Deep learning enhances radiologists’ detection of potential spinal malignancies in CT scans
Incidental spinal bone lesions, potential indicators of malignancies, are frequently underreported in abdominal and thoracic CT imaging due to scan focus and diagnostic bias towards patient complaints. Here, we evaluate a deep-learning algorithm (DLA) designed to support radiologists’ reporting of i...
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| Main Authors: | , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
13 July 2023
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| In: |
Applied Sciences
Year: 2023, Volume: 13, Issue: 14, Pages: 1-11 |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app13148140 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.3390/app13148140 Verlag, kostenfrei, Volltext: https://www.mdpi.com/2076-3417/13/14/8140 
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| Author Notes: | Leonard Gilberg, Bianca Teodorescu, Leander Maerkisch, Andre Baumgart, Rishi Ramaesh, Elmer Jeto Gomes Ataide and Ali Murat Koç |
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| 520 | |a Incidental spinal bone lesions, potential indicators of malignancies, are frequently underreported in abdominal and thoracic CT imaging due to scan focus and diagnostic bias towards patient complaints. Here, we evaluate a deep-learning algorithm (DLA) designed to support radiologists’ reporting of incidental lesions during routine clinical practice. The present study is structured into two phases: unaided and AI-assisted. A total of 32 scans from multiple radiology centers were selected randomly and independently annotated by two experts. The U-Net-like architecture-based DLA used for the AI-assisted phase showed a sensitivity of 75.0% in identifying potentially malignant spinal bone lesions. Six radiologists of varying experience levels participated in this observational study. During routine reporting, the DLA helped improve the radiologists’ sensitivity by 20.8 percentage points. Notably, DLA-generated false-positive predictions did not significantly bias radiologists in their final diagnosis. These observations clearly indicate that using a suitable DLA improves the detection of otherwise missed potentially malignant spinal cases. Our results further emphasize the potential of artificial intelligence as a second reader in the clinical setting. | ||
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