AREAS COVERED: This review will highlight dengue diagnostics strategies and discuss other possible targets for dengue diagnosis. Understanding the dynamics of the immune response and how it affects viral infection has enabled informed diagnosis. As more technologies emerge, precise assays that include some clinical markers need to be included.
EXPERT OPINION: Future diagnostic strategies will require the use both viral and clinical markers in a serial manner with the use of artificial intelligence technology to determine from the first point of illness to better determine severity status and management. A definitive endpoint is not in the horizon as the disease as well as the virus is constantly evolving and hence many developed assays need to be constantly changing some of their reagents periodically as newer genotypes and probably too serotypes emerge.
METHOD: In the present study, three separate data cohorts containing 1288 breast lesions from three countries (Malaysia, Iran, and Turkey) were utilized for MLmodel development and external validation. The model was trained on ultrasound images of 725 breast lesions, and validation was done separately on the remaining data. An expert radiologist and a radiology resident classified the lesions based on the BI-RADS lexicon. Thirteen morphometric features were selected from a contour of the lesion and underwent a three-step feature selection process. Five features were chosen to be fed into the model separately and combined with the imaging signs mentioned in the BI-RADS reference guide. A support vector classifier was trained and optimized.
RESULTS: The diagnostic profile of the model with various input data was compared to the expert radiologist and radiology resident. The agreement of each approach with histopathologic specimens was also determined. Based on BI-RADS and morphometric features, the model achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.885, which is higher than the expert radiologist and radiology resident performances with AUC of 0.814 and 0.632, respectively in all cohorts. DeLong's test also showed that the AUC of the ML protocol was significantly different from that of the expert radiologist (ΔAUCs = 0.071, 95%CI: (0.056, 0.086), P = 0.005).
CONCLUSIONS: These results support the possible role of morphometric features in enhancing the already well-excepted classification schemes.