Is Dental Panoramic Radiography Reliable to Assess Position of Bicortical Fixation Screws in Mandibular Fractures? Case Report

Background: Panoramic radiography is widely used as a diagnostic tool before oral surgeries and can be considered the foremost follow-up image after. It provides a broad observation of the maxillomandibular complex at a lower cost and radiation dose. But cone-beam computed tomography (CBCT) examination, after panoramic radiograph evaluation, can produce a change in the diagnostic thinking of maxillofacial surgeons leading to alterations in treatment plans. Objective: We aim to report a case with incidentally discovered radiographic findings where diagnostic changes were caused by switching from panoramic to CBCT imaging. Conclusion: Radiographic assessment of the position and angulation of screws by panoramic x-ray should be approached with extreme caution. The image of choice is CBCT if nerve injury is suspected.

It is not always COVID-19: Case report about an undiagnosed HIV man with dyspnea

Background: The current COVID-19 pandemic has attracted great attention from the medical world. In the past year, there have been reports of missed or delayed treatments for conditions that mimic COVID-19. The main symptoms caused by SARS-CoV-2, such as fever and cough, belong to different clinical conditions. It is of the utmost importance that the diagnostic thinking used to analyze data and information to reach a COVID-19 diagnosis does not overlook the plethora of different diagnoses related to these symptoms. Case report: The aim of this work is to present the clinical case of a patient having unrecognized HIV infection with a 4-week history of fever, cough, and hypoxia. When tests were allowed to highlight HIV-related immunodeficiency status, a CMV assay was performed in order to evaluate opportunistic pneumonia. Through this, diagnosis of HIV combined with CMV pneumonia was made, thus excluding COVID-19 respiratory insufficiency. Conclusion: The diagnosis of the two conditions in the COVID-19 era is challenging due to overlapping clinical and radiological features and limitations of current diagnostic assays. This causes clinical implications due to diagnostic delays.

Assessment of Clinical Reasoning and Diagnostic Thinking Among Dental Students

Aim and background: Diagnostic thinking is the ultimate goal of educational system and the basis for clinical reasoning. The aim of this study was to assess the clinical reasoning and diagnostic thinking ability of dental students by key features test and Diagnostic thinking inventory (DTI) questionnaire. Materials and methods The present study was a descriptive cross-sectional study. The participants consisted of 61 senior dental students. Clinical reasoning and diagnostic thinking were assessed by key feature test and DTI questionnaire, respectively. To design the KF test questions, the blueprint of exam was first designed in expert panel based on dental curriculum. The questions developed based on common cases in oral and maxillofacial diseases by the group of oral and maxillofacial specialists. The DTI was developed by Bourdieu et al. in France and consists of 41 questions on a 6-point Likert scale, of which 21 are memory structure category and 20 are in flexibility in thinking category. Satisfaction of student assessed through a 10-item questionnaire. Data were analyzed using SPSS 19 by descriptive tests (mean, SD, percentage) and student independent T-test and Pearson test. Significance level was determined p < 0.05. Results The mean scores of the key features test of students were 56.55 ± 7.80. No significant difference was reported between clinical reasoning scores of key features test by students' gender (p-value = 0.19). There was no significant difference between the scores of diagnostic thinking between men and women (p-value = 0.11). The difference in students' scores in the domain of flexibility in thinking was significantly higher among male students than female students. (P-value = 0.04). There was no significant correlation between students' diagnostic thinking scores and their clinical reasoning scores in the key features test. Conclusion Based on the present results, the clinical reasoning and diagnostic thinking skills of participants were reported in the low level. This issue emphasizes the need for training to enhance diagnostic thinking and clinical reasoning in dental education. Formative evaluation and reform the educational programs of this course should be considered.

Effect of Point Spread Function Deconvolution in Reconstruction of Brain 18F-FDG PET Images on the Diagnostic Thinking Efficacy in Alzheimer's Disease

Purpose: This study aims to determine the effect of applying Point Spread Function (PSF) deconvolution, which is known to improve contrast and spatial resolution in brain 18F-FDG PET images, to the diagnostic thinking efficacy in Alzheimer's disease (AD).Methods: We compared Hoffman 3-D brain phantom images reconstructed with or without PSF. The effect of PSF deconvolution on AD diagnostic clinical performance was determined from digital brain 18F-FDG PET images of AD (n = 38) and healthy (n = 35) subjects compared to controls (n = 36). Performances were assessed with SPM at the group level (p &lt; 0.001 for the voxel) and at the individual level by visual interpretation of SPM T-maps (p &lt; 0.005 for the voxel) by the consensual analysis of three experienced raters.Results: A mix of large hypometabolic (1,483cm3, mean value of −867 ± 492 Bq/ml) and intense hypermetabolic (902 cm3, mean value of 1,623 ± 1,242 Bq/ml) areas was observed in the PSF compared to the no PSF phantom images. Significant hypometabolic areas were observed in the AD group compared to the controls, for reconstructions with and without PSF (respectively 23.7 and 26.2 cm3), whereas no significant hypometabolic areas were observed when comparing the group of healthy subjects to the control group. At the individual level, no significant differences in diagnostic performances for discriminating AD were observed visually (sensitivity of 89 and 92% for reconstructions with and without PSF respectively, similar specificity of 74%).Conclusion: Diagnostic thinking efficacy performances for diagnosing AD are similar for 18F-FDG PET images reconstructed with or without PSF.

Artificial intelligence in radiology: 100 commercially available products and their scientific evidence

Objectives Map the current landscape of commercially available artificial intelligence (AI) software for radiology and review the availability of their scientific evidence. Methods We created an online overview of CE-marked AI software products for clinical radiology based on vendor-supplied product specifications (www.aiforradiology.com). Characteristics such as modality, subspeciality, main task, regulatory information, deployment, and pricing model were retrieved. We conducted an extensive literature search on the available scientific evidence of these products. Articles were classified according to a hierarchical model of efficacy. Results The overview included 100 CE-marked AI products from 54 different vendors. For 64/100 products, there was no peer-reviewed evidence of its efficacy. We observed a large heterogeneity in deployment methods, pricing models, and regulatory classes. The evidence of the remaining 36/100 products comprised 237 papers that predominantly (65%) focused on diagnostic accuracy (efficacy level 2). From the 100 products, 18 had evidence that regarded level 3 or higher, validating the (potential) impact on diagnostic thinking, patient outcome, or costs. Half of the available evidence (116/237) were independent and not (co-)funded or (co-)authored by the vendor. Conclusions Even though the commercial supply of AI software in radiology already holds 100 CE-marked products, we conclude that the sector is still in its infancy. For 64/100 products, peer-reviewed evidence on its efficacy is lacking. Only 18/100 AI products have demonstrated (potential) clinical impact. Key Points • Artificial intelligence in radiology is still in its infancy even though already 100 CE-marked AI products are commercially available. • Only 36 out of 100 products have peer-reviewed evidence of which most studies demonstrate lower levels of efficacy. • There is a wide variety in deployment strategies, pricing models, and CE marking class of AI products for radiology.

A hierarchical deep learning approach with transparency and interpretability based on small samples for glaucoma diagnosis

The application of deep learning algorithms for medical diagnosis in the real world faces challenges with transparency and interpretability. The labeling of large-scale samples leads to costly investment in developing deep learning algorithms. The application of human prior knowledge is an effective way to solve these problems. Previously, we developed a deep learning system for glaucoma diagnosis based on a large number of samples that had high sensitivity and specificity. However, it is a black box and the specific analytic methods cannot be elucidated. Here, we establish a hierarchical deep learning system based on a small number of samples that comprehensively simulates the diagnostic thinking of human experts. This system can extract the anatomical characteristics of the fundus images, including the optic disc, optic cup, and appearance of the retinal nerve fiber layer to realize automatic diagnosis of glaucoma. In addition, this system is transparent and interpretable, and the intermediate process of prediction can be visualized. Applying this system to three validation datasets of fundus images, we demonstrate performance comparable to that of human experts in diagnosing glaucoma. Moreover, it markedly improves the diagnostic accuracy of ophthalmologists. This system may expedite the screening and diagnosis of glaucoma, resulting in improved clinical outcomes.

Diagnostic Reasoning by Master Clinicians: What Distinguishes Them From Their Peers?

Introduction: Master clinicians are a group of physicians recognized in large part for their superior diagnostic reasoning abilities. However, their reasoning skills have not been rigorously and quantitatively compared to other clinicians using a validated instrument.Methods: We surveyed Internal Medicine physicians at the University of Iowa to identify the master clinicians. These master clinicians were administered the Diagnostic Thinking Inventory, along with an equivalent number of their peers in the general population of internists. Scores were tabulated for structure and thinking, as well as four previously identified elements of diagnostic reasoning (data acquisition, problem representation, hypothesis generation, and illness script search and selection). The 2-sample t-test was used to compare scores between the two groups.Results: 17 master clinicians were identified, of whom 17 (100%) completed the inventory. 19 out of 25 randomly-selected internists also completed the inventory (76%). Mean total scores were 187.2 and 175.8 for the Master Clinician (MC) and the Internist (IM) groups respectively. Thinking and structure subscores were 91.5 and 95.71 for MCs, compared to 85.5 and 90.3 for IMs (p-values: 0.0783 and 0.1199, respectively). The mean data acquisition, problem representation, hypothesis generation, and illness script selection subscores for MCs were 4.46, 4.57, 4.71, and 4.46, compared to 4.13, 4.38, 4.45, and 4.13 in the IM group (p-values: 0.2077, 0.4528, 0.095, and 0.029, respectively). Conclusions: Master Clinicians have greater proficiency in searching for and selecting illness scripts compared to their peers. There were no statistically significant differences between the other scores and subscores. These results will help to inform continuing medical education efforts to improve diagnostic reasoning.