Artificial neural networks in the diagnosis of acute appendicitis

Yoldaş, Ömer; Tez, Mesut; Karaca, Turgut

doi:10.1016/j.ajem.2011.06.019

Cited by 24 publications

(21 citation statements)

References 9 publications

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“…ANNs are excellent model systems for prediction of postoperative outcomes, and have been used in general surgery and surgical oncology, among other specialties [22, 26–35]. In bariatric surgery, ANN modeling has been used sparingly.…”

Section: Discussionmentioning

confidence: 99%

Prediction of excess weight loss after laparoscopic Roux-en-Y gastric bypass: data from an artificial neural network

2015

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Introduction Laparoscopic Roux-en-Y Gastric Bypass (LRYGB) has become the gold standard for surgical weight loss. The success of LRYGB may be measured by excess body-mass index loss (%EBMIL) over 25 kg/m2, which is partially determined by multiple patient factors. In this study, artificial neural network (ANN) modeling was used to derive a reasonable estimate of expected postoperative weight loss using only known preoperative patient variables. Additionally, ANN modeling allowed for the discriminant prediction of achievement of benchmark 50% EBMIL at one year postoperatively. Methods Six-hundred and forty-seven LRYGB included patients were retrospectively reviewed for preoperative factors independently associated with EBMIL at 180 and 365 days postoperatively (EBMIL180 and EBMIL365, respectively). Previously validated factors were selectively analyzed, including age; race; gender; preoperative BMI (BMI0); hemoglobin; and diagnoses of hypertension (HTN), diabetes mellitus (DM), and depression or anxiety disorder. Variables significant upon multivariate analysis (P<.05) were modeled by “traditional” multiple linear regression and an ANN, to predict %EBMIL180 and %EBMIL365. Results The mean EBMIL180 and EBMIL365 were 56.4%±16.5% and 73.5%±21.5%, corresponding to total body weight losses of 25.7%±5.9% and 33.6%±8.0%, respectively. Upon multivariate analysis, independent factors associated with EBMIL180 included black race (B=−6.3%, P<.001), BMI0 (B=−1.1%/unit BMI, P<.001) and DM (B=−3.2%, P<.004). For EBMIL365, independently associated factors were female gender (B=6.4%, P<.001), black race (B=−6.7%, P<.001), BMI0 (B=−1.2%/unit BMI, P<.001), HTN (B=−3.7%, P=.03) and DM (B=−6.0%, P<.001). Pearson r2 values for the multiple linear regression and ANN models were .38 (EBMIL180) and .35 (EBMIL365), and .42 (EBMIL180) and .38 (EBMIL365), respectively. ANN-prediction of benchmark 50% EBMIL at 365 days generated an area under the curve of 0.78±0.03 in the training set (n=518), and 0.83±0.04 (n=129) in the validation set. Conclusions Available at https://redcap.vanderbilt.edu/surveys/?s=3HCR43AKXR, this, or other ANN models may be used to provide an optimized estimate of postoperative EBMIL following LRYGB.

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Section: Discussionmentioning

confidence: 99%

Prediction of excess weight loss after laparoscopic Roux-en-Y gastric bypass: data from an artificial neural network

2015

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“…(1,4,5) These tools often prove useful as they are able to improve their predictive ability, or "learn", as they encounter new data, and they benefit from internal validation and testing. (1,2,5,6,14,15) These ANNs are computational constructs that can segregate inputs and pattern recognize within these data to make predictions, using historical outputs. Over time, they can be trained to continue to fine tune their predictions as more input and output data is provided, and overtime can "learn" to make better predictions, in a way that, for example, logistic regression and most conventional statistics cannot.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural networks can predict trauma volume and acuity regardless of center size and geography: A multicenter study

Dennis

Stonko

Callcut

et al. 2019

J Trauma Acute Care Surg

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Background-Trauma has long been considered unpredictable. Artificial neural networks (ANN) have recently shown the ability to predict admission volume, acuity and operative needs at a single trauma center with very high reliability. This model has not been tested in a multicenter model with differing climate and geography. We hypothesize that an artificial neural network can accurately predict trauma admission volume, penetrating trauma admissions, and mean ISS with a high degree of reliability across multiple trauma centers.

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“…Thus, ANNs have been designed to use patient factors and disease characteristics (such as histopathologi-cal features from a tumor biopsy or demographic information) to predict outcomes in various clinical models, including TBI, postsurgical outcomes, and complications from surgery. 4,6,14,17,18 Previous traditional statistical models have failed in their inflexibility and inability to change based on the data that is presented. Therefore, we aimed to create a model that can predict outcomes using binary and continuous variables allowing for real-time clinical utilization, resulting in greater accuracy and higher predictive value in children sustaining TBI.…”

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confidence: 99%

Machine-learning analysis outperforms conventional statistical models and CT classification systems in predicting 6-month outcomes in pediatric patients sustaining traumatic brain injury

Hale

Stonko

Brown

et al. 2018

Neurosurgical Focus

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OBJECTIVEModern surgical planning and prognostication requires the most accurate outcomes data to practice evidence-based medicine. For clinicians treating children following traumatic brain injury (TBI) these data are severely lacking. The first aim of this study was to assess published CT classification systems in the authors’ pediatric cohort. A pediatric-specific machine-learning algorithm called an artificial neural network (ANN) was then created that robustly outperformed traditional CT classification systems in predicting TBI outcomes in children.METHODSThe clinical records of children under the age of 18 who suffered a TBI and underwent head CT within 24 hours after TBI (n = 565) were retrospectively reviewed.RESULTS“Favorable” outcome (alive with Glasgow Outcome Scale [GOS] score ≥ 4 at 6 months postinjury, n = 533) and “unfavorable” outcome (death at 6 months or GOS score ≤ 3 at 6 months postinjury, n = 32) were used as the primary outcomes. The area under the receiver operating characteristic (ROC) curve (AUC) was used to delineate the strength of each CT grading system in predicting survival (Helsinki, 0.814; Rotterdam, 0.838; and Marshall, 0.781). The AUC for CT score in predicting GOS score ≤ 3, a measure of overall functionality, was similarly predictive (Helsinki, 0.717; Rotterdam, 0.748; and Marshall, 0.663). An ANN was then constructed that was able to predict 6-month outcomes with profound accuracy (AUC = 0.9462 ± 0.0422).CONCLUSIONSThis study showed that machine-learning can be leveraged to more accurately predict TBI outcomes in children.

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Artificial neural networks in the diagnosis of acute appendicitis

Cited by 24 publications

References 9 publications

Prediction of excess weight loss after laparoscopic Roux-en-Y gastric bypass: data from an artificial neural network

Prediction of excess weight loss after laparoscopic Roux-en-Y gastric bypass: data from an artificial neural network

Artificial neural networks can predict trauma volume and acuity regardless of center size and geography: A multicenter study

Machine-learning analysis outperforms conventional statistical models and CT classification systems in predicting 6-month outcomes in pediatric patients sustaining traumatic brain injury

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