Artificial neural networks: useful aid in diagnosing acute appendicitis

Applying an explainable machine learning model might reduce the number of negative appendectomies in pediatric patients with a high probability of acute appendicitis

The diagnosis of acute appendicitis and concurrent surgery referral is primarily based on clinical presentation, laboratory and radiological imaging. However, utilizing such an approach results in as much as 10-15% of negative appendectomies. Hence, in the present study, we aimed to develop a machine learning (ML) model designed to reduce the number of negative appendectomies in pediatric patients with a high clinical probability of acute appendicitis. The model was developed and validated on a registry of 551 pediatric patients with suspected acute appendicitis that underwent surgical treatment. Clinical, anthropometric, and laboratory features were included for model training and analysis. Three machine learning algorithms were tested (random forest, eXtreme Gradient Boosting, logistic regression) and model explainability was obtained. Random forest model provided the best predictions achieving mean specificity and sensitivity of 0.17 ± 0.01 and 0.997 ± 0.001 for detection of acute appendicitis, respectively. Furthermore, the model outperformed the appendicitis inflammatory response (AIR) score across most sensitivity-specificity combinations. Finally, the random forest model again provided the best predictions for discrimination between complicated appendicitis, and either uncomplicated acute appendicitis or no appendicitis at all, with a joint mean sensitivity of 0.994 ± 0.002 and specificity of 0.129 ± 0.009. In conclusion, the developed ML model might save as much as 17% of patients with a high clinical probability of acute appendicitis from unnecessary surgery, while missing the needed surgery in only 0.3% of cases. Additionally, it showed better diagnostic accuracy than the AIR score, as well as good accuracy in predicting complicated acute appendicitis over uncomplicated and negative cases bundled together. This may be useful in centers that advocate for the conservative treatment of uncomplicated appendicitis. Nevertheless, external validation is needed to support these findings.

Artificial intelligence in the diagnosis and treatment of acute appendicitis: a narrative review

Artificial intelligence is transforming healthcare. Artificial intelligence can improve patient care by analyzing large amounts of data to help make more informed decisions regarding treatments and enhance medical research through analyzing and interpreting data from clinical trials and research projects to identify subtle but meaningful trends beyond ordinary perception. Artificial intelligence refers to the simulation of human intelligence in computers, where systems of artificial intelligence can perform tasks that require human-like intelligence like speech recognition, visual perception, pattern-recognition, decision-making, and language processing. Artificial intelligence has several subdivisions, including machine learning, natural language processing, computer vision, and robotics. By automating specific routine tasks, artificial intelligence can improve healthcare efficiency. By leveraging machine learning algorithms, the systems of artificial intelligence can offer new opportunities for enhancing both the efficiency and effectiveness of surgical procedures, particularly regarding training of minimally invasive surgery. As artificial intelligence continues to advance, it is likely to play an increasingly significant role in the field of surgical learning. Physicians have assisted to a spreading role of artificial intelligence in the last decade. This involved different medical specialties such as ophthalmology, cardiology, urology, but also abdominal surgery. In addition to improvements in diagnosis, ascertainment of efficacy of treatment and autonomous actions, artificial intelligence has the potential to improve surgeons' ability to better decide if acute surgery is indicated or not. The role of artificial intelligence in the emergency departments has also been investigated. We considered one of the most common condition the emergency surgeons have to face, acute appendicitis, to assess the state of the art of artificial intelligence in this frequent acute disease. The role of artificial intelligence in diagnosis and treatment of acute appendicitis will be discussed in this narrative review.

Artificial Intelligence and Acute Appendicitis: A Systematic Review of Diagnostic and Prognostic Models

BACKGROUND

To assess the efficacy of artificial intelligence (AI) models in diagnosing and prognosticating acute appendicitis (AA) in adult patients compared to traditional methods. AA is a common cause of emergency department visits and abdominal surgeries. It is typically diagnosed through clinical assessments, laboratory tests, and imaging studies. However, traditional diagnostic methods can be time-consuming and inaccurate. Machine learning models have shown promise in improving diagnostic accuracy and predicting outcomes.

MAIN BODY

A systematic review following the PRISMA guidelines was conducted, searching PubMed, Embase, Scopus, and Web of Science databases. Studies were evaluated for risk of bias using the Prediction Model Risk of Bias Assessment Tool. Data points extracted included model type, input features, validation strategies, and key performance metrics.

RESULTS

In total, 29 studies were analyzed, out of which 21 focused on diagnosis, seven on prognosis, and one on both. Artificial neural networks (ANNs) were the most commonly employed algorithm for diagnosis. Both ANN and logistic regression were also widely used for categorizing types of AA. ANNs showed high performance in most cases, with accuracy rates often exceeding 80% and AUC values peaking at 0.985. The models also demonstrated promising results in predicting postoperative outcomes such as sepsis risk and ICU admission. Risk of bias was identified in a majority of studies, with selection bias and lack of internal validation being the most common issues.

CONCLUSION

AI algorithms demonstrate significant promise in diagnosing and prognosticating AA, often surpassing traditional methods and clinical scores such as the Alvarado scoring system in terms of speed and accuracy.

Prediction of Perforated and Nonperforated Acute Appendicitis Using Machine Learning-Based Explainable Artificial Intelligence

Background: The primary aim of this study was to create a machine learning (ML) model that can predict perforated and nonperforated acute appendicitis (AAp) with high accuracy and to demonstrate the clinical interpretability of the model with explainable artificial intelligence (XAI). Method: A total of 1797 patients who underwent appendectomy with a preliminary diagnosis of AAp between May 2009 and March 2022 were included in the study. Considering the histopathological examination, the patients were divided into two groups as AAp (n = 1465) and non-AAp (NA; n = 332); the non-AAp group is also referred to as negative appendectomy. Subsequently, patients confirmed to have AAp were divided into two subgroups: nonperforated (n = 1161) and perforated AAp (n = 304). The missing values in the data set were assigned using the Random Forest method. The Boruta variable selection method was used to identify the most important variables associated with AAp and perforated AAp. The class imbalance problem in the data set was resolved by the SMOTE method. The CatBoost model was used to classify AAp and non-AAp patients and perforated and nonperforated AAp patients. The performance of the model in the holdout test set was evaluated with accuracy, F1- score, sensitivity, specificity, and area under the receiver operator curve (AUC). The SHAP method, which is one of the XAI methods, was used to interpret the model results. Results: The CatBoost model could distinguish AAp patients from non-AAp individuals with an accuracy of 88.2% (85.6–90.8%), while distinguishing perforated AAp patients from nonperforated AAp individuals with an accuracy of 92% (89.6–94.5%). According to the results of the SHAP method applied to the CatBoost model, it was observed that high total bilirubin, WBC, Netrophil, WLR, NLR, CRP, and WNR values, and low PNR, PDW, and MCV values increased the prediction of AAp biochemically. On the other hand, high CRP, Age, Total Bilirubin, PLT, RDW, WBC, MCV, WLR, NLR, and Neutrophil values, and low Lymphocyte, PDW, MPV, and PNR values were observed to increase the prediction of perforated AAp. Conclusion: For the first time in the literature, a new approach combining ML and XAI methods was tried to predict AAp and perforated AAp, and both clinical conditions were predicted with high accuracy. This new approach proved successful in showing how well which demographic and biochemical parameters could explain the current clinical situation in predicting AAp and perforated AAp.

Validity of Machine Learning in Detecting Complicated Appendicitis in a Resource-Limited Setting: Findings from Vietnam

Background

Complicated appendicitis, a potentially life-threatening condition, is common. However, the diagnosis of this condition is mainly based on physician's experiences and advanced diagnostic equipment. This study built and validated machine learning models to facilitate the detection of complicated appendicitis.

Methods

A retrospective cohort study was conducted based on medical charts of all patients undergoing a laparoscopic appendectomy at a city hospital during 2016-2020. The synthetic minority over-sampling technique (SMOTE) was used to adjust for the imbalance. Multiple classification approaches were used to train and validate models including support vector machine (SVM), decision tree (DT), K-nearest neighbor (KNN), logistic regression (LR), artificial neural network (ANN), and gradient boosting (GB).

Results

Among 1,950 patients included in the data analysis, there were 483 patients identified as having complicated appendicitis (24.8%). Based on data without SMOTE adjustment for imbalance, the accuracy levels and AUCs were high in all models using different parameters, ranging from 0.687 to 0.815. After adjusting for imbalance data using SMOTE, AUC and accuracy levels in the models using imbalance adjusted data were higher. Of these, the GB had all AUC and accuracy values of approximately 0.8 or more in both adjusted and unadjusted data.

Conclusions

Machine learning approaches including SVM, DT, logistic, KNN, ANN, and GB have a high level of validity in classifying patients with complicated appendicitis and patients without complicated appendicitis. Among these, GB had the highest level of validity and should be used or further validated. Our study indicates the beneficial potentials of machine learning techniques in a clinical setting in general and in the diagnosis of complicated appendicitis in particular.

Machine learning to guide clinical decision-making in abdominal surgery-a systematic literature review

PURPOSE

An indication for surgical therapy includes balancing benefits against risk, which remains a key task in all surgical disciplines. Decisions are oftentimes based on clinical experience while guidelines lack evidence-based background. Various medical fields capitalized the application of machine learning (ML), and preliminary research suggests promising implications in surgeons' workflow. Hence, we evaluated ML's contemporary and possible future role in clinical decision-making (CDM) focusing on abdominal surgery.

METHODS

Using the PICO framework, relevant keywords and research questions were identified. Following the PRISMA guidelines, a systemic search strategy in the PubMed database was conducted. Results were filtered by distinct criteria and selected articles were manually full text reviewed.

RESULTS

Literature review revealed 4,396 articles, of which 47 matched the search criteria. The mean number of patients included was 55,843. A total of eight distinct ML techniques were evaluated whereas AUROC was applied by most authors for comparing ML predictions vs. conventional CDM routines. Most authors (N = 30/47, 63.8%) stated ML's superiority in the prediction of benefits and risks of surgery. The identification of highly relevant parameters to be integrated into algorithms allowing a more precise prognosis was emphasized as the main advantage of ML in CDM.

CONCLUSIONS

A potential value of ML for surgical decision-making was demonstrated in several scientific articles. However, the low number of publications with only few collaborative studies between surgeons and computer scientists underpins the early phase of this highly promising field. Interdisciplinary research initiatives combining existing clinical datasets and emerging techniques of data processing may likely improve CDM in abdominal surgery in the future.

Machine Learning Algorithms for Predicting Surgical Outcomes after Colorectal Surgery: A Systematic Review

BACKGROUND

Machine learning (ML) has been introduced in various fields of healthcare. In colorectal surgery, the role of ML has yet to be reported. In this systematic review, an overview of machine learning models predicting surgical outcomes after colorectal surgery is provided.

METHODS

Databases PubMed, EMBASE, Cochrane, and Web of Science were searched for studies using machine learning models for patients undergoing colorectal surgery. To be eligible for inclusion, studies needed to apply machine learning models for patients undergoing colorectal surgery. Absence of machine learning or colorectal surgery or studies reporting on reviews, children, study abstracts were excluded. The Probast risk of bias tool was used to evaluate the methodological quality of machine learning models.

RESULTS

A total of 1821 studies were analysed, resulting in the inclusion of 31 articles. A vast proportion of ML algorithms have been used to predict the course of disease and response to neoadjuvant chemoradiotherapy. Radiomics have been applied most frequently, along with predictive accuracies up to 91%. However, most studies included a retrospective study design without external validation or calibration.

CONCLUSIONS

Machine learning models have shown promising potential in predicting surgical outcomes after colorectal surgery. However, large-scale data is warranted to bridge the gap between calibration and external validation. Clinical implementation is needed to demonstrate the contribution of ML within daily practice.

Integration of Physical Examination, Old and New Biomarkers, and Ultrasonography by Using Neural Networks for Pediatric Appendicitis

OBJECTIVE

The objective of this study was to evaluate physical examinations, imaging, and laboratory analyses individually and combined using innovative statistical analysis methods for the accurate diagnosis of pediatric appendicitis.

METHODS

Patients admitted to hospital with symptoms of abdominal pain whose pediatric appendicitis scores greater than 3 were included in the study. Clinical, radiologic, and laboratory findings and as a new biomarker calprotectin (CPT) concentrations were evaluated individually and combined using artificial neural networks (ANNs), which revealed latent relationships for a definitive diagnosis.

RESULTS

Three hundred twenty patients were evaluated (190 appendicitis [43 perforated] vs 130 no appendicitis). The mean ± SD age was 11.3 ± 3.6 years and 63% were male. Pediatric appendicitis scores, white blood cell (WBC) count, absolute neutrophil count (ANC), C-reactive protein (CRP) level, procalcitonin (PCT) and CPT concentrations were higher in the appendicitis group; however, only WBC and ANC were higher in first 24 hours of pain. White blood cells and CRP were diagnostic markers in patients whose appendix could not be visualized using ultrasonography (US). On classic receiver operating characteristic (ROC) analysis, the areas under the curve (AUCs) were not strong enough for differential diagnosis (WBC, 0.73; ANC, 0.72; CRP, 0.65; PCT and CPT, 0.61). However, when the physical examination, US, and laboratory findings were analyzed in a multivariate model and the ROC analysis obtained from the variables with ANN, an ROC curve could be obtained with 0.91 AUC, 89.8% sensitivity, and 81.2% specificity. C-reactive protein and PCT were diagnostic for perforated appendicitis with 0.83 and 0.75 AUC on ROC.

CONCLUSIONS

Although none of the biomarkers were sufficient for an accurate diagnosis of appendicitis individually, a combination of physical examination and laboratory and US was a good diagnostic tool for pediatric appendicitis.

To Scan or Not to Scan: Development of a Clinical Decision Support Tool to Determine if Imaging Would Aid in the Diagnosis of Appendicitis

BACKGROUND

Appendicitis is one of the most common surgically treated diseases in the world. CT scans are often over-utilized and ordered before a surgeon has evaluated the patient. Our aim was to develop a tool using machine learning (ML) algorithms that would help determine if there would be benefit in obtaining a CT scan prior to surgeon consultation.

METHODS

Retrospective chart review of 100 randomly selected cases who underwent appendectomy and 100 randomly selected controls was completed. Variables included components of the patient's history, laboratory values, CT readings, and pathology. Pathology was used as the gold standard for appendicitis diagnosis. All variables were then used to build the ML algorithms. Random Forest (RF), Support Vector Machine (SVM), and Bayesian Network Classifiers (BNC) models with and without CT scan results were trained and compared to CT scan results alone and the Alvarado score using area under the Receiver Operator Curve (ROC), sensitivity, and specificity measures as well as calibration indices from 500 bootstrapped samples.

RESULTS

Among the cases that underwent appendectomy, 88% had pathology-confirmed appendicitis. All the ML algorithms had better sensitivity, specificity, and ROC than the Alvarado score. SVM with and without CT had the best indices and could predict if imaging would aid in appendicitis diagnosis.

CONCLUSION

This study demonstrated that SVM with and without CT results can be used for selective imaging in the diagnosis of appendicitis. This study serves as the initial step and proof-of-concept to externally validate these results with larger and more diverse patient population.

Convolutional-neural-network-based diagnosis of appendicitis via CT scans in patients with acute abdominal pain presenting in the emergency department

Acute appendicitis is one of the most common causes of abdominal emergencies. We investigated the feasibility of a neural-network-based diagnosis algorithm of appendicitis by using computed tomography (CT) for patients with acute abdominal pain visiting the emergency room (ER). A neural-network-based diagnostic algorithm of appendicitis was developed and validated using CT data from three institutions who visited the ER with abdominal pain and underwent abdominopelvic CT. For input data, 3D isotropic cubes including the appendix were manually extracted and labeled as appendicitis or a normal appendix. A 3D convolutional neural network (CNN) was trained to binary classification on the input. For model development and testing, 8-fold cross validation was conducted for internal validation and an ensemble model was used for external validation. Diagnostic performance was excellent in both the internal and external validation with an accuracy larger than 90%. The CNN-based diagnosis algorithm may be feasible in diagnosing acute appendicitis using the CT data of patients visiting the ER with acute abdominal pain.

Modern perioperative medicine - past, present, and future

Modern perioperative medicine has dramatically altered the care for patients undergoing major surgery. Anaesthetic and surgical practice has been directed at mitigating the surgical stress response and reducing physiological insult. The development of standardised enhanced recovery programmes combined with minimally invasive surgical techniques has lead to reduction in length of stay, morbidity, costs, and improved outcomes. The enhanced recovery after surgery (ERAS) society and its national chapters provide a means for sharing best practice in this field and developing evidence based guidelines. Research has highlighted persisting challenges with compliance as well as ensuring the effectiveness and sustainability of ERAS. There is also a growing need for increasingly personalised care programmes as well as complex geriatric assessment of frailer patients. Continuous collection of outcome and process data combined with machine learning, offers a potentially powerful solution to delivering bespoke care pathways and optimising individual management. Long-term data from ERAS programmes remain scarce and further evaluation of functional recovery and quality of life is required.

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

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.

Application of an artificial neural network model for early outcome prediction of gamma knife radiosurgery in patients with trigeminal neuralgia and determining the relative importance of risk factors

OBJECTIVES

Stereotactic radiosurgery (SRS) is a minimally invasive modality for the treatment of trigeminal neuralgia (TN). Outcome prediction of this modality is very important for proper case selection. The aim of this study was to create artificial neural networks (ANN) to predict the clinical outcomes after gamma knife radiosurgery (GKRS) in patients with TN, based on preoperative clinical factors.

PATIENTS AND METHODS

We used the clinical findings of 155 patients who were underwent GKRS (from March 2000 to march 2015) at Iran Gamma Knife center, Teheran, Iran. Univariate analysis was performed for a long list of risk factors, and those with P-Value < 0.2 were used to create back-propagation ANN models to predict pain reduction and hypoesthesia after GKRS. Pain reduction was defined as BNI score 3a or lower and hypoesthesia was defined as BNI score 3 or 4.

RESULTS

Typical trigeminal neuralgia (TTN) (P-Value = 0.018) and age>65 (P-Value = 0.040) were significantly associated with successful pain reduction and three other variables including radiation dosage >85 (P-Value = 0.098), negative history of diabetes mellitus (P-Value = 0.133) and depression (P-Value = 0.190). On the other hand, radio dosage>85 (P-Value = 0.008) was significantly associated with hypoesthesia, other related risk factors (with p-Value<0.2), were history of multiple sclerosis (P-Value = 0.106), pain duration more than 10 years before GKRS (P-Value = 0.115), history of depression (P-Value = 0.139), history of percutaneous ablative procedures (P-Value = 0.148) and history of diabetes mellitus (P-Value = 0.169).ANN models could predict pain reduction and hypoesthesia with the accuracy of 84.5% and 91.5% respectively. By mutual elimination of each factor in this model we could also evaluate the contribution of each factor in the predictive performance of ANN.

CONCLUSIONS

The findings show that artificial neural networks can predict post operative outcomes in patients who underwent GKRS with a high level of accuracy. Also the contribution of each factor in the prediction of outcomes can be determined using the trained network.

A Computer Based Model in Comparison with Sonography Imaging to Diagnosis of Acute Appendicitis in Iran

Introduction

Acute appendicitis overlaps with conditions of other diseases in terms of Symptoms and signs in the first hours of presentation. Ultrasound imaging and laboratory tests are usually used to decrease the diagnosis errors in the case of abdominal pain. However, same results may be happened using the mentioned examination tools for a string of diseases with abdominal pain. Moreover, those tests raise the medical costs for hospitals and patients. Clinical Decision Support Systems (CDSSs) can be used to assist the physicians to make the proper health care decisions particularly in the unreliable conditions.

Objectives

To improve the decision making process by physicians in diagnosis of acute appendicitis, an optimizing model was developed. The main objective is to discover a diagnostic model using the minimum clinical factors available in the first hours of abdominal pain.

Methods

Fuzzy-rule based classifier is a known technique in the Decision Support Systems (DSSs). In this article thus the useful clinical factors were explored and the diagnosis knowledge was discovered using Honey Bee Reproduction Cycle (HRBC) algorithm in the Fuzzy-rule based system. In this model, the proposed algorithm created the Fuzzy rules as the diagnosis knowledge in an optimizing process. To evaluate the accuracy of the proposed model for diagnosing of appendicitis, a collection of data was gathered from abdominal patients who referred to the educational general hospitals in Ahvaz, Iran in 2014 to 2015 years. In this process, the proposed model was optimized first in a training phase using a training dataset, and then it was tested with the testing dataset. Then, the achieved results from the computer base model were compared with ultrasound imaging findings before surgery as well as other detection methods in the previous studies.

Results

The comparison results illustrated that the proposed hybrid classification model as a CDSS improves considerably the accuracy of acute appendicitis diagnosis. Experimental outcomes illustrated that the proposed algorithm improves considerably the optimization performance in the diagnostic problem with the accuracy rate of 89.9%. The mentioned rate was achieved while a limited range of factors as the input parameters were used in the hybrid model.

Conclusion

The proposed differential diagnostic model can be used as a CDSS especially conditions in which access to costly equipment such as CT scans and Sonography tools are limited. The developed model improves the diagnosis time as well as the treatment costs for the patients with acute abdomen suspicious of acute appendicitis.

Can new inflammatory markers improve the diagnosis of acute appendicitis?

BACKGROUND

The diagnosis of appendicitis is difficult and resource consuming. New inflammatory markers have been proposed for the diagnosis of appendicitis, but their utility in combination with traditional diagnostic variables has not been tested. Our objective is to explore the potential of new inflammatory markers for improving the diagnosis of appendicitis.

METHODS

The diagnostic properties of the six most promising out of 21 new inflammatory markers (interleukin [IL]-6, chemokine ligand [CXCL]-8, chemokine C-C motif ligand [CCL]-2, serum amyloid A [SAA], matrix metalloproteinase [MMP]-9, and myeloperoxidase [MPO]) were compared with traditional diagnostic variables included in the Appendicitis Inflammatory Response (AIR) score (right iliac fossa pain, vomiting, rebound tenderness, guarding, white blood cell [WBC] count, proportion neutrophils, C-reactive protein and body temperature) in 432 patients with suspected appendicitis by uni- and multivariable regression models.

RESULTS

Of the new inflammatory variables, SAA, MPO, and MMP9 were the strongest discriminators for all appendicitis (receiver operating characteristics [ROC] 0.71) and SAA was the strongest discriminator for advanced appendicitis (ROC 0.80) compared with defence or rebound tenderness, which were the strongest traditional discriminators for all appendicitis (ROC 0.84) and the WBC count for advanced appendicitis (ROC 0.89). CCL2 was the strongest independent discriminator beside the AIR score variables in a multivariable model. The AIR score had an ROC area of 0.91 and could correctly classify 58.3 % of the patients, with an accuracy of 92.9 %. This was not improved by inclusion of the new inflammatory markers.

CONCLUSION

The conventional diagnostic variables for appendicitis, as combined in the AIR score, is an efficient screening instrument for classifying patients as low-, indeterminate-, or high-risk for appendicitis. The addition of the new inflammatory variables did not improve diagnostic performance further.

The use of artificial neural networks to predict delayed discharge and readmission in enhanced recovery following laparoscopic colorectal cancer surgery

BACKGROUND

Artificial neural networks (ANNs) can be used to develop predictive tools to enable the clinical decision-making process. This study aimed to investigate the use of an ANN in predicting the outcomes from enhanced recovery after colorectal cancer surgery.

METHODS

Data were obtained from consecutive colorectal cancer patients undergoing laparoscopic surgery within the enhanced recovery after surgery (ERAS) program between 2002 and 2009 in a single center. The primary outcomes assessed were delayed discharge and readmission within a 30-day period. The data were analyzed using a multilayered perceptron neural network (MLPNN), and a prediction tools were created for each outcome. The results were compared with a conventional statistical method using logistic regression analysis.

RESULTS

A total of 275 cancer patients were included in the study. The median length of stay was 6 days (range 2-49 days) with 67 patients (24.4 %) staying longer than 7 days. Thirty-four patients (12.5 %) were readmitted within 30 days. Important factors predicting delayed discharge were related to failure in compliance with ERAS, particularly with the postoperative elements in the first 48 h. The MLPNN for delayed discharge had an area under a receiver operator characteristic curve (AUROC) of 0.817, compared with an AUROC of 0.807 for the predictive tool developed from logistic regression analysis. Factors predicting 30-day readmission included overall compliance with the ERAS pathway and receiving neoadjuvant treatment for rectal cancer. The MLPNN for readmission had an AUROC of 0.68.

CONCLUSIONS

These results may plausibly suggest that ANN can be used to develop reliable outcome predictive tools in multifactorial intervention such as ERAS. Compliance with ERAS can reliably predict both delayed discharge and 30-day readmission following laparoscopic colorectal cancer surgery.

Computer-assisted abdominal surgery: new technologies

BACKGROUND

Computer-assisted surgery is a wide field of technologies with the potential to enable the surgeon to improve efficiency and efficacy of diagnosis, treatment, and clinical management.

PURPOSE

This review provides an overview of the most important new technologies and their applications.

METHODS

A MEDLINE database search was performed revealing a total of 1702 references. All references were considered for information on six main topics, namely image guidance and navigation, robot-assisted surgery, human-machine interface, surgical processes and clinical pathways, computer-assisted surgical training, and clinical decision support. Further references were obtained through cross-referencing the bibliography cited in each work. Based on their respective field of expertise, the authors chose 64 publications relevant for the purpose of this review.

CONCLUSION

Computer-assisted systems are increasingly used not only in experimental studies but also in clinical studies. Although computer-assisted abdominal surgery is still in its infancy, the number of studies is constantly increasing, and clinical studies start showing the benefits of computers used not only as tools of documentation and accounting but also for directly assisting surgeons during diagnosis and treatment of patients. Further developments in the field of clinical decision support even have the potential of causing a paradigm shift in how patients are diagnosed and treated.

Predicting outcomes in patients with perforated gastroduodenal ulcers: artificial neural network modelling indicates a highly complex disease

Purpose

Mortality prediction models for patients with perforated peptic ulcer (PPU) have not yielded consistent or highly accurate results. Given the complex nature of this disease, which has many non-linear associations with outcomes, we explored artificial neural networks (ANNs) to predict the complex interactions between the risk factors of PPU and death among patients with this condition.

Methods

ANN modelling using a standard feed-forward, back-propagation neural network with three layers (i.e., an input layer, a hidden layer and an output layer) was used to predict the 30-day mortality of consecutive patients from a population-based cohort undergoing surgery for PPU. A receiver-operating characteristic (ROC) analysis was used to assess model accuracy.

Results

Of the 172 patients, 168 had their data included in the model; the data of 117 (70 %) were used for the training set, and the data of 51 (39 %) were used for the test set. The accuracy, as evaluated by area under the ROC curve (AUC), was best for an inclusive, multifactorial ANN model (AUC 0.90, 95 % CIs 0.85–0.95; p < 0.001). This model outperformed standard predictive scores, including Boey and PULP. The importance of each variable decreased as the number of factors included in the ANN model increased.

Conclusions

The prediction of death was most accurate when using an ANN model with several univariate influences on the outcome. This finding demonstrates that PPU is a highly complex disease for which clinical prognoses are likely difficult. The incorporation of computerised learning systems might enhance clinical judgments to improve decision making and outcome prediction.

Creation of an effective colorectal anastomotic leak early detection tool using an artificial neural network

PURPOSE

Anastomotic leaks greatly increase both morbidity and mortality amongst colorectal patients. Earlier detection of leaks leads to improved patient outcomes; however, diagnosis often proves difficult due to heterogeneous presentation and varied differential diagnosis. The purpose of the study was to create an artificial neural network (ANN) capable of accurately identifying patients at risk of developing a post-operative colorectal anastomotic leak.

METHODS

A genetic ANN was trained and validated on a retrospective patient cohort. Two comparative groups were identified: those with anastomotic leaks confirmed at re-operation with a control group of patients with a post-operative delayed recovery, but in whom leak was excluded and no re-operation required.

RESULTS

Seventy-six patients were identified: 20 confirmed leaks and 56 controls. No significant difference in the baseline features between leak and control groups in terms of age (leaks 65.9 years [SD 9.29] controls 58.3 years [SD 17.0)], P = 0.054). Utilising backwards variable selection, ANN maintained 19 input variables. Internal validation of the ANN produced a sensitivity of 85.0 %, specificity of 82.1 %, and AUC of 0.89 for correct identification of clinical anastomotic leaks. Of the 20 confirmed leaks, the model correctly identified 17 and misclassified 10 control patients in the clinical leak category. External validation on 12 consecutive pilot prospective patients produced a specificity of 83.3 %.

CONCLUSIONS

ANNs can be created to accurately detect clinical anastomotic leaks in the early post-operative period using routinely available clinical data. Further prospective ANN testing is required to confirm generalisability. ANNs may provide useful real-world tools for improving patient safety and outcomes.

A hybrid decision support model to discover informative knowledge in diagnosing acute appendicitis

Background

The aim of this study is to develop a simple and reliable hybrid decision support model by combining statistical analysis and decision tree algorithms to ensure high accuracy of early diagnosis in patients with suspected acute appendicitis and to identify useful decision rules.

Methods

We enrolled 326 patients who attended an emergency medical center complaining mainly of acute abdominal pain. Statistical analysis approaches were used as a feature selection process in the design of decision support models, including the Chi-square test, Fisher's exact test, the Mann-Whitney U-test (p < 0.01), and Wald forward logistic regression (entry and removal criteria of 0.01 and 0.05, or 0.05 and 0.10, respectively). The final decision support models were constructed using the C5.0 decision tree algorithm of Clementine 12.0 after pre-processing.

Results

Of 55 variables, two subsets were found to be indispensable for early diagnostic knowledge discovery in acute appendicitis. The two subsets were as follows: (1) lymphocytes, urine glucose, total bilirubin, total amylase, chloride, red blood cell, neutrophils, eosinophils, white blood cell, complaints, basophils, glucose, monocytes, activated partial thromboplastin time, urine ketone, and direct bilirubin in the univariate analysis-based model; and (2) neutrophils, complaints, total bilirubin, urine glucose, and lipase in the multivariate analysis-based model. The experimental results showed that the model with univariate analysis (80.2%, 82.4%, 78.3%, 76.8%, 83.5%, and 80.3%) outperformed models using multivariate analysis (71.6%, 69.3%, 73.7%, 69.7%, 73.3%, and 71.5% with entry and removal criteria of 0.01 and 0.05; 73.5%, 66.0%, 80.0%, 74.3%, 72.9%, and 73.0% with entry and removal criteria of 0.05 and 0.10) in terms of accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under ROC curve, during a 10-fold cross validation. A statistically significant difference was detected in the pairwise comparison of ROC curves (p < 0.01, 95% CI, 3.13-14.5; p < 0.05, 95% CI, 1.54-13.1). The larger induced decision model was more effective for identifying acute appendicitis in patients with acute abdominal pain, whereas the smaller induced decision tree was less accurate with the test data.

Conclusions

The decision model developed in this study can be applied as an aid in the initial decision making of clinicians to increase vigilance in cases of suspected acute appendicitis.

The Alvarado score for predicting acute appendicitis: a systematic review

BACKGROUND

The Alvarado score can be used to stratify patients with symptoms of suspected appendicitis; the validity of the score in certain patient groups and at different cut points is still unclear. The aim of this study was to assess the discrimination (diagnostic accuracy) and calibration performance of the Alvarado score.

METHODS

A systematic search of validation studies in Medline, Embase, DARE and The Cochrane library was performed up to April 2011. We assessed the diagnostic accuracy of the score at the two cut-off points: score of 5 (1 to 4 vs. 5 to 10) and score of 7 (1 to 6 vs. 7 to 10). Calibration was analysed across low (1 to 4), intermediate (5 to 6) and high (7 to 10) risk strata. The analysis focused on three sub-groups: men, women and children.

RESULTS

Forty-two studies were included in the review. In terms of diagnostic accuracy, the cut-point of 5 was good at 'ruling out' admission for appendicitis (sensitivity 99% overall, 96% men, 99% woman, 99% children). At the cut-point of 7, recommended for 'ruling in' appendicitis and progression to surgery, the score performed poorly in each subgroup (specificity overall 81%, men 57%, woman 73%, children 76%). The Alvarado score is well calibrated in men across all risk strata (low RR 1.06, 95% CI 0.87 to 1.28; intermediate 1.09, 0.86 to 1.37 and high 1.02, 0.97 to 1.08). The score over-predicts the probability of appendicitis in children in the intermediate and high risk groups and in women across all risk strata.

CONCLUSIONS

The Alvarado score is a useful diagnostic 'rule out' score at a cut point of 5 for all patient groups. The score is well calibrated in men, inconsistent in children and over-predicts the probability of appendicitis in women across all strata of risk.

Empirical antibiotic therapy (ABT) of lower respiratory tract infections (LRTI) in the elderly: application of artificial neural network (ANN). Preliminary results

LRTI are among the most common diseases in developed countries, including chronic obstructive pulmonary disease (COPD), one of the most frequent conditions. Their treatment in general practice is often unsuccessful and this increases hospital admissions. We know, bacterial infections in the elderly show a higher morbidity and mortality, either for more severe symptoms, than in younger adults, or because the causing agent often remains unknown. The need for a quick initiation of ABT often requires to chose on empirical grounds. To date there are no official guidelines for empirical ABT of COPD exacerbations, but only heterogeneous and often conflicting recommendations exist. The aim of our study was to identify a tool to guide the choice of the most effective empirical ABT when symptoms are acute and bacteriological tests cannot be performed. We used an ANN to study 117 patients aged between 55 and 97 years (mean 81.5 ± 8.7 years) (± S.D.), admitted with a diagnosis of pneumonia, COPD exacerbation or pneumonia with respiratory failure. We registered symptoms at onset and some individual variables such as age, sex, risk factors, comorbidity, current drug therapies. Then the ANN was applied to choose ABT in 20 patients versus 20 subjects whose therapy was chosen by the physicians, comparing these groups for therapy's efficacy, mean durations of therapy and hospitalization (H). In the learning phase, the ANN could predict the resolution index 99.05% of the time (i.e., 104 times) with a ± S.D. = 0.23. After the training, during the test phase, the network predicted the resolution index 91.67% of the time (i.e., 11 times) with a ± S.D. = 0.54, thus proving the validity of the relations identified during the learning phase. Preliminary results of the application of our tool, show the ANN allowed us to greatly reduce the duration of the ABT and subsequently of the H. Based on preliminary results, we assume that the use of ANN can make a valuable contribution in the choice of empirical ABT in the course of acute lung diseases in elderly.

An approach to model Right Iliac Fossa pain using pain-only-parameters for screening acute appendicitis

Acute appendicitis (AA) is one of the commonest of multiple possible pathologies at the backdrop of Right Iliac Fossa (RIF) pain. RIF is the most common acute surgical condition of the abdomen. Even though AA is a recognized disease entity since decades, its diagnosis still lacks clinical confidence and mandates laboratory tests. Given the issue, this paper proposes a mathematical model using Pain-Only-Parameters (POP) obtained from available literature to screen AA. Weights have been assigned for each POP to create a training data matrix (N = 51) and used to calculate the cumulative effect or weighted sum, which is termed as the Pain Confidence Score (PCS). Based on PCS, a group of real-world patients (N = 40; AA and NA = 20 each) are classified as cases of AA or non-appendicitis (NA) with satisfactory results (sensitivity 85%, specificity 75%, precision 77%, and accuracy 80%). Most rural health centers (RHC) in developing nations lack specialist services and related infrastructure. Hence, such a tool could be useful in RHC to assist general physicians in screening AA and their timely referral to higher centers.

Decision model for acute appendicitis treatment with decision tree technology--a modification of the Alvarado scoring system

BACKGROUND

How to decide the proper time to do laparotomies for acute appendicitis patients is sometimes very difficult, especially in areas with no imaging diagnostic tools. The Alvarado scoring system (ASS) is a convenient and inexpensive decision making tool; however, its accuracy needs to be improved. The decision tree is the most frequently used data mining technology for diagnostic model building. This study used a decision tree to modify the ASS and to prioritize the variables.

METHODS

We collected 532 patients who underwent appendectomy. Patients who had undergone incidental appendectomy were excluded from the study. The decision tree algorithm was constructed with the data mining workbench Clementine version 8.1. It is a top-down algorithm designed to generate a decision tree model with entropy. The algorithm chooses the best decision node with which to separate different classes from empirical data. The Wilcoxon signed rank test, Student t test and chi(2) test were used for statistical analysis.

RESULTS

Among the 532 patients recruited into the study, 420 had acute appendicitis and 112 had normal appendix. Women with acute appendicitis were older than their male counterparts (p < 0.001). All patients had right lower quadrant tenderness. The new model was constructed with decision tree technology, and the accuracy of the diagnostic rate was better than that of ASS (p < 0.001). The sensitivity and specificity of the new model were 0.945 and 0.805, respectively.

CONCLUSION

The new model is more convenient and accurate than ASS. Right lower quadrant tenderness is an inclusion criterion for acute appendicitis diagnosis. Migrating pain and neutrophil count > 75% were significant factors for acute appendicitis diagnosis if ASS score < 6. Although the criteria of nausea/vomiting and white blood cell count > 10,000/dL were significantly different between acute appendicitis and normal appendix, there was no significant contribution of entropy change below the "neutrophil count > 75%" nodes in the model. So they were erased from the decision tree model. Further studies need to be conducted to investigate why older women are at higher risk for acute appendicitis.

Novel solutions for an old disease: diagnosis of acute appendicitis with random forest, support vector machines, and artificial neural networks

BACKGROUND

Diagnosing acute appendicitis clinically is still difficult. We developed random forests, support vector machines, and artificial neural network models to diagnose acute appendicitis.

METHODS

Between January 2006 and December 2008, patients who had a consultation session with surgeons for suspected acute appendicitis were enrolled. Seventy-five percent of the data set was used to construct models including random forest, support vector machines, artificial neural networks, and logistic regression. Twenty-five percent of the data set was withheld to evaluate model performance. The area under the receiver operating characteristic curve (AUC) was used to evaluate performance, which was compared with that of the Alvarado score.

RESULTS

Data from a total of 180 patients were collected, 135 used for training and 45 for testing. The mean age of patients was 39.4 years (range, 16-85). Final diagnosis revealed 115 patients with and 65 without appendicitis. The AUC of random forest, support vector machines, artificial neural networks, logistic regression, and Alvarado was 0.98, 0.96, 0.91, 0.87, and 0.77, respectively. The sensitivity, specificity, positive, and negative predictive values of random forest were 94%, 100%, 100%, and 87%, respectively. Random forest performed better than artificial neural networks, logistic regression, and Alvarado.

CONCLUSION

We demonstrated that random forest can predict acute appendicitis with good accuracy and, deployed appropriately, can be an effective tool in clinical decision making.

An evaluation of the utility of additional tests in the preoperative diagnostics of acute appendicitis

BACKGROUND

Determining the optimum algorithm for diagnostic procedure in suspected acute appendicitis (AA) may not only reduce the number of unnecessary operations, but also the frequency of complications, and may contribute measurably to reducing the costs of treating patients with acute abdominal conditions.

OBJECTIVE

The aim of the study was to assess the value of standard diagnostic methods and measurement of selected biochemical and hematological parameters (C-reactive protein, CRP; interleukin-6, IL-6; procalcitonin, PCT; total count of white blood cell, WBC) in the accuracy of preoperative AA diagnosis.

MATERIAL AND METHODS

The prospective study included 132 patients (female: 52.3%, male: 47.7%) emergency admitted to the Surgical Department, aged 15 to 74 years (mean 36 years), with a suspicion of appendicitis. Measurement of PCT concentration was carried out by immunoluminometric assay, IL-6 concentration by micro enzyme-linked immunosorbent assay and CRP concentration by immunonephelometric assay. Statistical analysis was done by the chi-square test and Fisher's exact test for categorized discrete variables, and the Mann-Whitney U and Kruskal-Wallis tests for continuous variables. In order to assay the diagnostic utility of tests, the receiver operating characteristic model of curve analysis was used.

RESULTS

AA was confirmed in 89 (67.5%) of the patients operated on (group A). Twenty-six (19.7%) of the patients were not operated on and did not require surgery (group C); in 13 patients (9.8%) operated with a preliminary diagnosis of AA, no changes in the appendix were found during the course of the operation (group B). Four (3%) of the patients treated conservatively for periappendicular infiltration were excluded from the following analysis (group D). The mean count of WBC in AA was 13.22 ± 4.45 × 103/μL, with no statistical significance between groups, which does not allow the patients requiring surgery to be distinguished. The highest elevation of IL-6 concentration was observed in the group with the AA and the periappendicular infiltration: 101.5 ± 355.9 vs. 173.6 ± 228.33 pg/mL, respectively; p < 0.05. No surgery patients of group C showed considerably lower CRP concentrations than those of group D: CRP: 2.05 ± 3.6 vs. 6.36 ± 4.74 mg/L; p < 0.05. In cases of advanced forms of AA, the gangrenous with perforation, higher marker values are obtained than those in the phlegmonose form (186.60 ± 541.2 vs. 40.08 ± 48.3 pg/mL; (p < 0.05) for IL-6 and 8.88 ± 7.45 vs. 2.84 ± 3.83 mg/L; (p < 0.001) for CRP, respectively).

CONCLUSIONS

1. AA diagnosis based only on an assessment of clinical status may lead to an increase in the number of people operated with false-positive diagnoses of AA. 2. Applying additional diagnostic methods such as IL-6 determination seems to be useful in reducing the numbers of false-positive diagnoses of AA. 3. Laboratory tests, i.e., CRP, IL-6, and PCT are much more useful in assessing the risk of complications during the course of AA.

An evaluation of the utility of additional tests in the preoperative diagnostics of acute appendicitis

BACKGROUND

Determining the optimum algorithm for diagnostic procedure in suspected acute appendicitis (AA) may not only reduce the number of unnecessary operations, but also the frequency of complications, and may contribute measurably to reducing the costs of treating patients with acute abdominal conditions.

OBJECTIVE

The aim of the study was to assess the value of standard diagnostic methods and measurement of selected biochemical and hematological parameters (C-reactive protein, CRP; interleukin-6, IL-6; procalcitonin, PCT; total count of white blood cell, WBC) in the accuracy of preoperative AA diagnosis.

MATERIAL AND METHODS

The prospective study included 132 patients (female: 52.3%, male: 47.7%) emergency admitted to the Surgical Department, aged 15 to 74 years (mean 36 years), with a suspicion of appendicitis. Measurement of PCT concentration was carried out by immunoluminometric assay, IL-6 concentration by micro enzyme-linked immunosorbent assay and CRP concentration by immunonephelometric assay. Statistical analysis was done by the chi-square test and Fisher's exact test for categorized discrete variables, and the Mann-Whitney U and Kruskal-Wallis tests for continuous variables. In order to assay the diagnostic utility of tests, the receiver operating characteristic model of curve analysis was used.

RESULTS

AA was confirmed in 89 (67.5%) of the patients operated on (group A). Twenty-six (19.7%) of the patients were not operated on and did not require surgery (group C); in 13 patients (9.8%) operated with a preliminary diagnosis of AA, no changes in the appendix were found during the course of the operation (group B). Four (3%) of the patients treated conservatively for periappendicular infiltration were excluded from the following analysis (group D). The mean count of WBC in AA was 13.22 ± 4.45 × 103/μL, with no statistical significance between groups, which does not allow the patients requiring surgery to be distinguished. The highest elevation of IL-6 concentration was observed in the group with the AA and the periappendicular infiltration: 101.5 ± 355.9 vs. 173.6 ± 228.33 pg/mL, respectively; p < 0.05. No surgery patients of group C showed considerably lower CRP concentrations than those of group D: CRP: 2.05 ± 3.6 vs. 6.36 ± 4.74 mg/L; p < 0.05. In cases of advanced forms of AA, the gangrenous with perforation, higher marker values are obtained than those in the phlegmonose form (186.60 ± 541.2 vs. 40.08 ± 48.3 pg/mL; (p < 0.05) for IL-6 and 8.88 ± 7.45 vs. 2.84 ± 3.83 mg/L; (p < 0.001) for CRP, respectively).

CONCLUSIONS

1. AA diagnosis based only on an assessment of clinical status may lead to an increase in the number of people operated with false-positive diagnoses of AA. 2. Applying additional diagnostic methods such as IL-6 determination seems to be useful in reducing the numbers of false-positive diagnoses of AA. 3. Laboratory tests, i.e., CRP, IL-6, and PCT are much more useful in assessing the risk of complications during the course of AA.