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View research View latest news Sign up for updates. Metrics details. Interpretable techniques known as white box methods including logistic regression and decision trees as well as less interpreitable techniques known as black box methods, such as support vector machines SVM , random forests and AdaBoost, were used to develop models trained and validated on unseen data to diagnose AMD.
The gold standard was confirmed diagnosis of AMD by physicians. Sensitivity, specificity and area under the receiver operating characteristic AUC were used to assess performance.
Study population included patients eyes. In terms of AUC, random forests, logistic regression and adaboost showed a mean performance of 0. Both black-box and white box methods performed well in identifying diagnoses of AMD and their decision pathways. Machine learning models developed through the proposed approach, relying on clinical signs identified by retinal specialists, could be embedded into EHR to provide physicians with real time interpretable support. Peer Review reports.
As the prevalence of AMD is steadily increasing due to increasing life expectancy [ 2 ], early diagnosis and treatment becomes essential in slowing down progression of AMD and subsequent vision loss [ 3 ]. Multimodal high-resolution imaging has had a substantial impact on diagnosis and treatment of macular diseases [ 4 ]. Different imaging modalities can be used for AMD diagnosis [ 5 ]. In particular, optical coherence tomography OCT associated with color fundus image acquisition technology is a non-contact, non-invasive, high resolution technique which produces real-time images used to derive several features of the macula [ 6 ].
Such characteristics may allow OCT to become an effective screening instrument, employable in non-specialized environments such as pharmacies and by non-specialized personnel to perform automatic diagnosis of AMD without the intervention of a medical retinal specialist. However, to allow diagnosis by non-specialized personnel, OCT technology could be coupled with other clinical decision support functionalities [ 7 ] based on patient data which could enhance the potential of image analysis data.
Currently, the majority of commercially available OCT technologies incorporate basic algorithms to automatically identify the presence of risk factors in macula images and diagnose AMD [ 8 — 10 ].
A recent review [ 5 ] showed how the majority of these algorithms mainly focus on automatic segmentation of soft drusen, previously identified as one of the most important signs for the diagnosis of AMD [ 11 ]. But relying on just one sign to diagnose AMD can be suboptimal since AMD is a complex pathology which involves different stages of progression and requires consideration of several clinical aspects [ 12 ]. Therefore image analysis should be used in conjunction with other clinical biomarkers to enhance diagnosis [ 5 ].
Machine learning techniques [ 13 ] have been applied successfully to identify, extract and analyze features in macula digital imaging [ 14 — 18 ]. Methods that do not allow the clinician to identify a clear decision pathway for the diagnosis are often regarded with skepticism in the clinical community [ 14 ]. Data were collected cross-sectionally from routine patient visits and stored in an Electronic Health Record, specifically designed for macular diseases management. The work reported in this paper is preliminary research into the potential of machine learning techniques to be used for AMD diagnostic support; from the perspective of using longitudinal data i.
Relevant clinical signs  identified by clinicians during the visit as binary variables positive if identified :. Data records were stored per single eye. Primary diagnosis of AMD is the study outcome dependent variable. Accordingly, each eye observation diagnosed as AMD was assigned to one class, while eyes diagnosed with other macular diseases were assigned to another class.
The covariate set input variables included all the other attributes listed above and referred to the same eye. There is evidence in support of the hypothesis of disease correlation between different eyes of the same patient [ 21 ].
A preliminary screening on our data confirmed this hypothesis. However, information on the fellow eye may not be available when diagnoses are performed during a visit for example, it may be the first encounter. Therefore, we performed the analysis in this paper without taking into account the information about the presence of AMD in the fellow eye. The purpose of this section is not to provide a detailed explanation of machine learning methods, which is left to referenced works, but to give some introduction about the techniques which readers may be less familiar with [ 22 ].
Logistic regression is used for predicting the outcome of a categorical dependent variable i. An embedded procedure within logistic regression, called the LogitBoost [ 24 ] as implemented in RWeka R library [ 25 ] , was included to select the most relevant variables.
No variable interactions were explored. Support Vector Machines [ 26 ] are classifiers that divide data instances of different categories with a linear boundary supported by a very clear gap called maximum margin. They can be optimised via different internal algorithms, therefore a parameter search is often recommended. This solution however is more difficult to interpret. In this study we adopted a linear kernel and the nu-classification , optimizing the parameter nu in the value range [0.
Decision trees are non-linear graphical models that take the form of a flow chart. Decision trees consist of nodes which represent input variables, and edges branching from the nodes dependent on possible values of those input variables. Each terminal node leaf represents the value of the target variable given the values of the input variables after following the path from the root to the leaf. A decision tree is usually grown by starting from the whole population, looking at the most discriminative variable to predict a desired outcome which becomes a node , and splitting the data based on a cut-off value of this variable inducing an edge.
In our analysis, we adopted the party package of decision tree learning [ 29 ] within the R software. A single decision tree often does not yield satisfactory prediction performance. To improve performance, multiple different trees can be aggregated, and this takes the general name of a tree ensemble.
A weel-recognised tree ensemble method is the random forest [ 30 ], which infers different decision trees via resampling and randomization, producing an average prediction from all trees.
We used the randomForest package of R [ 31 ]. The combination of several trees makes the method more powerful, but also more difficult to interpret than a single decision tree.
We performed the complete cases analysis with all methods and used three different approaches for imputation of missing values: i addition of a categorical variable encoding the presence of a missing value; ii substitution with the overall population mode for binary attributes and mean for numeric ones; iii non-linear imputation based on random forests [ 33 ]. The robustness of performance was assessed via bootstrapping [ 35 ], a validation technique based on random data resampling with replacement here, 50 times ; we used the very conservative out-of-bag estimator which calculates errors on unseen data.
To assess the entity of the difference between means of two performance distributions, a modified t -test was used, penalising the degrees of freedom due to sample overlap [ 36 ]. The percentage of males was The mean std. The proportion of AMD diagnoses was Healthy subjects accounted for Contingency tables are available in the Additional files 1 and 2 for all the other variables.
Results were computed using complete cases and the three different imputation techniques described in the Methods section. Performance is shown in terms of AUC, sensitivity and specificity. In regards to AUC, random forest and logistic regression were ranked as the best, followed by AdaBoost, support vector machine, decision tree and one-rule. When considering sensitivity the percentage of patients who are correctly identified as having AMD , support vector machine was superior, whilst random forest displayed the highest specificity the percentage of healthy people who are correctly identified as not having AMD.
Soft drusen, as expected, was the most important variable with an odds ratio positive vs negative of As shown by the overall sensitivity, specificity and AUC results, the decision tree assures fair performance and its structure has high interpretability. The tree should be traversed from the root node downwards. Split nodes are evaluated according to the value of the variable of interest and the decision pathway to follow is the corresponding attribute value on the branch. Again, soft drusen had the highest discriminative power 76 eyes out of 83 with a positive soft drusen are diagnosed with AMD and was selected as the root node.
The tree is to be traversed downwards from the root node. The p-values are calculated according to a chi-square test and represent the discriminatory power of a variable in a data stratum as induced by the tree partition. Each final leaf node gives the probability of AMD diagnosis based on the prevalence in the population sub-stratum following the corresponding tree pathway induced by node splits on variable values.
In agreement with logistic regression and decision tree, soft drusen and age are consistently at the top of the ranking. This is confirmed in all analyses performed with different imputation methods see Additional files 1 and 2. This work investigated several machine learning approaches for deriving an automated system for AMD diagnosis, using clinical attributes identified by a medical retinal specialist during a routine visit.
The study population was monitored via an Electronic Health Record employed by a single clinical practice in Genoa, Italy. We found that higher complexity-higher performance does not necessarily hold in all contexts and a performance-complexity compromise may be found.
These two modelling techniques combined interpretability and performance, as shown through the odds ratio table and tree diagram, which can be easily followed by a clinician during the diagnostic process. Physicians must be involved in the decision about what type of system will be used in practice because without their agreement and trust, such a system risks not to being used. Generally, from the perspective of a fully automated system, where a computer program performs all calculations, the main driver should not be the interpretability of the model, but the overall performance.
But in the case where model performances are comparable, such as the results reported in this study referring, the white-box is a preferable alternative. The methods proved to be robust to handle missing values and obtained performances did not change significantly varying the imputation methods. In fact, the clinicians that performed the analysis suggested that the majority of missing values are likely to be clinical signs that they did not identify during encounters, and thus negative values were not recorded in the system to save time, starting from the assumption that if a sign had been identified a positive value would have been registered in the system.
This study has some limitations. The study population itself is not large subjects and eyes and includes only patients from a local regional area. Although the out of bag error estimator is very conservative, a way in which the generalisation error could be challenged is by considering the study population and the diagnostic process as regionally biased: for instance, by assuming that the population of Genoa and neighbouring areas Liguria is different from Italy or worldwide and that doctors make diagnoses differently.
Accordingly, it would be interesting to see how the automated diagnostic algorithms would behave on patients from other countries. This would unveil indirectly the differences in the population characteristics and in the gold-standard diagnostic procedures.
Performance would be affected only by using two different systems trained on two different populations, whilst one could infer a new integrated model which takes into account such regional differences and aims at the same diagnostic ability in different settings. A more thorough analysis of missing values could be performed in order to identify the characteristic of missingness and their relevancy.
Also, further investigation on intra-patient correlation is warranted. Using two records from the same patient i. We carried out a series of additional experiments, not shown in this paper, using only single-patient and single-eye data out of eyes, we selected eyes pertaining to different patients randomly, for 10 times.
The analysis on this uncorrelated data was consistent with the main results in terms of sensitivity but yielded slightly lower specificity. This is most likely due to the smaller sample size vs. A larger population and attribute set may also help to refine the model and allow prediction of different subtypes of AMD, for instance neurovascular AMD.
From a rationale point of view, the utility of the system -for now- is to determine which are the diagnostic processes followed by the physicians, since the data were cross-sectional and the diagnoses were made by doctors during visits. We found that even by using powerful nonlinear machine learning models, we could not exactly all the consistent sets of diagnostic pathways. When longitudinal data and new background variables e. In fact, we are in an era where diagnosis of AMD is most commonly pursued by image analysis, yet digital image processing techniques embedded in commercial OCT systems are still in their infancy.