Dataset Information

Diagnosis and classification of pediatric acute appendicitis by artificial intelligence methods: An investigator-independent approach.

ABSTRACT: Acute appendicitis is one of the major causes for emergency surgery in childhood and adolescence. Appendectomy is still the therapy of choice, but conservative strategies are increasingly being studied for uncomplicated inflammation. Diagnosis of acute appendicitis remains challenging, especially due to the frequently unspecific clinical picture. Inflammatory blood markers and imaging methods like ultrasound are limited as they have to be interpreted by experts and still do not offer sufficient diagnostic certainty. This study presents a method for automatic diagnosis of appendicitis as well as the differentiation between complicated and uncomplicated inflammation using values/parameters which are routinely and unbiasedly obtained for each patient with suspected appendicitis. We analyzed full blood counts, c-reactive protein (CRP) and appendiceal diameters in ultrasound investigations corresponding to children and adolescents aged 0-17 years from a hospital based population in Berlin, Germany. A total of 590 patients (473 patients with appendicitis in histopathology and 117 with negative histopathological findings) were analyzed retrospectively with modern algorithms from machine learning (ML) and artificial intelligence (AI). The discovery of informative parameters (biomarker signatures) and training of the classification model were done with a maximum of 35% of the patients. The remaining minimum 65% of patients were used for validation. At clinical relevant cut-off points the accuracy of the biomarker signature for diagnosis of appendicitis was 90% (93% sensitivity, 67% specificity), while the accuracy to correctly identify complicated inflammation was 51% (95% sensitivity, 33% specificity) on validation data. Such a test would be capable to prevent two out of three patients without appendicitis from useless surgery as well as one out of three patients with uncomplicated appendicitis. The presented method has the potential to change today's therapeutic approach for appendicitis and demonstrates the capability of algorithms from AI and ML to significantly improve diagnostics even based on routine diagnostic parameters.

SUBMITTER: Reismann J

PROVIDER: S-EPMC6760759 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Diagnosis and classification of pediatric acute appendicitis by artificial intelligence methods: An investigator-independent approach.

Reismann Josephine J Romualdi Alessandro A Kiss Natalie N Minderjahn Maximiliane I MI Kallarackal Jim J Schad Martina M Reismann Marc M

PloS one 20190925 9

Acute appendicitis is one of the major causes for emergency surgery in childhood and adolescence. Appendectomy is still the therapy of choice, but conservative strategies are increasingly being studied for uncomplicated inflammation. Diagnosis of acute appendicitis remains challenging, especially due to the frequently unspecific clinical picture. Inflammatory blood markers and imaging methods like ultrasound are limited as they have to be interpreted by experts and still do not offer sufficient ...[more]

PMID: 31553729

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Project description:BackgroundView classification is a key step toward building a fully automated system for interpretation of echocardiograms. However, compared with adult echocardiograms, creating a view classification model for pediatric echocardiograms poses additional challenges, such as greater variation in anatomy, structure size, and views. The aim of this study was to develop a computer vision model to autonomously perform view classification on pediatric echocardiographic images.MethodsUsing a training set of 12,067 echocardiographic images from patients aged 0 to 19 years, a convolutional neural network model was trained to identify 27 preselected standard pediatric echocardiographic views which included anatomic sweeps, color Doppler, and Doppler tracings. A validation set of 6,197 images was used for parameter tuning and model selection. A test set of 9,684 images from 100 different patients was then used to evaluate model accuracy. The model was also evaluated on a per study basis using a second test set consisting of 524 echocardiograms from children with leukemia to identify six preselected views pertinent to cardiac dysfunction surveillance.ResultsThe model identified the 27 preselected views with 90.3% accuracy. Accuracy was similar across age groups (89.3% for 0-4 years, 90.8% for 4-9 years, 90.0% for 9-14 years, and 91.2% for 14-19 years; P = .12). Examining the view subtypes, accuracy was 78.3% for the cine one location, 90.5% for sweeps with color Doppler, 82.2% for sweeps without color Doppler, and 91.1% for Doppler tracings. Among the leukemia cohort, the model identified the six preselected views on a per study basis with a positive predictive value of 98.7% to 99.2% and sensitivity of 76.9% to 94.8%.ConclusionsA convolutional neural network model was constructed for view classification of pediatric echocardiograms that was accurate across the spectrum of ages and view types. This work lays the foundation for automated quantitative analysis and diagnostic support to promote efficient, accurate, and scalable analysis of pediatric echocardiograms.

Project description:Purpose:Next-generation sequencing technologies are actively applied in clinical oncology. Bioinformatics pipeline analysis is an integral part of this process; however, humans cannot yet realize the full potential of the highly complex pipeline output. As a result, the decision to include a variant in the final report during routine clinical sign-out remains challenging. Methods:We used an artificial intelligence approach to capture the collective clinical sign-out experience of six board-certified molecular pathologists to build and validate a decision support tool for variant reporting. We extracted all reviewed and reported variants from our clinical database and tested several machine learning models. We used 10-fold cross-validation for our variant call prediction model, which derives a contiguous prediction score from 0 to 1 (no to yes) for clinical reporting. Results:For each of the 19,594 initial training variants, our pipeline generates approximately 500 features, which results in a matrix of > 9 million data points. From a comparison of naive Bayes, decision trees, random forests, and logistic regression models, we selected models that allow human interpretability of the prediction score. The logistic regression model demonstrated 1% false negativity and 2% false positivity. The final models' Youden indices were 0.87 and 0.77 for screening and confirmatory cutoffs, respectively. Retraining on a new assay and performance assessment in 16,123 independent variants validated our approach (Youden index, 0.93). We also derived individual pathologist-centric models (virtual consensus conference function), and a visual drill-down functionality allows assessment of how underlying features contributed to a particular score or decision branch for clinical implementation. Conclusion:Our decision support tool for variant reporting is a practically relevant artificial intelligence approach to harness the next-generation sequencing bioinformatics pipeline output when the complexity of data interpretation exceeds human capabilities.

Project description:ImportanceDelayed diagnosis of appendicitis is associated with worse outcomes than timely diagnosis, but clinical features associated with diagnostic delay are uncertain, and the extent to which delays are preventable is unclear.ObjectiveTo determine clinical features associated with delayed diagnosis of pediatric appendicitis, assess the frequency of preventable delay, and compare delay outcomes.Design, setting, and participantsThis case-control study included 748 children treated at 5 pediatric emergency departments in the US between January 1, 2010, and December 31, 2019. Participants were younger than 21 years and had a diagnosis of appendicitis.ExposuresIndividual features of appendicitis and pretest likelihood of appendicitis were measured by the Pediatric Appendicitis Risk Calculator (pARC).Main outcomes and measuresCase patients had a delayed diagnosis of appendicitis, defined as 2 emergency department visits leading to diagnosis and a case review showing the patient likely had appendicitis at the first visit. Control patients had a single emergency department visit yielding a diagnosis. Clinical features and pARC scores were compared by case-control status. Preventability of delay was assessed as unlikely, possible, or likely. The proportion of children with indicated imaging based on an evidence-based cost-effectiveness threshold was determined. Outcomes of delayed diagnosis were compared by case-control status, including hospital length of stay, perforation, and multiple surgical procedures.ResultsA total of 748 children (mean [SD] age, 10.2 [4.3] years; 392 boys [52.4%]; 427 White children [57.1%]) were included in the study; 471 (63.0%) had a delayed diagnosis of appendicitis, and 277 (37.0%) had no delay in diagnosis. Children with a delayed diagnosis were less likely to have pain with walking (adjusted odds ratio [aOR], 0.16; 95% CI, 0.10-0.25), maximal pain in the right lower quadrant (aOR, 0.12; 95% CI, 0.07-0.19), and abdominal guarding (aOR, 0.33; 95% CI, 0.21-0.51), and were more likely to have a complex chronic condition (aOR, 2.34; 95% CI, 1.05-5.23). The pretest likelihood of appendicitis was 39% to 52% lower in children with a delayed vs timely diagnosis. Among children with a delayed diagnosis, 109 cases (23.1%) were likely to be preventable, and 247 (52.4%) were possibly preventable. Indicated imaging was performed in 104 (22.0%) to 289 (61.3%) children with delayed diagnosis, depending on the imputation method for missing data on white blood cell count. Patients with delayed diagnosis had longer hospital length of stay (mean difference between the groups, 2.8 days; 95% CI, 2.3-3.4 days) and higher perforation rates (OR, 7.8; 95% CI, 5.5-11.3) and were more likely to undergo 2 or more surgical procedures (OR, 8.0; 95% CI, 2.0-70.4).Conclusions and relevanceIn this case-control study, delayed appendicitis was associated with initially milder symptoms but worse outcomes. These findings suggest that a majority of delayed diagnoses were at least possibly preventable and that many of these patients did not undergo indicated imaging, suggesting an opportunity to prevent delayed diagnosis of appendicitis in some children.

Dataset Information

Diagnosis and classification of pediatric acute appendicitis by artificial intelligence methods: An investigator-independent approach.

Publications

Diagnosis and classification of pediatric acute appendicitis by artificial intelligence methods: An investigator-independent approach.

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