
==== Front
Int J Surg
Int J Surg
JS9
International Journal of Surgery (London, England)
1743-9191
1743-9159
Lippincott Williams & Wilkins Hagerstown, MD

38814316
IJS-D-24-02093
10.1097/JS9.0000000000001718
00130
3
Correspondence
Comment on ‘Deep learning-assisted detection and segmentation of intracranial hemorrhage in noncontrast computed tomography scans of acute stroke patients: a systematic review and meta-analysis’
Jiang Weihua MD a*jiangweihua2023@163.com

Tian Yeqing MD 15372002872@163.com
b
Shen Yenan MD a18058809207@163.com

a Department of Neurology, The First People’s Hospital of Linping District
b Department of Radiology, The First People’s Hospital of Linping District, Hangzhou, Zhejiang, People’s Republic of China
* Corresponding author. Address: Department of Neurology, The First People’s Hospital of Linping District, Hangzhou 311100, Zhejiang, People’s Republic of China. Tel.: +86 176 890 23647. E-mail: jiangweihua2023@163.com (W. Jiang).
9 2024
29 5 2024
110 9 60036004
16 5 2024
19 5 2024
Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0/

OPEN-ACCESSTRUE
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pmc Dear Editor,

Hu et al.1 recently conducted a systematic review and meta-analysis to evaluate the effectiveness of deep learning algorithms in detecting and segmenting intracranial hemorrhage (ICH) on noncontrast computed tomography (NCCT) scans of acute stroke patients. This study highlights the growing potential of artificial intelligence (AI) in enhancing medical imaging diagnostics. The researchers systematically reviewed multiple studies, focusing on the performance metrics of deep learning models. They found that these models generally exhibited high sensitivity and specificity in identifying ICH, demonstrating their robustness across diverse datasets and imaging conditions. The meta-analysis1 further quantified these metrics, providing a comprehensive assessment of the models’ diagnostic accuracy. In addition to detection, the study explored the segmentation capabilities of deep learning algorithms. Accurate segmentation is crucial for determining the volume of hemorrhage, which directly influences clinical decision-making and treatment planning. The results indicated that AI-driven segmentation matched or surpassed the accuracy of manual delineation by radiologists, suggesting that deep learning can significantly reduce the workload on medical professionals while maintaining high diagnostic standards. Although the findings of this study are novel and interesting, we note the following concerns that warrant further clarification.

Firstly, we noticed an important issue in this study1. In the abstract, the authors1 stated that deep learning technologies take less processing time than manual labeling [weight mean difference (WMD)=2.26, 95% CI: 1.96–2.56, P=0.001]. However, the description for Figure 4B1 mentioned that there is no significant difference in processing time between manual labeling and the deep learning model, which is obviously contradictory to the description in the abstract. Moreover, the data from the included studies (as illustrated in Figure 4B of this study1) show that the processing time of deep learning is significantly less than that of manual labeling, yet the study found that deep learning requires more processing time (WMD=2.26, 95% CI: 1.96–2.56, P=0.001), which contradicts the included data. Furthermore, we noticed that the data for ‘Wang T 2023 (a)’ and ‘Wang T 2023 (b)’ in Figure 4B are both from the study by Wang T et al.2. It is important to emphasize that meta-analysis aims to pool results from different studies, not multiple data from the same study. Therefore, including different data from the same study in a meta-analysis is inappropriate. The inclusion of multiple data from the same study in a meta-analysis further undermines the validity of the meta-analytic conclusions, as it introduces redundancy and potential bias, skewing the overall results.

Secondly, it is important to note that there is significant heterogeneity among the populations in the included studies. For example, in the study by Jiang et al.3, the exclusion criteria specified the exclusion of patients with traumatic brain injury. However, the studies by Farzaneh et al.4 and Phaphuangwittayakul et al.5 clearly stated that they included patients with traumatic brain injury. This heterogeneity in the included populations significantly increases the potential risk of bias in this meta-analysis1. When a meta-analysis includes different types of patients without accounting for these differences, the results can be misleading. For instance, the pathophysiology and treatment responses of patients with traumatic brain injury can differ markedly from those without such injuries. Therefore, combining these populations without stratification can result in conclusions that are not truly representative of either group. Thus, it remains unclear whether the conclusions of this study apply specifically to patients with traumatic brain injury or those without. From our perspective, including only a homogeneous group of patients, such as exclusively those with traumatic brain injury or those without, can provide more precise and applicable results. Alternatively, conducting subgroup analyses allows for the examination of potential differences in outcomes between these distinct populations, thereby offering more nuanced and actionable insights. Careful consideration of study heterogeneity and appropriate methodological adjustments are essential for advancing the reliability and applicability of scientific research. This includes transparent reporting of inclusion criteria, detailed descriptions of patient characteristics, and thoughtful analysis strategies to address heterogeneity and potential biases. Only in this way can researchers enhance the validity and impact of their findings in informing clinical decision-making and advancing scientific knowledge.

Ethical approval

Not applicable.

Source of funding

Not applicable.

Conflicts of interest disclosure

There are no conflicts of interest.

Author contribution

W.J., Y.T., and Y.S.: collaborated to create and write this letter.

Research registration unique identifying number (UIN)

Not applicable.

Guarantor

Weihua Jiang.

Data availability statement

This letter contains no data.

Provenance and peer review

Commentary, internally reviewed.

Acknowledgements

Not applicable.

Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.

Published online 29 May 2024
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References

1 Hu P Yan T Xiao B . Deep learning-assisted detection and segmentation of intracranial hemorrhage in noncontrast computed tomography scans of acute stroke patients: a systematic review and meta-analysis. Int J Surg 2024. doi:10.1097/JS9.0000000000001266. [Epub ahead of print].
2 Wang T Song N Liu L . Efficiency of a deep learning-based artificial intelligence diagnostic system in spontaneous intracerebral hemorrhage volume measurement. BMC Med Imaging 2021;21 :125.34388981
3 Jiang X Wang S Zheng Q . Deep-learning measurement of intracerebral haemorrhage with mixed precision training: a coarse-to-fine study. Clin Radiol 2023;78 :e328–e335.36746725
4 Farzaneh N Williamson CA Jiang C . Automated segmentation and severity analysis of subdural hematoma for patients with traumatic brain injuries. Diagnostics (Basel) 2020;10 :773.33007929
5 Phaphuangwittayakul A Guo Y Ying F . An optimal deep learning framework for multi-type hemorrhagic lesions detection and quantification in head CT images for traumatic brain injury. Appl Intell (Dordr) 2022;52 :7320–7338.34764620
