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Editorial for “Anatomical Partition‐Based Deep Learning: An Automatic Nasopharyngeal Magnetic Resonance Image Recognition Scheme”

Deep learning is a promising artificial intelligence tool that continues to transform medical imaging, with applications including image acquisition, image transformation, lesion detection and segmentation, and even image interpretation. While fascinating, the clinical translation of deep learning remains limited by the complexity of medical image interpretation, the requirement for large volumes of annotated training datasets, model generalizability, and the challenge of model maintenance as source data and practice patterns change. In this issue of JMRI, Li et al attack the problem of automated disease classification by training convolutional neural networks with images centered on the nasopharynx as a method of integrating specific anatomical information into model training. Using this methodology, they show good model performance, with a mean average area under the curve (AUC) of 0.95 for differentiating six diagnoses, a similar accuracy to that obtained using traditional methodology and full-size images (mean average AUC of 0.94). Furthermore, they show how this method sustains model performance despite reduced data costs, with a mean average AUC of 0.82 when trained with only 25% of the dataset (compared to 0.64 using traditional methods). The concept of automatic segmentation is not a new idea, as deep learning algorithms are actually fairly good at segmenting lesions on medical images. In this study, however, the authors go beyond simply segmenting lesions of interest. Rather, they incorporate the anatomy surrounding the lesions of interest into model training as a way of introducing geographic context for data input. This was accomplished by selecting the geometric center of the predicted segmentation region from the full-size images and extending it 56 or 112 pixels in all directions to generate small field-of-view images centered on the nasopharynx, termed seg112 and seg224 images. Using this method, they essentially trained models to associate specific conditions with the nasopharynx. While only six conditions were classified in this study, the effect of training models with this approach is noteworthy, as performance was no different from traditional methods when trained with the full dataset, and only minimally declined when trained with half the dataset (mean average AUC declined from 0.95 to 0.91 for models trained with the seg112 images using the 100% and 50% datasets). This result is encouraging, as it demonstrates a plausible method for reducing data costs associated with model training. The thought processes guiding this proof-of-concept work are interesting, as they approach the problem of image interpretation in a similar manner to how radiologists actually look at studies. While intrinsic signal characteristics and change over time are undoubtedly important imaging features, specific anatomical location is also of great importance for developing a differential diagnosis. This is especially true in the head and neck, as disease processes are classically described to be centered in specific “spaces.” With this space-based approach, differential diagnoses are constructed by the constituents of each space, with the understanding that most disease processes will be limited by spatial boundaries. This study supports the notion that evaluating an abnormality with regard to its location matters, as model accuracy declined when diagnoses were considered independent of location. This is interesting because convolutional neural networks are fairly good at extracting imaging features for disease classification, although this may be limited in the setting of a single T2-weighted image and the small size of lesions in the nasopharynx. Additionally, it may be challenging to identify the abnormality or appreciate its importance in the absence of clinical history when considering the full image. Thus, it is logical that models trained using segmented images centered on the nasopharynx outperformed traditional full-size images in the classification of nasopharyngeal disease, especially in the setting of reduced data input. While this novel method for training deep learning models aims to improve automated disease classification and address the problem of data costs in medical imaging interpretation, the reality of the results is that this alone is insufficient. Going forward, I am eager to see how this method can be used to combine additional regions of interest (or “spaces”), and then subsequently use these data to generate probabilistic