Reference For Diagnosis Research Articles

Changes in Radiologists' Gaze Patterns Against Lung X-rays with Different Abnormalities: a Randomized Experiment.

The workload of some radiologists increased dramatically in the last several, which resulted in a potentially reduced quality of diagnosis. It was demonstrated that diagnostic accuracy of radiologists significantly reduces at the end of work shifts. The study aims to investigate how radiologists cover chest X-rays with their gaze in the presence of different chest abnormalities and high workload. We designed a randomized experiment to quantitatively assess how radiologists' image reading patterns change with the radiological workload. Four radiologists read chest X-rays on a radiological workstation equipped with an eye-tracker. The lung fields on the X-rays were automatically segmented with U-Net neural network allowing to measure the lung coverage with radiologists' gaze. The images were randomly split so that each image was shown at a different time to a different radiologist. Regression models were fit to the gaze data to calculate the treads in lung coverage for individual radiologists and chest abnormalities. For the study, a database of 400 chest X-rays with reference diagnoses was assembled. The average lung coverage with gaze ranged from 55 to 65% per radiologist. For every 100 X-rays read, the lung coverage reduced from 1.3 to 7.6% for the different radiologists. The coverage reduction trends were consistent for all abnormalities ranging from 3.4% per 100 X-rays for cardiomegaly to 4.1% per 100 X-rays for atelectasis. The more image radiologists read, the smaller part of the lung fields they cover with the gaze. This pattern is very stable for all abnormality types and is not affected by the exact order the abnormalities are viewed by radiologists. The proposed randomized experiment captured and quantified consistent changes in X-ray reading for different lung abnormalities that occur due to high workload.

Evaluation of IOTA-ADNEX Model and Simple Rules for Identifying Adnexal Masses by Operators with Varying Levels of Expertise: A Single-Center Diagnostic Accuracy Study.

Objectives The discrimination of ovarian lesions presents a significant problem in everyday clinical practice with ultrasonography appearing to be the most effective diagnostic technique. The aim of our study was to externally evaluate the performance of different diagnostic models when applied by examiners with various levels of experience. Methods This was a diagnostic accuracy study including women who were admitted for adnexal masses, between July 2018 and April 2021, to a Greek tertiary oncology center. Preoperatively sonographic data were evaluated by an expert gynecologist, a 6 th and a 1 st year gynecology resident, who applied the International Ovarian Tumor Analysis (IOTA) Simple Rules (SR) and Assessment of Different NEoplasias in the adneXa (ADNEX) model to discriminate between benign and malignant ovarian tumors. The explant pathology report was used as the reference diagnosis. Kappa statistics were used for the investigation of the level of agreement between the examined systems and the raters. Results We included 66 women, 39 with benign and 27 with malignant ovarian tumors. ADNEX (with and without "CA-125") had high sensitivity (96-100%) when applied by all raters but a rather low specificity (36%) when applied by the 1st year resident. SR could not be applied in 6% to 17% of the cases. It had slightly lower sensitivity, higher specificity, and higher overall accuracy, especially when applied by the 1st year resident (61% vs. 92%), compared to ADNEX. Conclusion Both ADNEX and SR can be utilized for screening in non-oncology centers since they offer high sensitivity even when used by less experienced examiners. In the hands of inexperienced examiners, SR appears to be the best model for assessing ovarian lesions.

BestCyte® primary screening of 500 ThinPrep Pap Test thin-layers: 3 Cytologists’ Interobserver diagnostic concordance with predicate manual microscopy relative to Truth Reference diagnoses defining NILM, ASCUS+, LSIL+, and ASCH+ thresholds for specificity, sensitivity, and equivalency grading

BackgroundThe BestCyte® Cell Sorter Imaging System (BestCyte) facilitates algorithmic discrimination of clinically relevant cells in Pap test cytopathology by classifying and projecting images of cells in galleries based on cytomorphology. Warranted is awareness of potential BestCyte advantages as measured through 3 cytologists’ interobserver diagnostic concordance, specificity and sensitivity differentials, and equivalency grading relative to manual microscopy (MM). ObjectivesUsing 500 MM-reported ThinPrep thin-layers, analyze: (1) cytologists’ blinded BestCyte screening to raise Bethesda diagnoses; (2) correlate BestCyte and MM diagnoses (i.e., predicate) to establish Truth Reference Diagnoses (TRDx) from concordance between 4 possible diagnoses; (3) analyze cytologists’ and MM predicate diagnoses through 4 diagnostic thresholds defined by TRDx: NILM (Negative) for specificity, and ASCUS+, LSIL+, and ASCH+ (Positive) for graded sensitivity (with abnormal cells decreasing in size with increasing dysplasia); and, (4) statistically determine cytologists’ equivalency grading to MM using 95% Confidence Interval (CI) ranges. Results500 TRDx breakdown (n/%): NILM (241/48.2), ASCUS (79/15.8), ASCH (9/1.80), AGUS (2/0.40), LSIL (86/17.2), HSIL (68/13.6), CA (2/0.40), UNSAT (13/2.60). TRDx breakdown (n/%) per 4 of 4, 3 of 4, 2 of 4 diagnostic concordances: 264 (52.8%), 182 (36.4%), 54 (10.8%), respectively. No cases of discordant diagnoses were recorded. HSIL TRDx were established from 66.2% of 4 of 4 concordances, followed by NILM (59.3%), LSIL (46.5%), ASCUS (41.8%); antithetically, from 4.40% of 2 of 4 concordances. Specificity for MM predicate (NILM): 67.08%; for Cytologists 1, 2, and 3: 89.71%, 82.30%, 97.53%, respectively. For NILM threshold, cytologists revealed Significantly Superior equivalency to MM. Sensitivity for ASCUS+, LSIL+, and ASCH+ thresholds: MM (91.36%, 86.67%, 74.36%); Cytologist 1 (95.88%, 96.97%, 94.87%); Cytologist 2 (95.47%, 95.76%, 93.59%), Cytologist 3 (94.65%, 95.15%, 98.72%), respectively. Cytologists revealed Significantly Superior equivalency to MM for graded Positive thresholds; with Cytologist 3 for ASCUS+ being: Superior. ConclusionsBestCyte detects and efficiently displays abnormal cells in strategic galleries standardizing objectivity by systematizing mosaics of cell-types for cytologists’ consideration. BestCyte fosters consistent, enhanced cytologists’ sensitivity values for the ASCUS+, LSIL+, and ASCH+ Positive thresholds relative to MM. Also, BestCyte facilitates improved specificity and superior equivalency grading to MM reflecting efficient screening, and reduced labor. Confident interpretations of small dysplastic epithelial cells characteristic of HSIL led to exceptional interobserver diagnostic concordance inferring BestCyte is primed for effective cervical cancer screening practice.

Machine learning-supported interpretation of kidney graft elementary lesions in combination with clinical data

Interpretation of kidney graft biopsies using the Banff classification is still heterogeneous. In this study, extreme gradient boosting classifiers learned from two large training datasets (n=631 and 304 cases) where the "reference diagnoses" were not strictly defined following the Banff rules but from central reading by expert pathologists and further interpreted consensually by experienced transplant nephrologists, in light of the clinical context. In three external validation datasets (n=3744, 589, and 360), the classifiers yielded a mean ROC curve AUC (95%CI) of: 0.97 (0.92-1.00), 0.97 (0.96-0.97), and 0.95 (0.93-0.97) for antibody-mediated rejection (ABMR); 0.94 (0.91-0.96), 0.94 (0.92-0.95), and 0.91 (0.88-0.95) for T cell-mediated rejection; >0.96 (0.90-1.00) with all three for interstitial fibrosis-tubular atrophy. We also developed a classifier to discriminate active and chronic active ABMR with 95% accuracy. In conclusion, we built highly sensitive and specific artificial intelligence classifiers able to interpret kidney graft scoring together with a few clinical data and automatically diagnose rejection, with excellent concordance with the Banff rules and reference diagnoses made by a group of experts. Some discrepancies may point toward possible improvements that could be made to the Banff classification.

Deep learning-based histotype diagnosis of ovarian carcinoma whole-slide pathology images

Ovarian carcinoma has the highest mortality of all female reproductive cancers and current treatment has become histotype-specific. Pathologists diagnose five common histotypes by microscopic examination, however, histotype determination is not straightforward, with only moderate interobserver agreement between general pathologists (Cohen's kappa 0.54–0.67). We hypothesized that machine learning (ML)-based image classification models may be able to recognize ovarian carcinoma histotype sufficiently well that they could aid pathologists in diagnosis. We trained four different artificial intelligence (AI) algorithms based on deep convolutional neural networks to automatically classify hematoxylin and eosin-stained whole slide images. Performance was assessed through cross-validation on the training set (948 slides corresponding to 485 patients), and on an independent test set of 60 patients from another institution. The best-performing model achieved a diagnostic concordance of 81.38% (Cohen's kappa of 0.7378) in our training set, and 80.97% concordance (Cohen's kappa 0.7547) on the external dataset. Eight cases misclassified by ML in the external set were reviewed by two subspecialty pathologists blinded to the diagnoses, molecular and immunophenotype data, and ML-based predictions. Interestingly, in 4 of 8 cases from the external dataset, the expert review pathologists rendered diagnoses, based on blind review of the whole section slides classified by AI, that were in agreement with AI rather than the integrated reference diagnosis. The performance characteristics of our classifiers indicate potential for improved diagnostic performance if used as an adjunct to conventional histopathology.

Microwave-induced thermoacoustic imaging for the early detection of canine intracerebral hemorrhage.

Purpose: This study aimed to investigate the feasibility and validation of microwave-induced thermoacoustic imaging (TAI) for the early detection of canine intracerebral hemorrhage. Methods: A TAI system was used to record the thermoacoustic signal (TAS) of canine intracerebral hemorrhage in the study. First, the difference in TAS between deionized water, fresh ex vivo porcine blood and brain tissue was explored. Second, the canine hemorrhagic stroke model was established, and canine brain ultrasound examination and TAI examination were performed before modeling and at 0.5h, 1h, 2h, 3h, 4h, 4.5h, 5h and 6h after modeling. Finally, pathology and ultrasound were used as the reference diagnoses to verify the accuracy of the thermoacoustic imaging data. Results: The results showed that significant differences were observed in TASs among deionized water, fresh ex vivo porcine blood and brain tissue. The intensity of the thermoacoustic signal of blood was significantly higher than that of ex vivo porcine brain tissue and deionized water. The intracerebral hemorrhage model of five beagles was successfully established. Hematomas presented hyperintensity in TAI. Considering ultrasound and pathology as reference diagnoses, TAI can be used to visualize canine intracerebral hemorrhage at 0.5h, 1h, 2h, 3h, 4h, 4.5h, 5h and 6h after modeling. Conclusion: This is the first experimental study to explore the use of TAI in the detection of intracerebral hemorrhage in large live animals (canine). The results indicated that TAI could detect canine intracerebral hemorrhage in the early stage and has the potential to be a rapid and noninvasive method for the detection of intracerebral hemorrhage in humans.

Deep Learning Model to Assist Pathological Complete Response Diagnosis in the Locally Advanced Rectal Cancer: A Prospective and Multicenter Study

<h3>Purpose/Objective(s)</h3> Previously, through extracting post-neoadjuvant treatment magnetic resonance imaging (MRI) features, we constructed a radiomics based deep learning model to identify pathological complete response (pCR) patients from non-pCR LARC patients. Here, we aim to investigate the power of this deep learning system in a prospective and multicenter clinical study (Clinical Trial. gov No. NCT04278274). <h3>Materials/Methods</h3> In this study, patients who are pathologically diagnosed rectal adenocarcinoma and MRI defined as locally advanced stage (II and III stages) will be enrolled. All patients will be treated with neoadjuvant chemoradiotherapy or chemotherapy, following TME surgery and adjuvant chemotherapy. The images of MRI will be collected after the neoadjuvant treatment. Further, based on the post-neoadjuvant treatment MRI, nine radiologists divided into three different levels will be employed to generate the predicted response ("predicted as pCR" vs. "predicted as non-pCR"). The diagnosis from radiologists was compared with a reference diagnosis from the deep learning system. If two diagnoses did not match, the radiologists could then choose to adhere to their own diagnosis or adopt the diagnosis from the deep learning system as the final diagnosis. Both the initial and final assisted diagnoses were recorded. Finally, the pathologic report of TME surgery specimen ("confirmed pCR" vs. "confirmed non-pCR") will be served as a standard. The prediction accuracy, specificity, sensitivity as well as the area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of identifying the pCR candidates from non-pCR individuals of the radiologists will be calculated. <h3>Results</h3> From February 2020 to July 2021, 205 patients were recruited in the prospective study. The preliminary predictive performance in deep learning system yielded a maximum AUC (0.850), which was higher than that of the radiologists (0.750, <i>p</i> < 0.01). Furthermore, the deep learning system improved the pooled AUC of the radiologists from 0.750 when diagnosing without deep learning system to 0.830 after supporting by deep learning system (<i>p</i> < 0.05). <h3>Conclusion</h3> Promisingly, based on the present prospective, multicenter clinical study, our deep learning prediction system displayed a higher predictive power to identify pCR patient using post-neoadjuvant treatment MRI. Importantly, this deep learning system may function as a diagnosis supporter to radiologists, particularly to the junior physician. Hopefully, the final and detailed result could be shared at 2022 ASTRO meeting.

Comparison of single- and dual-energy CT combined with artificial intelligence for the diagnosis of pulmonary nodules

To explore the efficiency of single- and dual-energy computed tomography (CT) with artificial intelligence (AI) for the diagnosis of pulmonary nodules. In a prospective study, 682 patients undergoing a chest CT examination using a dual-energy system were divided randomly into two groups: single-energy mode (group S, n=341) and dual-energy mode (group D, n=341). CT images were first analysed automatically with the AI pulmonary nodule-detection software. CT features including nodule number, lesion size, and nodule type were then analysed by experienced radiologists to establish a reference diagnosis. Subsequently, the accuracy, sensitivity, false-positive rate, and miss rate of AI were calculated. Additionally, image quality and radiation dose were also compared between the two groups. The contrast-to-noise ratio data suggested that the image quality of group D was superior to that of group S (0.16±0.10 versus 0.00±0.17), and the radiation dose of group D was lower than that of group S (0.32±0.10 versus 0.62±0.11 mSv.cm). Compared to group S, group D exhibited a significantly higher sensitivity and lower accuracy for nodule identification, size classification, and nodule type (all p<0.05, except for 5-10 mm and calcified nodules). Compared with single-energy CT, dual-energy CT may significantly improve the sensitivity of AI for the diagnosis of pulmonary nodules and is practical for the screening of pulmonary nodules in a large population. In addition, dual-energy CT examination demonstrates improved image quality and is associated with reduced exposure to ionising radiation, but its accuracy is poorer.

Flow cytometry lyophilised-reagent tube for quantifying peripheral blood neutrophil myeloperoxidase expression in myelodysplastic syndromes (MPO-MDS-Develop): protocol for a diagnostic accuracy study

IntroductionSuspicion of myelodysplastic syndromes (MDS) is the most common reason for bone marrow aspirate in elderly patients. Peripheral blood neutrophil myeloperoxidase expression quantified by flow cytometric analysis might rule out...

Implementation of a software application in staging and grading of periodontitis cases.

The purpose of this study was to assess the diagnostic accuracy and the inter-rater agreement among general dentists when staging and grading periodontitis cases with the aid of a software application (SA) developed by the Italian Society of Periodontology and Implantology. Ten general dentists were asked to independently assess 25 periodontitis cases using the SA. Accuracy was estimated using quadratic weighted kappa and examiners' percentage of agreement with a reference diagnosis provided by a gold standard examiner. Inter-rater agreement was evaluated using Fleiss kappa statistics. The overall case definition agreed with the reference diagnosis in 53.6% of cases. The agreements for each general dentist's pairwise comparisons against the reference definition were at least substantial in 100% of cases for stage, in 70% of cases for grade and in none of the cases for extent. Fleiss kappa was 0.818, 0.608, and 0.632 for stage, extent, and grade, respectively. The study recognized possible reasons that could lead to decreased accuracy using the SA. Supported by the SA, general dentists have reached substantial inter-rater agreement and highly accurate assignments of stage and grade. However, complete case definitions were correctly diagnosed in slightly over half of the cases.

Reclassification of atypical immature metaplasia of the uterine cervix by combination of nuclear features on hematoxylin and eosin–stained sections without auxiliary immunohistochemistry

Reclassification of endocervical atypical immature metaplasia (AIM) into reactive changes and neoplastic lesion is often challenging. We aimed to accurately reclassify AIM on hematoxylin and eosin (HE)-stained sections without auxiliary immunohistochemistry (IHC). A total of 133 AIM diagnosed by punch biopsy were reclassified by IHC for p16 and Ki-67 into high-grade squamous intraepithelial lesion (HSIL) or negative for intraepithelial lesion or malignancy or low-grade squamous intraepithelial lesion (NILM/LSIL) as a reference. Nuclear features significantly associated with HSIL on HE-stained sections were extracted by multivariate logistic regression analysis. Propensity score (PS) of HSIL was calculated in each case and cut-off was determined by receiver operation characteristic (ROC) curve analysis. As a result, AIM was reclassified into 104 NILM/LSIL and 29 HSIL by IHC. Compared with reference diagnosis, accuracy of pathologists' subjective diagnosis was 54.9% (kappa coefficient, 0.208). Three nuclear features on HE-stained sections, ie, nuclear enlargement with anisokaryosis, nuclear hyperchromasia, and mitosis, were significantly associated with HSIL. The ROC curve analyses revealed that PS and number of nuclear features were significant predictors of HSIL. Diagnostic accuracy of PS-based diagnosis was 76.7% (kappa, 0.447). When AIM with 2 or more of the 3 nuclear features was diagnosed with HSIL, diagnostic accuracy was 77.4% (kappa, 0.448). Nuclear feature-based diagnosis significantly improved diagnostic accuracy on HE-stained sections compared with subjective diagnosis and may be useful when IHC is not available. However, a considerable proportion of AIM would still remain misdiagnosed and IHC for p16 and Ki-67 should be mandatory for accurate reclassification.

Are Pathologists Self-Aware of Their Diagnostic Accuracy? Metacognition and the Diagnostic Process in Pathology.

Metacognition is a cognitive process that involves self-awareness of thinking, understanding, and performance. This study assesses pathologists' metacognition by examining the association between their diagnostic accuracy and self-reported confidence levels while interpreting skin and breast biopsies. We studied 187 pathologists from the Melanoma Pathology Study (M-Path) and 115 pathologists from the Breast Pathology Study (B-Path). We measured pathologists' metacognitive ability by examining the area under the curve (AUC), the area under each pathologist's receiver operating characteristic (ROC) curve summarizing the association between confidence and diagnostic accuracy. We investigated possible relationships between this AUC measure, referred to as metacognitive sensitivity, and pathologist attributes. We also assessed whether higher metacognitive sensitivity affected the association between diagnostic accuracy and a secondary diagnostic action such as requesting a second opinion. We found no significant associations between pathologist clinical attributes and metacognitive AUC. However, we found that pathologists with higher AUC showed a stronger trend to request secondary diagnostic action for inaccurate diagnoses and not for accurate diagnoses compared with pathologists with lower AUC. Pathologists reported confidence in specific diagnostic terms, rather than the broader classes into which the diagnostic terms were later grouped to determine accuracy. In addition, while there is no gold standard for the correct diagnosis to determine the accuracy of pathologists' interpretations, our studies achieved a high-quality reference diagnosis by using the consensus diagnosis of 3 experienced pathologists. Metacognition can affect clinical decisions. If pathologists have self-awareness that their diagnosis may be inaccurate, they can request additional tests or second opinions, providing the opportunity to correct inaccurate diagnoses. Metacognitive sensitivity varied across pathologists, with most showing higher sensitivity than expected by chance.None of the demographic or clinical characteristics we examined was significantly associated with metacognitive sensitivity.Pathologists with higher metacognitive sensitivity were more likely to request additional tests or second opinions for their inaccurate diagnoses.

Artificial Intelligence for the Analysis of Workload-Related Changes in Radiologists’ Gaze Patterns

Around 60-80% of radiological errors are attributed to overlooked abnormalities, the rate of which increases at the end of work shifts. In this study, we run an experiment to investigate if artificial intelligence (AI) can assist in detecting radiologists' gaze patterns that correlate with fatigue. A retrospective database of lung X-ray images with the reference diagnoses was used. The X-ray images were acquired from 400 subjects with a mean age of 49 ± 17, and 61% men. Four practicing radiologists read these images while their eye movements were recorded. The radiologists passed a series of concentration tests at prearranged breaks of the experiment. A U-Net neural network was adapted to annotate lung anatomy on X-rays and calculate coverage and information gain features from the radiologists' eye movements over lung fields. The lung coverage, information gain, and eye tracker-based features were compared with the cumulative work done (CDW) label for each radiologist. The gaze-traveled distance, X-ray coverage, and lung coverage statistically significantly (p < 0.01) deteriorated with cumulative work done (CWD) for three out of four radiologists. The reading time and information gain over lungs statistically significantly deteriorated for all four radiologists. We discovered a novel AI-based metric blending reading time, speed, and organ coverage, which can be used to predict changes in the fatigue-related image reading patterns.

Fault diagnosis and location of independent sub-module of three-phase MMC based on the optimal deep BiD-LSTM networks

Modular multilevel converter (MMC), which is highly modular, easy to be expanded, and has good output voltage waveform, is especially suitable for medium and high voltage power system applications. The MMC bridge arm inductance fault and the independent sub-module fault have the greatest influence on the transmission quality of transmission systems. However, there is few research on fault diagnosis and location for multiple sub-modules(SM) in a single arm at present. A deep bidirectional Long Short Term Memory (Bid-LSTM) network is proposed in this paper, which is used to diagnose and locate the faults of different sub-modules. which can deal well with the prediction problem with sequence dependence. In addition, because the Bid-LSTM model contains many hyperparameters, which seriously affects the prediction performance of BiD-LSTM, an optimal fruit fly optimization (FOA) algorithm based on the composite chaotic mapping of 3-dimensional Logistic-Sine (3d-CCFOA), which searches the optimal value of related hyperparameters to ensure the accuracy of classification. 3d-CCFOA has good chaos characteristics, which enables FOA to avoid falling into local optimal solution effectively and obtains an effective global optimal solution. Then, the MMC simulation system of RT-TAB is used to simulate the open circuit faults of different independent sub-modules of one bridge arm and collect relevant fault data. The analysis process and experimental results adequately indicate that this method can effectively and accurately diagnose and locate the faults of independent sub-modules. Meanwhile, this method also provides a reference diagnosis strategy for multi-bridge arm fault diagnosis and multi-sub-module location.

External Validation and Clinical Applicability of Two Optical Coherence Tomography-Based Risk Calculators for Detecting Glaucoma.

PurposeTo clinically validate the diagnostic ability of two optical coherence tomography (OCT)-based glaucoma diagnostic calculators (GDCs).MethodsWe conducted a retrospective, consecutive sampling of 76 patients with primary open-angle glaucoma, 107 glaucoma suspects, and 67 controls. Demographics, reliable visual field testing, and macular and optic disc OCT were collected. The reference diagnosis was compared against the probability of having glaucoma obtained from two GDCs derived from multivariate logistic regressions using quantitative and qualitative (GDC1) or only quantitative (GDC2) OCT data. The discrimination (area under the curve [AUC]) and calibration (calibration plots) were compared for both calculators and the best OCT parameters.ResultsGDC2 was able to identify 46.9% more suspects and 14.7% more glaucomatous eyes than GDC1. Both GDCs obtained the highest discriminative ability in glaucomatous eyes (GDC1 AUC = 0.949; GDC2 = 0.943 vs inferior peripapillary retinal nerve fiber layer [pRNFL] = 0.931; P = 0.43). The discriminating ability was not as good for glaucoma suspects, but the GDCs were not inferior to pRNFL (GDC 1 AUC = 0.739; GDC2 = 0.730; inferior pRNFL = 0.760; P = 0.54) and GDC2 was still able to correctly identify up to 30.8% more cases than the conventional OCT classification. Calibration showed risk underestimation for both groups and calculators, but it was better in GDC2 and in patients with glaucoma.ConclusionsOCT-based calculators showed an excellent diagnostic performance in glaucomatous eyes. GDC2 was able to identify approximately 30% more cases than the conventional pRNFL inferior OCT classification in both groups, suggesting a potential role of these composite scores in clinical practice.Translational RelevanceThese OCT-based calculators may improve glaucoma diagnosis in clinical care.

Evaluation of a Probability-Based Predictive Tool on Pathologist Agreement Using Urinary Bladder as a Pilot Tissue.

Simple SummaryThere is a common joke in pathology—put three pathologists in a room and you will obtain three different answers. This saying comes from the fact that pathology can be subjective; pathologists’ diagnoses can be influenced by many different biases, and pathologists are also influenced by the presence or absence of animal information and medical history. Compared to pathology, statistics is a much more objective field. This study aimed to develop a probability-based tool using statistics obtained by analyzing 338 histopathology slides of canine and feline urinary bladders, then see if the tool affected agreement between the test pathologists. Four pathologists diagnosed 25 canine and feline bladder slides and they conducted this three times: without animal and clinical information, then with this information, and finally using the probability tool. Results showed large differences in the pathologists’ interpretation of bladder slides, with kappa agreement values (low value for digital slide images, high value for glass slides) of 7–37% without any animal or clinical information, 23–37% with animal signalment and history, and 31–42% when our probability tool was used. This study provides a starting point for the use of probability-based tools in standardizing pathologist agreement in veterinary pathology.Inter-pathologist variation is widely recognized across human and veterinary pathology and is often compounded by missing animal or clinical information on pathology submission forms. Variation in pathologist threshold levels of resident inflammatory cells in the tissue of interest can further decrease inter-pathologist agreement. This study applied a predictive modeling tool to bladder histology slides that were assessed by four pathologists: first without animal and clinical information, then with this information, and finally using the predictive tool. All three assessments were performed twice, using digital whole-slide images (WSI) and then glass slides. Results showed marked variation in pathologists’ interpretation of bladder slides, with kappa agreement values of 7–37% without any animal or clinical information, 23–37% with animal signalment and history, and 31–42% when our predictive tool was applied, for digital WSI and glass slides. The concurrence of test pathologists to the reference diagnosis was 60% overall. This study provides a starting point for the use of predictive modeling in standardizing pathologist agreement in veterinary pathology. It also highlights the importance of high-quality whole-slide imaging to limit the effect of digitization on inter-pathologist agreement and the benefit of continued standardization of tissue assessment in veterinary pathology.

Histopathological diagnosis of cutaneous melanocytic lesions: blinded and nonblinded second opinions offer similar improvement in diagnostic accuracy.

Previous studies of second opinions in the diagnosis of melanocytic skin lesions have examined blinded second opinions, which do not reflect usual clinical practice. The current study, conducted in the USA, investigated both blinded and nonblinded second opinions for their impact on diagnostic accuracy. In total, 100 melanocytic skin biopsy cases, ranging from benign to invasive melanoma, were interpreted by 74 dermatopathologists. Subsequently, 151 dermatopathologists performed nonblinded second and third reviews. We compared the accuracy of single reviewers, second opinions obtained from independent, blinded reviewers and second opinions obtained from sequential, nonblinded reviewers. Accuracy was defined with respect to a consensus reference diagnosis. The mean case-level diagnostic accuracy of single reviewers was 65.3% (95% CI 63.4-67.2%). Second opinions arising from sequential, nonblinded reviewers significantly improved accuracy to 69.9% (95% CI 68.0-71.7%; P < 0.001). Similarly, second opinions arising from blinded reviewers improved upon the accuracy of single reviewers (69.2%; 95% CI 68.0-71.7%). Nonblinded reviewers were more likely than blinded reviewers to give diagnoses in the same diagnostic classes as the first diagnosis. Nonblinded reviewers tended to be more confident when they agreed with previous reviewers, even with inaccurate diagnoses. We found that both blinded and nonblinded second reviewers offered a similar modest improvement in diagnostic accuracy compared with single reviewers. Obtaining second opinions with knowledge of previous reviews tends to generate agreement among reviews, and may generate unwarranted confidence in an inaccurate diagnosis. Combining aspects of both blinded and nonblinded review in practice may leverage the advantages while mitigating the disadvantages of each approach. Specifically, a second pathologist could give an initial diagnosis blinded to the results of the first pathologist, with subsequent nonblinded discussion between the two pathologists if their diagnoses differ.

EASI™ 12‑lead ECG with a handheld computer refines cardiovascular diagnosis in general practice

BackgroundElectrocardiogram (ECG) is used to a small extent in general medicine, because of general practitioner (GP) apprehension about interpretation and time consumption. AimThis study tested the hypothesis that user-friendly EASI ECG improves GP diagnosis of cardiovascular symptoms. MethodPatients over 18 years with recent cardiovascular symptoms or auscultation rhythm abnormalities were included in this prospective, multicentric study (10 practices, 17 GPs). ECG recordings were made with Cardiosecur® (4‑lead ECG connected to a handheld computer for EASI™ processing). Besides clinical data, diagnosis/patient referral were noted before and after ECG and interpretation. GP diagnosis and ECG interpretation were compared with a reference diagnosis made by ECG specialist. ResultsThere were 338 patients; 66% had cardiovascular risk factors. ECGs were performed for chest pain (41%), auscultation rhythm abnormalities (33%) or palpitations (19%). Average time to perform ECG was 4.7 ± 2.1 min, with possible home recordings. Compared with standard ECG, improvement provided by Cardiosecur® was scored 9/10 (range 7–10) by GPs. GPs correctly interpreted ECG normality/abnormality in 77% of patients. Diagnosis was correctly changed for 14% of patients thanks to the ECG, and wrongly changed for 2%. One new appropriate final diagnosis was achieved for 9 ECG recordings (p < 0.001). Diagnostic certainty increased 1.9 ± 2.1/10 (p < 0.001). ECG brought about changes in GP decision making: referral or treatment changed for 82 patients (24%) and complementary test for 69 patients (20%). ConclusionThe EASI™ algorithm coupled with a handheld computer facilitates ECG recordings in the primary care setting, providing improved diagnosis.

LOw-dose CT Or Lung UltraSonography versus standard of care based-strategies for the diagnosis of pneumonia in the elderly: protocol for a multicentre randomised controlled trial (OCTOPLUS)

IntroductionPneumonia is a leading cause of mortality and a common indication for antibiotic in elderly patients. However, its diagnosis is often inaccurate. We aim to compare the diagnostic accuracy, the...

Enostosis-Like Lesion in Thoroughbred Horse

Background: Enostosis like lesions are characterized by areas of intramedullary sclerosis affecting the long bones and their presence in any cases may be not associated with lameness. It has a migratory characteristic and, therefore there is the occurrence of lameness at different sites from the initial lesion. Its etiology is speculative and has been attributed to intraosseous increased pressure, of Havers’ canals compression, stimulation of unmyelinated fibers and circulating platelet aggregates. Diagnosis is made through nuclear scintigraphy and associated with radiographic examination. This paper aims to report a clinical case on the use of scintigraphy for the reference diagnosis of enostosis-type injury and treatment through surgical bone decompression.Case: A 2-year-old thoroughbred mare, weighing 483 kg, with a history of acute lameness of the left pelvic limb associated with the no previous signs of trauma and no noteworthy changes in radiographic and ultrasound images, was referred to the Horse Center Veterinary Clinic. In the examination of the locomotor system, the animal presented a 2/5 degree lameness in a straight line, with accentuated exacerbation of the same after flexion of the left femoro-tibio-patellar joint. In addition, presented a reduction in the caudal phase of the stride and croup asymmetry associated with mild myopenia. The findings of the scintigraphic exam characterized by intense focal area of hyperconcentration of medullary radiopharmaceutical in the proximal third of the right third metatarsal, and multiple areas of hyperconcentration in the aspect proximal to the distal third of the left tibia. In the radiographic images, multifocal radiopaque regions that coincided with the areas of radiopharmaceutical hyperconcentration were observed. The initial treatment was based on rest, use of non-steroidal antiinflammatory drugs and acetylsalicylic acid. In the 60 days’ later evaluation of the first exam, the patient returned to the clinic presenting 4/5 degree lameness and with an unsatisfactory evolution. Therefore, surgical bone decompression was performed on the left radius through intramedullary perforations with a 3.5 mm drill in the lesion sites. Approximately 30 days after the surgical procedure, the animal returned to the clinic complaining of acute 2/5 degree lameness of the left pelvic limb. The patient was removed from his race career and destined for amateur jumping events where he is currently doing the same without presenting a clinical complaint of persistent lameness.Discussion: The presence of focal areas of radiopharmaceutical hyperconcentration in several bones of a limb, not just in the same lame limb, makes it even more difficult to understand this pathology. The intensity of radiopharmaceutical uptake evident in scintigraphy exams is related to the degree of lameness. Severe lameness is associated with intense radiopharmaceutical concentration indicating an acute stage of the disease, as well as a decrease in radiopharmaceutical concentration in follow-up exams, demonstrating an improvement in the degree of lameness. In the present clinical case described, there was a decrease in the radiopharmaceutical concentration in the right radius, but in the left radius, the limb in which spinal cord decompression was performed, it was still possible to observe radiopharmaceutical hyperconcentration. This was possibly due to an inflammatory bone process caused by surgical decompression. The literature suggests a favorable prognosis for the return to athletic function, with clinical resolution after following a period of rest and administration of non-steroidal anti-inflammatory drugs. The patient in the described clinical case returned to sports activities with a reduced athletic performance requirement, replacing running events with basic and amateur jumping events. Keywords: lameness, intramedullary sclerosis, bone, equine.Título: Enostose múltipla em equino puro sangue de corridaDescritores: claudicação, esclerose intramedular, osso, equino.