Computer-Aided Diagnosis of Children with Cerebral Palsy under Deep Learning Convolutional Neural Network Image Segmentation Model Combined with Three-Dimensional Cranial Magnetic Resonance Imaging

A Lightweight Convolutional Neural Network Based on Dynamic Level-Set Loss Function for Spine MR Image Segmentation

BACKGROUND

Spine MR image segmentation is important foundation for computer-aided diagnostic (CAD) algorithms of spine disorders. Convolutional neural networks segment effectively, but require high computational costs.

PURPOSE

To design a lightweight model based on dynamic level-set loss function for high segmentation performance.

STUDY TYPE

Retrospective.

POPULATION

Four hundred forty-eight subjects (3163 images) from two separate datasets. Dataset-1: 276 subjects/994 images (53.26% female, mean age 49.02 ± 14.09), all for disc degeneration screening, 188 had disc degeneration, 67 had herniated disc. Dataset-2: public dataset with 172 subjects/2169 images, 142 patients with vertebral degeneration, 163 patients with disc degeneration.

FIELD STRENGTH/SEQUENCE

T2 weighted turbo spin echo sequences at 3T.

ASSESSMENT

Dynamic Level-set Net (DLS-Net) was compared with four mainstream (including U-net++) and four lightweight models, and manual label made by five radiologists (vertebrae, discs, spinal fluid) used as segmentation evaluation standard. Five-fold cross-validation are used for all experiments. Based on segmentation, a CAD algorithm of lumbar disc was designed for assessing DLS-Net's practicality, and the text annotation (normal, bulging, or herniated) from medical history data were used as evaluation standard.

STATISTICAL TESTS

All segmentation models were evaluated with DSC, accuracy, precision, and AUC. The pixel numbers of segmented results were compared with manual label using paired t-tests, with P < 0.05 indicating significance. The CAD algorithm was evaluated with accuracy of lumbar disc diagnosis.

RESULTS

With only 1.48% parameters of U-net++, DLS-Net achieved similar accuracy in both datasets (Dataset-1: DSC 0.88 vs. 0.89, AUC 0.94 vs. 0.94; Dataset-2: DSC 0.86 vs. 0.86, AUC 0.93 vs. 0.93). The segmentation results of DLS-Net showed no significant differences with manual labels in pixel numbers for discs (Dataset-1: 1603.30 vs. 1588.77, P = 0.22; Dataset-2: 863.61 vs. 886.4, P = 0.14) and vertebrae (Dataset-1: 3984.28 vs. 3961.94, P = 0.38; Dataset-2: 4806.91 vs. 4732.85, P = 0.21). Based on DLS-Net's segmentation results, the CAD algorithm achieved higher accuracy than using non-cropped MR images (87.47% vs. 61.82%).

DATA CONCLUSION

The proposed DLS-Net has fewer parameters but achieves similar accuracy to U-net++, helps CAD algorithm achieve higher accuracy, which facilitates wider application.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 1.

Strengthening Equitable Access to Care and Support for Children with Cerebral Palsy and Their Caregivers

Cerebral palsy is one of the most prevalent groups of motor disorders affecting children and adults across the world. As increasingly more children with cerebral palsy are living longer into adulthood, it is ever more crucial to ensure access to timely and needed early intervention from the onset of diagnosis, on a continuum, to optimize medical, developmental, socio-emotional, and academic outcomes for these children over time. The American Academy of Pediatrics (AAP), in collaboration with the American Academy of Cerebral Palsy and Developmental Medicine (AACPDM), substantially revised the clinical practice guidelines for cerebral palsy in 2022, after their prior publication of the guidelines in 2006. The revised guidelines account for a range of considerations that are in line with the biopsychosocial, risk and resilience, and family-centered care models as well as promote a more strengths-based approach to care. Furthermore, there is increased emphasis in the guidelines on promoting equitable access to care as part of contributing towards health equity for all children with cerebral palsy. In addition, the 2022 guidelines clearly present recommendations for earlier diagnosis of cerebral palsy, potentially as early as infancy, as the basis for activating access to early intervention services for children that can bolster their neuroplasticity and global development from an earlier age onward. We consolidate the existing literature on caregiver perceptions, beliefs and concerns surrounding earlier diagnosis of cerebral palsy and connect them to the recommendations in the revised guidelines. We also delineate several considerations surrounding education for healthcare providers and caregivers of children in navigating the chronicity of cerebral palsy in both community and healthcare contexts. There is a scant amount of literature on cerebral palsy across traditional and nontraditional sources of media in published studies, which we also review. Lastly, we present a wealth of recommendations for further research and practice that account for the revised 2022 guidelines, caregiver preferences and acceptability of care, and health equity as the bases for strengthening equitable access to care for children with cerebral palsy on a continuum as they transition into adulthood.

A Comprehensive Survey on the Detection, Classification, and Challenges of Neurological Disorders

Neurological disorders (NDs) are becoming more common, posing a concern to pregnant women, parents, healthy infants, and children. Neurological disorders arise in a wide variety of forms, each with its own set of origins, complications, and results. In recent years, the intricacy of brain functionalities has received a better understanding due to neuroimaging modalities, such as magnetic resonance imaging (MRI), magnetoencephalography (MEG), and positron emission tomography (PET), etc. With high-performance computational tools and various machine learning (ML) and deep learning (DL) methods, these modalities have discovered exciting possibilities for identifying and diagnosing neurological disorders. This study follows a computer-aided diagnosis methodology, leading to an overview of pre-processing and feature extraction techniques. The performance of existing ML and DL approaches for detecting NDs is critically reviewed and compared in this article. A comprehensive portion of this study also shows various modalities and disease-specified datasets that detect and records images, signals, and speeches, etc. Limited related works are also summarized on NDs, as this domain has significantly fewer works focused on disease and detection criteria. Some of the standard evaluation metrics are also presented in this study for better result analysis and comparison. This research has also been outlined in a consistent workflow. At the conclusion, a mandatory discussion section has been included to elaborate on open research challenges and directions for future work in this emerging field.