A Bayesian network for diagnosis of primary bone tumors

Kahn, Charles Η.; Laur, John J.; Carrera, Guillermo F.

doi:10.1007/bf03190296

Cited by 34 publications

(19 citation statements)

References 4 publications

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“…After testing over 20 different algorithms, we selected the Bayesian Network (BayesNet) for further study as it maximized accuracy and transparency. A Bayesian Network uses a graphical representation to represent a compact encoding of the joint distribution of input variables and the outcome(s) of interest 24,25 . Although more complex Bayesian network structures were evaluated, the naïve Bayes models proved as accurate, and more comprehensible, than the complex networks.…”

Section: Methodsmentioning

confidence: 99%

Improving diagnostic recognition of primary hyperparathyroidism with machine learning

Somnay

Craven

McCoy

et al. 2017

Surgery

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Importance Parathyroidectomy offers the only cure for primary hyperparathyroidism (PHPT), but today only 50% of PHPT patients are referred for surgery, in large part because the condition is widely under-recognized. PHPT diagnosis can be especially challenging with mild biochemical indices. Machine learning (ML) is a collection of methods in which computers build predictive algorithms based on labeled examples. Objective With the aim of facilitating diagnosis, we tested the ability of ML to distinguish PHPT from normal physiology using clinical and laboratory data. Design This is a retrospective cohort study using a labeled training set and 10-fold cross-validation to evaluate algorithm accuracy. Measures of accuracy included area under the ROC curve, precision (sensitivity), and positive and negative predictive value. Several different ML algorithms and ensembles of algorithms were tested using the Weka platform. Setting 3 high-volume endocrine surgery programs Participants Among 11,830 patients managed surgically from March, 2001 to August 2013, 6,777 underwent parathyroidectomy for PHPT, and 5,053 control patients without PHPT underwent thyroidectomy. Main Outcomes and Measures Test-set accuracies for ML models were determined using 10-fold cross-validation. Age, gender, preoperative calcium, phosphate, PTH, Vitamin D, and creatinine were defined as potential predictors of PHPT. Mild PHPT was defined as PHPT with normal preoperative calcium or PTH levels. Results After testing a variety of ML algorithms, Bayesian network models proved most accurate, correctly classifying 95.2% of all PHPT patients (area under ROC=0.989). Omitting PTH from the model did not significantly reduce the accuracy (area under ROC = 0.985). However, in mild disease cases, the Bayesian network model correctly classified 71.1% of patients with normal calcium and 92.1% with normal PTH levels preoperatively. Bayesian networking + AdaBoost improved the accuracy to 97.2% correctly classified (area under ROC=0.994) cases, and 91.9% of PHPT patients with mild disease. This was significantly improved relative to Bayesian networking alone (p<0.0001). Conclusions and Relevance ML can accurately diagnose PHPT without human input, even in mild disease. Incorporation of this tool into electronic medical record systems may greatly aid in recognition of this under-diagnosed disorder.

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Section: Methodsmentioning

confidence: 99%

Improving diagnostic recognition of primary hyperparathyroidism with machine learning

Somnay

Craven

McCoy

et al. 2017

Surgery

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“…He developed a computer program that used Bayes' formula to calculate the probability of a bone tumor diagnosis based on digitized demographic characteristics as well as radiographic findings and correctly predicted 77.9% of cases from a data set composed of eight different tumor types. 14 Several additional models for bone tumor diagnosis using Bayes' formula and using patient data and radiographic lesion characteristics were developed including OncOs by Kahn et al 15 and Bone Browser by Lejbkowicz et al 16 More recent work includes a study by Do et al who designed a naive Bayes machine, validated using a leave-one-out cross-validation analysis that calculates the probability of differential diagnoses based on 18 demographic and radiographic features. 17 For the 29 tested diagnoses, the correct pathologic diagnosis was the top machine prediction in 44% of cases and was within the top three machine predictions in 60% of cases.…”

Section: Bone Tumor Diagnosismentioning

confidence: 99%

Pattern Recognition in Musculoskeletal Imaging Using Artificial Intelligence

Gorelik

Chong

Lin

2020

Semin Musculoskelet Radiol

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Artificial intelligence (AI) has the potential to affect every step of the radiology workflow, but the AI application that has received the most press in recent years is image interpretation, with numerous articles describing how AI can help detect and characterize abnormalities as well as monitor disease response. Many AI-based image interpretation tasks for musculoskeletal (MSK) pathologies have been studied, including the diagnosis of bone tumors, detection of osseous metastases, assessment of bone age, identification of fractures, and detection and grading of osteoarthritis. This article explores the applications of AI for image interpretation of MSK pathologies.

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“…28 Based on his work, the researchers who followed developed computed models to aid in diagnosing bone tumors. [29][30][31] However, attempts were limited to 10 bone tumor entities, although the World Health Organization defines > 20. 32 To overcome this, a Bayesian model was developed to predict bone tumor diagnosis and differentials on 710 annotated pathologic radiographs.…”

Section: Automatic Diagnosis Bone Tumor Diagnosismentioning

confidence: 99%

Artificial Intelligence in Musculoskeletal Imaging: Review of Current Literature, Challenges, and Trends

Hirschmann

Cyriac

Stieltjes

et al. 2019

Semin Musculoskelet Radiol

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Artificial intelligence (AI) has gained major attention with a rapid increase in the number of published articles, mostly recently. This review provides a general understanding of how AI can or will be useful to the musculoskeletal radiologist. After a brief technical background on AI, machine learning, and deep learning, we illustrate, through examples from the musculoskeletal literature, potential AI applications in the various steps of the radiologist's workflow, from managing the request to communication of results. The implementation of AI solutions does not go without challenges and limitations. These are also discussed, as well as the trends and perspectives.

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A Bayesian network for diagnosis of primary bone tumors

Cited by 34 publications

References 4 publications

Improving diagnostic recognition of primary hyperparathyroidism with machine learning

Improving diagnostic recognition of primary hyperparathyroidism with machine learning

Pattern Recognition in Musculoskeletal Imaging Using Artificial Intelligence

Artificial Intelligence in Musculoskeletal Imaging: Review of Current Literature, Challenges, and Trends

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