Machine learning-based medical imaging diagnosis in patients with temporomandibular disorders: a diagnostic test accuracy systematic review and meta-analysis

OBJECTIVES

Temporomandibular disorders (TMDs) are the second most common musculoskeletal condition which are challenging tasks for most clinicians. Recent research used machine learning (ML) algorithms to diagnose TMDs intelligently. This study aimed to systematically evaluate the quality of these studies and assess the diagnostic accuracy of existing models.

MATERIALS AND METHODS

Twelve databases (Europe PMC, Embase, etc.) and two registers were searched for published and unpublished studies using ML algorithms on medical images. Two reviewers extracted the characteristics of studies and assessed the methodological quality using the QUADAS-2 tool independently.

RESULTS

A total of 28 studies (29 reports) were included: one was at unclear risk of bias and the others were at high risk. Thus the certainty of evidence was quite low. These studies used many types of algorithms including 8 machine learning models (logistic regression, support vector machine, random forest, etc.) and 15 deep learning models (Resnet152, Yolo v5, Inception V3, etc.). The diagnostic accuracy of a few models was relatively satisfactory. The pooled sensitivity and specificity were 0.745 (0.660-0.814) and 0.770 (0.700-0.828) in random forest, 0.765 (0.686-0.829) and 0.766 (0.688-0.830) in XGBoost, and 0.781 (0.704-0.843) and 0.781 (0.704-0.843) in LightGBM.

CONCLUSIONS

Most studies had high risks of bias in Patient Selection and Index Test. Some algorithms are relatively satisfactory and might be promising in intelligent diagnosis. Overall, more high-quality studies and more types of algorithms should be conducted in the future.

CLINICAL RELEVANCE

We evaluated the diagnostic accuracy of the existing models and provided clinicians with much advice about the selection of algorithms. This study stated the promising orientation of future research, and we believe it will promote the intelligent diagnosis of TMDs.

USP14 promotes the cancer stem-like cell properties of OSCC via promoting SOX2 deubiquitination

OBJECTIVE

USP14 (Ubiquitin-specific-processing protease 14) is a deubiquitinating enzyme with oncogenic effects in oral squamous cell carcinoma (OSCC). This study aims to identify new substrates of USP14 and elucidate their role in modulating cancer stem-like cells (CSCs) in OSCC.

MATERIALS AND METHODS

Bioinformatics prediction and docking were performed using UbiBrowser 2.0 and HDOCK, respectively. OSCC cell lines and patient-derived cells were used for experimental validation, employing co-immunoprecipitation, cycloheximide chase assays, and tumor sphere formation to evaluate the effects of USP14 on SOX2 stability, ubiquitination, and CSC phenotypes.

RESULTS

USP14 upregulation was associated with worse overall survival and progression-free interval in OSCC. USP14 interacted with SOX2 with its ubiquitin carboxyl-terminal hydrolase domain. USP14 knockdown impaired SOX2 stability by increasing its polyubiquitination. Ectopic overexpression of wild-type USP14, but not the hydrolase-deficient-mutant USP14C114A , enhanced SOX2 stability by reducing polyubiquitination. USP14 knockdown suppressed OSCC cell proliferation, colony formation, and tumor sphere formation in vitro and tumor growth in vivo. However, the reduction of CSC markers following USP14 knockdown was mitigated by overexpressing SOX2. These findings were verified in OSCC patient-derived CSC cells.

CONCLUSION

This study revealed a USP14-SOX2 axis regulating the CSC properties of OSCC.