Anton Faron1,2, Thorsten Sichtermann3, Nikolas Teichert3,4, Julian A Luetkens5, Annika Keulers3, Omid Nikoubashman3, Jessica Freiherr3, Anastasios Mpotsaris3, Martin Wiesmann3. 1. Department of Radiology, University Hospital Bonn, Sigmund-Freud-Str. 25, 53127, Bonn, Germany. Anton.Faron@ukbonn.de. 2. Department of Diagnostic and Interventional Neuroradiology, University Hospital RWTH Aachen, Aachen, Germany. Anton.Faron@ukbonn.de. 3. Department of Diagnostic and Interventional Neuroradiology, University Hospital RWTH Aachen, Aachen, Germany. 4. Department of Diagnostic and Interventional Radiology, University Hospital Düsseldorf, Düsseldorf, Germany. 5. Department of Radiology, University Hospital Bonn, Sigmund-Freud-Str. 25, 53127, Bonn, Germany.
Abstract
PURPOSE: To study the clinical potential of a deep learning neural network (convolutional neural networks [CNN]) as a supportive tool for detection of intracranial aneurysms from 3D time-of-flight magnetic resonance angiography (TOF-MRA) by comparing the diagnostic performance to that of human readers. METHODS: In this retrospective study a pipeline for detection of intracranial aneurysms from clinical TOF-MRA was established based on the framework DeepMedic. Datasets of 85 consecutive patients served as ground truth and were used to train and evaluate the model. The ground truth without annotation was presented to two blinded human readers with different levels of experience in diagnostic neuroradiology (reader 1: 2 years, reader 2: 12 years). Diagnostic performance of human readers and the CNN was studied and compared using the χ2-test and Fishers' exact test. RESULTS: Ground truth consisted of 115 aneurysms with a mean diameter of 7 mm (range: 2-37 mm). Aneurysms were categorized as small (S; <3 mm; N = 13), medium (M; 3-7 mm; N = 57), and large (L; >7 mm; N = 45) based on the diameter. No statistically significant differences in terms of overall sensitivity (OS) were observed between the CNN and both of the human readers (reader 1 vs. CNN, P = 0.141; reader 2 vs. CNN, P = 0.231). The OS of both human readers was improved by combination of each readers' individual detections with the detections of the CNN (reader 1: 98% vs. 95%, P = 0.280; reader 2: 97% vs. 94%, P = 0.333). CONCLUSION: A CNN is able to detect intracranial aneurysms from clinical TOF-MRA data with a sensitivity comparable to that of expert radiologists and may have the potential to improve detection rates of incidental findings in a clinical setting.
PURPOSE: To study the clinical potential of a deep learning neural network (convolutional neural networks [CNN]) as a supportive tool for detection of intracranial aneurysms from 3D time-of-flight magnetic resonance angiography (TOF-MRA) by comparing the diagnostic performance to that of human readers. METHODS: In this retrospective study a pipeline for detection of intracranial aneurysms from clinical TOF-MRA was established based on the framework DeepMedic. Datasets of 85 consecutive patients served as ground truth and were used to train and evaluate the model. The ground truth without annotation was presented to two blinded human readers with different levels of experience in diagnostic neuroradiology (reader 1: 2 years, reader 2: 12 years). Diagnostic performance of human readers and the CNN was studied and compared using the χ2-test and Fishers' exact test. RESULTS: Ground truth consisted of 115 aneurysms with a mean diameter of 7 mm (range: 2-37 mm). Aneurysms were categorized as small (S; <3 mm; N = 13), medium (M; 3-7 mm; N = 57), and large (L; >7 mm; N = 45) based on the diameter. No statistically significant differences in terms of overall sensitivity (OS) were observed between the CNN and both of the human readers (reader 1 vs. CNN, P = 0.141; reader 2 vs. CNN, P = 0.231). The OS of both human readers was improved by combination of each readers' individual detections with the detections of the CNN (reader 1: 98% vs. 95%, P = 0.280; reader 2: 97% vs. 94%, P = 0.333). CONCLUSION: A CNN is able to detect intracranial aneurysms from clinical TOF-MRA data with a sensitivity comparable to that of expert radiologists and may have the potential to improve detection rates of incidental findings in a clinical setting.
Authors: Bio Joo; Hyun Seok Choi; Sung Soo Ahn; Jihoon Cha; So Yeon Won; Beomseok Sohn; Hwiyoung Kim; Kyunghwa Han; Hwa Pyung Kim; Jong Mun Choi; Sang Min Lee; Tae Gyu Kim; Seung-Koo Lee Journal: Yonsei Med J Date: 2021-11 Impact factor: 2.759
Authors: Javier Bravo; Arvin R Wali; Brian R Hirshman; Tilvawala Gopesh; Jeffrey A Steinberg; Bernard Yan; J Scott Pannell; Alexander Norbash; James Friend; Alexander A Khalessi; David Santiago-Dieppa Journal: Cureus Date: 2022-03-30
Authors: Lenhard Pennig; Ulrike Cornelia Isabel Hoyer; Alexandra Krauskopf; Rahil Shahzad; Stephanie T Jünger; Frank Thiele; Kai Roman Laukamp; Jan-Peter Grunz; Michael Perkuhn; Marc Schlamann; Christoph Kabbasch; Jan Borggrefe; Lukas Goertz Journal: Neuroradiology Date: 2021-04-10 Impact factor: 2.804