Diagnostics

Background/Objectives: While previous studies have evaluated AI algorithms for cervical spine fracture (CSFx) detection on CT, many have lacked validation on diverse, multinational datasets or have focused primarily on overall case-level classification This study aimed to evaluate the performance of an AI application for acute CSFx detection in case-level classification, fracture localization, and spinal level labeling on multicenter, multinational, and multivendor CT data. Methods: Non-enhanced CTs were retrospectively collected from a U.S. teleradiology company, a French teleradiology company, and a U.S. university hospital. Four radiologists independently labeled the presence and location (including the spinal level) of acute CSFx to establish the reference standard. Per-case diagnostic performance, per-bounding box positive predictive value (PPV) for localization, and overall agreement of cervical vertebral level labeling of the AI were assessed. Results: A total of 155 patients (60.6 years ± 21.2 years, 104 men) with acute CSFx and 173 patients (51.9 years ± 22.7 years, 91 men) without acute CSFx were evaluated. Data were acquired using scanners from five manufacturers. For acute CSFx diagnosis, the AI achieved a per-case sensitivity of 90.3%, a specificity of 91.9%, an accuracy of 91.2%, an area under the receiver operating characteristic curve (AUC) of 0.91, and Matthews correlation coefficient of 0.82. Among 192 bounding boxes representing acute CSFx generated for 154 positive cases by the AI, 162 were true positives (per-bounding box PPV, 84.4%). Of the 186 bounding boxes for which the AI displayed cervical spinal level, 181 were labeled correctly (overall agreement, 97.3%). Conclusions: The AI application for detecting acute CSFx demonstrated high diagnostic performance on multicenter, multinational, and multivendor data, with high performance in fracture localization and spinal level labeling.

The hospital-at-home (HaH) model delivers hospital-level care to patients in their homes, with point-of-care ultrasonography (PoCUS) serving as a cornerstone diagnostic tool for respiratory illnesses such as pneumonia. This review—the third in a series—addresses the prognostic, synchronous, and potential overdiagnostic concerns of lung ultrasound (LUS) in managing pneumonia within HaH settings. LUS offers advantages of safety and repeatability, allowing clinicians to identify “red flag” sonographic findings that signal complicated or severe disease, including pleural line abnormalities, fluid bronchograms, absent Doppler perfusion, or poor diaphragmatic motion. Serial LUS examinations correlate closely with clinical recovery, showing progressive resolution of consolidations, B-lines, and pleural effusions, and thus provide a non-invasive method for monitoring therapeutic response. Compared with chest radiography, LUS demonstrates superior sensitivity in detecting pneumonia, pleural effusion, and interstitial syndromes across pediatric and adult populations. However, specificity may decline in tuberculosis-endemic or obese populations due to technical limitations and overlapping imaging patterns. Overdiagnosis remains a concern, as highly sensitive ultrasonography may identify minor or clinically irrelevant lesions, potentially leading to overtreatment. To mitigate this, PoCUS should be applied in parallel with conventional diagnostics and integrated into comprehensive clinical assessment. Standardized training, multi-zone scanning protocols, and structured image acquisition are recommended to improve reproducibility and inter-operator consistency.

For decades, cervical insufficiency (CI) has been framed predominantly as a mechanical failure of the cervix resulting in painless mid-trimester dilatation. This disease-centered paradigm, reinforced by clinical teaching and administrative coding, does not fully capture the dynamic and biologically integrated nature of cervical remodeling. Accumulating evidence suggests that cervical change is governed by coordinated mechanical, inflammatory, and immunologic interactions rather than by a purely anatomic defect. To outline a process-oriented conceptual framework that situates CI within the broader preterm-birth continuum, this perspective aims to integrate biomechanical, inflammatory, and immunologic dimensions of cervical remodeling and to emphasize that infection- and inflammation-related changes represent dynamic, potentially modifiable elements that may inform more individualized, biology-guided clinical decision-making. This Perspective traces the evolution from a traditional “disease entity” interpretation of CI toward a more integrated view of cervical remodeling as a dynamic, biology-responsive process. Emerging data suggest that when intra-amniotic infection or inflammation is appropriately managed, cervical competence may be partially restored, and mechanical support can be applied more safely in selected patients. Clinical observations indicate that infection-controlled cerclage is associated with meaningful prolongation of gestation. Earlier reports describing double-level mechanical reinforcement techniques conceptually align with contemporary interpretations of infection-controlled emergent cerclage by linking surgical timing with the underlying biology of cervical change. Rather than proposing a prescriptive management pathway, this framework highlights how mechanical, inflammatory, and immunologic factors may interact across heterogeneous CI etiologies and how individualized intervention may be guided by biologic context. Understanding CI as a dynamic rather than a fixed condition provides a framework that integrates its mechanical, inflammatory, and immunologic dimensions within the preterm birth continuum. Such a perspective encourages individualized, biology-informed interpretation of cervical change and supports more context-specific use of established interventions such as cerclage. By emphasizing developmental processes rather than a static defect, this approach seeks to bridge classical clinical practice with contemporary insights into cervical remodeling.