# Deep Learning Enhances Radiologists' Detection of Potential Spinal Alignancies in CT Scans
## 1 Introduction  
Discrepancies in radiology are a long-known issue and--due to the extended workload and limited evaluation time--have remained the same despite continuously improving imaging techniques over the last decades [1, 2]. A perceptual error, or false negative, is an abnormality present in a diagnostic image but not described by the interpreter. Such overlooked findings constitute the vast majority of human error in image interpretation [2, 3, 4].  
Spinal bone lesions that present themselves as a conglomerate are frequently an indicator of malignancy, with the vertebrae being the most prevalent hotspot for bone metastasis [5]. On the other side of the spectrum, solitary lesions are more challenging and can indicate both malignant and benign processes [6, 7], creating uncertainty within the diagnostic procedure, which may require further investigation [8]. In this case, if missed or initially overlooked (perceptual error), they can exhibit major negative consequences on a patient's quality of life and, subsequently, their morbidity and mortality [9, 10].  
With CT being a reliable imaging modality for assessing osseous involvement and the destruction degree of spine abnormalities [6; 11], the past years have shown a rising interest in automatizing the detection and classification of spinal lesions to a large extent [12; 13; 14]. Artificial intelligence (AI) is now an active part of various medical diagnostic procedures within real-life hospital workflows, with deep-learning (DL)-based analysis of radiologic images as one of its key applications. AI as a screening tool or a second reader for abnormality detection already shows promising results in various fields, such as chest X-ray reporting and lung nodule detection [15; 16]. However, reliable deep-learning-based algorithms for spinal lesion detection are still sparse as their development has proven to be more challenging, mostly due to the overlapping image features of degenerative and neoplastic events [6]. To our knowledge, there are currently no EU MDR-certified or FDA-approved AI second reader software that detect incidental spinal lesions in CT scans of unrelated indications. A reliable algorithm with such capacities could assist the reporting physician with accurate supplementary information, reduce the rate of missed potentially malignant lesions, and streamline the diagnostic pathway.  
This work examines the clinical impact of a deep-learning algorithm (DLA) that assists radiologists in their day-to-day workflow within a simulated hospital setting. The algorithm was developed to detect potentially malignant cases within the vertebrae and act as a second reader, using native and contrast-enhanced abdomen and thoracic CT imaging sequences. Its clinical efficacy was evaluated in an observational cross-over study design, where the algorithm's performance and the effect on the decision-making of six subspecialty radiologists with and without the intervention of the DLA were assessed. The distinct feature of this study design is its emphasis on reducing incidental findings during the reporting of other main underlying diseases that the patient has. This approach, further accentuated by limiting the scope to only CT abdomen and thoracic scans, shifts the focus away from solely detecting vertebral malignancy as the primary objective of the responsible radiologist. Such \"background acting\" tools open new avenues in how one can correctly integrate AI in the medical sector and underline the crucial role of human involvement in the overall process.