Effective End-to-End Deep Learning Process in Medical Imaging Using Independent Task Learning: Application for Diagnosis of Maxillary Sinusitis

Abstract

Purpose This study aimed to propose an effective end-to-end process in medical imaging using an independent task learning (ITL) algorithm and to evaluate its performance in maxillary sinusitis applications. Materials and Methods For the internal dataset, 2122 Waters’ view X-ray images, which included 1376 normal and 746 sinusitis images, were divided into training (n=1824) and test (n=298) datasets. For external validation, 700 images, including 379 normal and 321 sinusitis images, from three different institutions were evaluated. To develop the automatic diagnosis system algorithm, four processing steps were performed: 1) preprocessing for ITL, 2) facial patch detection, 3) maxillary sinusitis detection, and 4) a localization report with the sinusitis detector. Results The accuracy of facial patch detection, which was the first step in the end-to-end algorithm, was 100%, 100%, 99.5%, and 97.5% for the internal set and external validation sets #1, #2, and #3, respectively. The accuracy and area under the receiver operating characteristic curve (AUC) of maxillary sinusitis detection were 88.93% (0.89), 91.67% (0.90), 90.45% (0.86), and 85.13% (0.85) for the internal set and external validation sets #1, #2, and #3, respectively. The accuracy and AUC of the fully automatic sinusitis diagnosis system, including site localization, were 79.87% (0.80), 84.67% (0.82), 83.92% (0.82), and 73.85% (0.74) for the internal set and external validation sets #1, #2, and #3, respectively. Conclusion ITL application for maxillary sinusitis showed reasonable performance in internal and external validation tests, compared with applications used in previous studies.

A Scoping Review of Artificial Intelligence Research in Rhinology

“…Of the 79 articles identified, 3 were published in 2017, 17–19 5 were published in 2018, 20–24 9 were published in 2019, 25–33 15 were published in 2020, 34–48 31 were published in 2021, 49–79 and 16 were published in 2022 80…”

“…Radiology Diagnostics There were 42 articles that used an application of AI for radiology/imaging diagnostics in rhinology. 19,21,22,25,26,29,31,38–43,45,46,48–51,54,55,58,59,62–64,67,69,75,78,80–86,89,91–93,95 Imaging tools used include CT (n = 25), 21,22,25,26,38–43,45,46,55,58,59,63,69,75,81–83,89,91,92,…”

“…The remaining 27 articles aimed to identify or differentiate between various pathologies 19,26,29,31,39,40,42,46,48,49,54,55,58,62–64,67,69,75,80–86,89 including concha bullosa (n = 1), 42 CRS (n = 3), 39,46,83 fungal ball (n = 1), 82 inverted papilloma (n = 3), 19,69,86 nasal cancers (n = 4), 19,49,63,86 maxillary defects (n = 1), 75 maxillary sinus lesions (n = 3), 55,58,81 maxillary sinusitis (n = 6), 29,31,54,62,64,85 nasal fractures (n = 1), 89 nasopharyngeal cancers (n = 4) 40,48,67,80 or other pathologies (n = 1), 84 and osteomeatal complex inflammation (n = 1). 26 Diagnostic accuracy was rated as excellent (n = 12), 39,40,42,46…”

“…There were 42 articles that used an application of AI for radiology/imaging diagnostics in rhinology. MRI (n = 10), 19,[48][49][50][51]78,80,84,86,93 and X-ray (n = 6), 29,31,54,62,64,85 with 1 article using both CT and MRI. 67 ML techniques including CNNs and deep learning were utilized by all 42 studies.…”

The Evolution and Application of Artificial Intelligence in Rhinology: A State of the Art Review

“…DL algorithms have been used to diagnose sinusitis [36][37][38] or quantify sinus volumes [39][40][41][42] on radiographic imaging. In fact, algorithms have shown superior accuracy in the diagnosis of maxillary sinusitis when compared with the performance of radiologists 41 or dental residents.…”