Diagnostic performance evaluation of adult Chiari malformation type I based on convolutional neural networks

Lin, Weiwei; Liu, Tianjian; Dai, Wei; Wang, Qiang-Wei; Hu, Xinben; Gu, Zhao-Wen; Zhu, Yu

doi:10.1016/j.ejrad.2022.110287

Cited by 4 publications

(3 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The model that combined volumetric MRI data with clinical features including the preoperative PCS result and extent of cerebellar ectopia performed best and had a mean F1 score of 0.74 (SD: 0.10) and a mean ROC AUC of 0.71 (SD: 0.19) on testing. Currently, the CM1 definition is based on tonsillar herniation in isolation and there are multiple studies utilizing AI to build on this definition and either improve the identification of tonsillar herniation or determine new clinically relevant neuroimaging and clinical features [7,[13][14][15]. However, the prediction of the recurrence of symptoms aims to aid in better patient selection to prevent the recurrence of symptoms and potential reoperation.…”

Section: Discussionmentioning

confidence: 99%

Use of Artificial Intelligence in the Prediction of Chiari Malformation Type 1 Recurrence After Posterior Fossa Decompressive Surgery

King,

Liu,

Russo

et al. 2024

Cureus

View full text Add to dashboard Cite

Purpose The purpose of this study was to train a deep learning-based method for the prediction of postoperative recurrence of symptoms in Chiari malformation type 1 (CM1) patients undergoing surgery. Studies suggest that certain radiological and clinical features do exist in patients with treatment failure, though these are inconsistent and poorly defined. Methodology This study was a retrospective cohort study of patients who underwent primary surgical intervention for CM1 from January 2010 to May 2020. Only patients who completed pre- and postoperative 12-item short form (SF-12) surveys were included and these were used to classify the recurrence or persistence of symptoms. Forty patients had an improvement in overall symptoms while 17 had recurrence or persistence. After magnetic resonance imaging (MRI) data augmentation, a ResNet50, pre-trained on the ImageNet dataset, was used for feature extraction, and then clustering-constrained attention multiple instance learning (CLAM), a weakly supervised multi-instance learning framework, was trained for prediction of recurrence. Five-fold cross-validation was used for the development of MRI only, clinical features only, and a combined machine learning model. Results This study included 57 patients who underwent CM1 decompression. The recurrence rate was 30%. The combined model incorporating MRI, pre-operative SF-12 physical component scale (PCS), and extent of cerebellar ectopia performed best with an area under the curve (AUC) of 0.71 and an F1 score of 0.74. Conclusion This is the first study to our knowledge to explore the prediction of postoperative recurrence of symptoms in CM1 patients using machine learning methods and represents the first step toward developing a clinically useful prognostication machine learning model. Further studies utilizing a similar deep learning approach with a larger sample size are needed to improve the performance.

show abstract

Section: Discussionmentioning

confidence: 99%

Use of Artificial Intelligence in the Prediction of Chiari Malformation Type 1 Recurrence After Posterior Fossa Decompressive Surgery

King,

Liu,

Russo

et al. 2024

Cureus

View full text Add to dashboard Cite

show abstract

“…have explored using CNN models on MRI to aid in CM-I diagnosis and decision-making. [ 61 ] Tanaka et al . have used ResNet model pretrained on ImageNet dataset as the substrate of their neural network model and employed transfer learning to train the ResNet to defect CM-I malformation on a small dataset (135 images).…”

Section: Magnetic Resonance Imaging-based Chiari I Prediction Using D...mentioning

confidence: 99%

Is foramen magnum decompression for acquired Chiari I malformation like putting a finger in the dyke? - A simplistic overview of artificial intelligence in assessing critical upstream and downstream etiologies

Manjila,

Alsalama,

Medani

et al. 2024

Journal of Craniovertebral Junction and Spine

View full text Add to dashboard Cite

Background: Missed diagnosis of evolving or coexisting idiopathic (IIH) and spontaneous intracranial hypotension (SIH) is often the reason for persistent or worsening symptoms after foramen magnum decompression for Chiari malformation (CM) I. We explore the role of artificial intelligence (AI)/convolutional neural networks (CNN) in Chiari I malformation in a combinatorial role for the first time in literature, exploring both upstream and downstream magnetic resonance findings as initial screening profilers in CM-1. We have also put together a review of all existing subtypes of CM and discuss the role of upright (gravity-aided) magnetic resonance imaging (MRI) in evaluating equivocal tonsillar descent on a lying-down MRI. We have formulated a workflow algorithm MaChiP 1.0 (Manjila Chiari Protocol 1.0) using upstream and downstream profilers, that cause de novo or worsening Chiari I malformation, which we plan to implement using AI. Materials and Methods: The PRISMA guidelines were used for “CM and machine learning and CNN” on PubMed database articles, and four articles specific to the topic were encountered. The radiologic criteria for IIH and SIH were applied from neurosurgical literature, and they were applied between primary and secondary (acquired) Chiari I malformations. An upstream etiology such as IIH or SIH and an isolated downstream etiology in the spine were characterized using the existing body of literature. We propose the utility of using four selected criteria for IIH and SIH each, over MRI T2 images of the brain and spine, predominantly sagittal sequences in upstream etiology in the brain and multiplanar MRI in spinal lesions. Results: Using MaChiP 1.0 (patent/ copyright pending) concepts, we have proposed the upstream and downstream profilers implicated in progressive Chiari I malformation. The upstream profilers included findings of brain sagging, slope of the third ventricular floor, pontomesencephalic angle, mamillopontine distance, lateral ventricular angle, internal cerebral vein–vein of Galen angle, and displacement of iter, clivus length, tonsillar descent, etc., suggestive of SIH. The IIH features noted in upstream pathologies were posterior flattening of globe of the eye, partial empty sella, optic nerve sheath distortion, and optic nerve tortuosity in MRI. The downstream etiologies involved spinal cerebrospinal fluid (CSF) leak from dural tear, meningeal diverticula, CSF-venous fistulae, etc. Conclusion: AI would help offer predictive analysis along the spectrum of upstream and downstream etiologies, ensuring safety and efficacy in treating secondary (acquired) Chiari I malformation, especially with coexisting IIH and SIH. The MaChiP 1.0 algorithm can help document worsening of a previously diagnosed CM-1 and find the exact etiology of a secondary CM-I. However, the role of posterior fossa morphometry and cine-flow MRI data for intracranial CSF flow dynamics, along with advanced spinal CSF studies using dynamic myelo-CT scanning in the formation of secondary CM-I is still being evaluated.

show abstract

“…The model achieved an accuracy of 82%, demonstrating DL’s powerful capability in identifying complex patterns in medical images that are often imperceptible to the human eye. Since then, a few studies have been published on the segmentation and target detection of intradural lesions [ 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural network-based magnetic resonance image differentiation of filum terminale ependymomas from schwannomas

Gu,

Dai,

Chen

et al. 2024

BMC Cancer

View full text Add to dashboard Cite

Purpose Preoperative diagnosis of filum terminale ependymomas (FTEs) versus schwannomas is difficult but essential for surgical planning and prognostic assessment. With the advancement of deep-learning approaches based on convolutional neural networks (CNNs), the aim of this study was to determine whether CNN-based interpretation of magnetic resonance (MR) images of these two tumours could be achieved. Methods Contrast-enhanced MRI data from 50 patients with primary FTE and 50 schwannomas in the lumbosacral spinal canal were retrospectively collected and used as training and internal validation datasets. The diagnostic accuracy of MRI was determined by consistency with postoperative histopathological examination. T1-weighted (T1-WI), T2-weighted (T2-WI) and contrast-enhanced T1-weighted (CE-T1) MR images of the sagittal plane containing the tumour mass were selected for analysis. For each sequence, patient MRI data were randomly allocated to 5 groups that further underwent fivefold cross-validation to evaluate the diagnostic efficacy of the CNN models. An additional 34 pairs of cases were used as an external test dataset to validate the CNN classifiers. Results After comparing multiple backbone CNN models, we developed a diagnostic system using Inception-v3. In the external test dataset, the per-examination combined sensitivities were 0.78 (0.71–0.84, 95% CI) based on T1-weighted images, 0.79 (0.72–0.84, 95% CI) for T2-weighted images, 0.88 (0.83–0.92, 95% CI) for CE-T1 images, and 0.88 (0.83–0.92, 95% CI) for all weighted images. The combined specificities were 0.72 based on T1-WI (0.66–0.78, 95% CI), 0.84 (0.78–0.89, 95% CI) based on T2-WI, 0.74 (0.67–0.80, 95% CI) for CE-T1, and 0.81 (0.76–0.86, 95% CI) for all weighted images. After all three MRI modalities were merged, the receiver operating characteristic (ROC) curve was calculated, and the area under the curve (AUC) was 0.93, with an accuracy of 0.87. Conclusions CNN based MRI analysis has the potential to accurately differentiate ependymomas from schwannomas in the lumbar segment.

show abstract

Diagnostic performance evaluation of adult Chiari malformation type I based on convolutional neural networks

Cited by 4 publications

References 31 publications

Use of Artificial Intelligence in the Prediction of Chiari Malformation Type 1 Recurrence After Posterior Fossa Decompressive Surgery

Use of Artificial Intelligence in the Prediction of Chiari Malformation Type 1 Recurrence After Posterior Fossa Decompressive Surgery

Is foramen magnum decompression for acquired Chiari I malformation like putting a finger in the dyke? - A simplistic overview of artificial intelligence in assessing critical upstream and downstream etiologies

Convolutional neural network-based magnetic resonance image differentiation of filum terminale ependymomas from schwannomas

Contact Info

Product

Resources

About