of reported true-positive findings without (\(-\)) and with (\(+\)) the aid of an AI tool as a second reader. small functions and add-ons for DICOM viewers, such as contrast enhancers or manual annotation support. With the use of AI increasing rapidly in the fields of medicine, with radiology as a leading candidate, future deep-learning-based CAD systems will not only optimize the users' workflow and improve their diagnostic abilities but also weigh in on their medical judgment and decision-making process. CTs are not just a series of images; they contain extensive information about the pathology in question that sometimes cannot be interpreted by the bare human eye [26]. There are models developed for almost every disease that can be assessed radiologically. More specifically, detection systems have shown an emerging potential in reducing missed radiological spinal lesions as a second reader [13; 27; 28; 29; 30]. In our study, we investigated the effects of a DLA when implemented in a routine CT reporting process performed by six radiologists having different experience levels. Our results show that DL-based spinal lesion detection can improve inter-observer agreement and overall increase performance in detecting these radiological findings, regardless of the training level of our participants. The case level sensitivity increased by 20.83 percentage points when the participants were aided by the DLA. However, this improvement should only be interpreted in the context of a rather low baseline of reported spinal findings in the first round. Another observational study by [PERSON] et al. [13] showed that the sensitivity of radiologists in detecting bone metastases could be elevated with the help of a DLA by 15.3 percentage points. In a similar study, [PERSON] et al. [31] reported an improvement in the performance of less-experienced radiologists in brain metastasis detection by 4.90 percentage points. Like our own study, [PERSON] et al. observed no significant increase in false-positive findings when utilizing a CAD system.