Deep learning algorithm in detecting intracranial hemorrhages on emergency computed tomographies

Kundisch, Almut; Hönning, Alexander; Mutze, Sven; Kreißl, Lutz; Spohn, Frederik; Lemcke, Johannes; Sitz, Maximilian; Sparenberg, Paul; Goelz, Leonie

doi:10.1371/journal.pone.0260560

Cited by 28 publications

(7 citation statements)

References 45 publications

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“…Other findings with high HU, such as basal ganglia calcification, beam hardening artifacts, dense cortical veins, and dural venous sinuses, were misclassified as hemorrhage. This is concordant with prior results in that common AI overcalls are calcification and beam-hardening artifacts 25 , 26 . While radiologists and clinicians gain experience in identifying actual ICHs, AI analysis software must also be trained to recognize these ICH mimickers.…”

Section: Discussionsupporting

confidence: 92%

A comparison of performance between a deep learning model with residents for localization and classification of intracranial hemorrhage

Angkurawaranon,

Sanorsieng,

Unsrisong

et al. 2023

Sci Rep

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Intracranial hemorrhage (ICH) from traumatic brain injury (TBI) requires prompt radiological investigation and recognition by physicians. Computed tomography (CT) scanning is the investigation of choice for TBI and has become increasingly utilized under the shortage of trained radiology personnel. It is anticipated that deep learning models will be a promising solution for the generation of timely and accurate radiology reports. Our study examines the diagnostic performance of a deep learning model and compares the performance of that with detection, localization and classification of traumatic ICHs involving radiology, emergency medicine, and neurosurgery residents. Our results demonstrate that the high level of accuracy achieved by the deep learning model, (0.89), outperforms the residents with regard to sensitivity (0.82) but still lacks behind in specificity (0.90). Overall, our study suggests that the deep learning model may serve as a potential screening tool aiding the interpretation of head CT scans among traumatic brain injury patients.

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Section: Discussionsupporting

confidence: 92%

A comparison of performance between a deep learning model with residents for localization and classification of intracranial hemorrhage

Angkurawaranon,

Sanorsieng,

Unsrisong

et al. 2023

Sci Rep

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“…In general, it appears reasonable that the relatively high false-positive rate may be explained by disturbing hyperdensities like artifacts, tumors, and defects, especially near the skull base [22]. For example, Kundisch et al described a relevant detection rate for these findings in an algorithm from a different vendor [23]. Our first two subgroup analyses suggest that neither motion and beam hardening artifacts nor chronic brain defects are a reason for the low PPV, since the adjusted rate of false-positive findings was just a tiny bit lower (PPV: 63.0% compared to 56.7%).…”

Section: Discussionmentioning

confidence: 99%

Diagnostic Accuracy and Performance Analysis of a Scanner-Integrated Artificial Intelligence Model for the Detection of Intracranial Hemorrhages in a Traumatology Emergency Department

Kiefer,

Kopp,

Ruettinger

et al. 2023

Bioengineering

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Intracranial hemorrhages require an immediate diagnosis to optimize patient management and outcomes, and CT is the modality of choice in the emergency setting. We aimed to evaluate the performance of the first scanner-integrated artificial intelligence algorithm to detect brain hemorrhages in a routine clinical setting. This retrospective study includes 435 consecutive non-contrast head CT scans. Automatic brain hemorrhage detection was calculated as a separate reconstruction job in all cases. The radiological report (RR) was always conducted by a radiology resident and finalized by a senior radiologist. Additionally, a team of two radiologists reviewed the datasets retrospectively, taking additional information like the clinical record, course, and final diagnosis into account. This consensus reading served as a reference. Statistics were carried out for diagnostic accuracy. Brain hemorrhage detection was executed successfully in 432/435 (99%) of patient cases. The AI algorithm and reference standard were consistent in 392 (90.7%) cases. One false-negative case was identified within the 52 positive cases. However, 39 positive detections turned out to be false positives. The diagnostic performance was calculated as a sensitivity of 98.1%, specificity of 89.7%, positive predictive value of 56.7%, and negative predictive value (NPV) of 99.7%. The execution of scanner-integrated AI detection of brain hemorrhages is feasible and robust. The diagnostic accuracy has a high specificity and a very high negative predictive value and sensitivity. However, many false-positive findings resulted in a relatively moderate positive predictive value.

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“…Therefore, IT and software development may continue to play a supporting role in further technological development at TMC and the challenge of dealing with an increasing image load. AI can further enhance QA through the reduction of false negative findings [ 40 ] and support in handling growing image volumes through an efficiency increase in the reporting process.…”

Section: Discussionmentioning

confidence: 99%

Quality Assurance of a Cross-Border and Sub-Specialized Teleradiology Service

Hetenyi¹,

Goelz

Boehmcker³

et al. 2022

Healthcare

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Background: The current literature discusses aspects of quality assurance (QA) and sub-specialization. However, the challenges of these topics in a teleradiology network have been less explored. In a project report, we aimed to review the development and enforcement of sub-specialized radiology at Telemedicine Clinic (TMC), one of the largest teleradiology providers in Europe, and to describe each step of its QA. Evaluation: The company-specific background was provided by the co-authors—current and former staff members of TMC. Detailed descriptions of the structures of sub-specialization and QA at TMC are provided. Exemplary quantitative evaluation of caseloads and disagreement rates of secondary reviews are illustrated. Description of Sub-specialization and Quality Assurance at TMC: Sub-specialization at TMC is divided into musculoskeletal radiology, neuroradiology, head and neck, a body, and an emergency section operating at local daytime in Europe and Australia. Quality assurance is based on a strict selection process of radiologists, specific reporting guidelines, feedback through the secondary reading of 100% of all radiology reports for new starters, and a minimum of 5% of radiology reports on a continuous basis for all other radiologists, knowledge sharing activities and ongoing training. The level of sub-specialization of each radiologist is monitored continuously on an individual basis in detail. After prospective secondary readings, the mean disagreement rate at TMC indicating at least possibly clinically relevant findings was 4% in 2021. Conclusion: With continuing and current developments in radiology in mind, the essential features of sub-specialization and innovative QA are relevant for further expansion of teleradiology services and for most radiology departments worldwide to respond to the increasing demand for value-based radiology.

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Deep learning algorithm in detecting intracranial hemorrhages on emergency computed tomographies

Cited by 28 publications

References 45 publications

A comparison of performance between a deep learning model with residents for localization and classification of intracranial hemorrhage

A comparison of performance between a deep learning model with residents for localization and classification of intracranial hemorrhage

Diagnostic Accuracy and Performance Analysis of a Scanner-Integrated Artificial Intelligence Model for the Detection of Intracranial Hemorrhages in a Traumatology Emergency Department

Quality Assurance of a Cross-Border and Sub-Specialized Teleradiology Service

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