Deep learning in interstitial lung disease—how long until daily practice

Interstitial lung diseases (ILDs) denote over 200 diverse lung disorders that involve inflammation and fibrosis of interstitium, with overlapping clinical, radiological, and pathological features, representing an important morbidity and mortality cause [1].

High-resolution computed tomography (HRCT) is the main method in ILD diagnosis, due to the lung tissue–specific radiation attenuation properties and maximum spatial resolution. The imaging data are evaluated by various textural patterns in the lung window extent and distribution. The assessment focuses on the image’s gray tones and geometrical structures, effectively a repetitive pattern matching problem, creating the perfect context for using computer-aided diagnosis (CAD) systems.

CAD enables medical practitioners to understand and utilize various imagistic investigations, with the help of information technology (IT) techniques [2]. The aim is to improve the diagnosis time and accuracy, with IT acting as support or even as an independent diagnostic option [3]. The CAD algorithms belong to artificial intelligence (AI), emulating human thinking [4]. The ILD diagnosis is essentially an algorithm with the following workflow: in comprehensive history, if physical examination findings and paraclinical investigations (chest X-ray, measurements of lung function, usual and specific blood tests) present a suspicion for ILD, a HRCT is performed [5]. The human factor intervenes next, by verifying the resulted data validity and, if no problems/artifacts are found, searching for patterns in specific locations. If there are clear findings, a diagnosis can be formulated, but if the results are inconclusive, the outcome is a list of possibilities requiring further discussions and more complex, invasive investigations. The individual performing this algorithms is therefore critical to accurate and speedy diagnosis, injecting an overall intrinsic variability. Since CAD can emulate the algorithm, it would make it an ideal choice for this stage, eliminating variances.

This paper aims to offer an in-depth analysis of the way virtual AI improves ILD diagnosis, with an emphasis on convolutional neural networks (CNNs).

AI component’s virtual subclass is machine learning (Fig. 1), comprising mathematical algorithms used by computer systems in order to learn a specific task through experiences, without specific human instructions [6].

The advancement of that is deep learning (DL), consisting of a multi-layer representation learning architecture. The representation activates the first layer of neurons through a sensor, which, in turn, activates the next layer by complex connections. Each layer processes the representation in a non-linear way, creating an increasingly complex schema, diverging from the general machine learning task-specific algorithm [7, 8].

DL’s major advantage is that it can improve autonomously, without human input. From a usage standpoint, it can perform arbitrary parallel computation more efficiently than other algorithms [9, 10]. DL is used in visual object recognition [7], speech recognition [11], driving assistance [12], and language classification [10] among others.

The first algorithm successfully used for pattern recognition was neocognitron, in 1980, which integrated neurophysiological architecture [8, 13, 14]. The key for successful feature extraction is in creating an appropriate network architecture, as shown by the apparition of backpropagation technique, in 1989, which allowed handwritten digit recognition, becoming a landmark reference [15].

CNNs require large, balanced datasets and advanced algorithms, reflecting into processing power and storage capacity requirements [8, 9]. Krizhevsky et al developed a CNN model named Alex Net by gathering the biggest database for training: 1.2 million images. The algorithm was able to classify the images into 1000 nature categories with the smallest possible error rate [16], making it the state-of-the-art database for training CNNs.

Performance wise, the first neural network to achieve superhuman performance in visual pattern recognition (http://​people.​idsia.​ch/​~juergen/​superhumanpatter​nrecognition.​html) appeared in 2011, when Ciresan et al used a deep neural network on graphics processing unit to recognize traffic signs images. In the last decade, graphics processing unit improvements made possible shorter computation time for complex operations in a common setting, flourishing CNN development [8, 17].

Each CNN has a complex architecture with an initial image input as pixel array from a receptive field with several hidden computational connection layers afterward [6, 7] as detailed in Fig. 2.

The convolutional layer, CNN’s main component, consists of multiple, weighted individual filters [7]. The multiple filter sets detect different patterns in images. The detection progresses from small patterns such as corners, lines, and edges to shapes and objects.

For a CNN to perform, it requires multiple layer types and transmission between them [6]. The final stage predicts the image category probabilities, determining the strongest and the most relevant active feature class [16].

The earliest CNN application in the Healthcare field dates into the early 1990s. Lo et al applied the CNN algorithm for lung nodule detection on chest X-rays, reaching a true-positive detection rate of 80% [18]. Sahiner et al used CNNs to classify mass from normal breast tissue on mammograms, with a 90% positive value [19]. In 2008, CNNs successfully detected hippocampal sclerosis on brain MRI [20]. These first studies’ high positive rates are biased due to the small database and easily detected lesion types.

There are multiple challenges in acquiring medical images for using in deep learning:

A workaround could be transfer-learning, where weights from a trained CNN on a nature dataset are conveyed to another CNN with a different dataset [6, 21].

Even though natural images and medical images are greatly different, the former being colored, whereas X-rays, CT images, and MRI images are gray-scaled, they all share the same descriptors. Histograms of oriented gradient and scale-invariant feature transform have been successfully applied to medical image segmentation and detection. Bar et al validated this in chest pathology by applying CNNs trained on non-medical image datasets to 93 chest X-ray images [22]. The area under the curve (AUC) was 0.93 for right pleural effusion detection, 0.89 for heart enlargement detection, and 0.79 for classifying normal versus abnormal chest X-rays. All values are well above 0.5, showing excellent model skill.

Starting in 2014, CNN applications in bio-imaging research flourished in segmentation, detection, and classification applications like lung nodule detection and classification, colon polyp detection, coronary calcification detection [23, 24], skin cancer classification [25], knee cartilage segmentation [26], brain tumor segmentation [27], and breast lesions classification [28, 29]. At present, the intention is to achieve higher accuracy and better performance than that of a human counterpart.

Although AI research has been predominantly directed to neurology [30], oncology, and cardiovascular diseases [6], as the first three major death causes, the chest imaging field also presents interest for lung nodule detection and classification [31, 32], tuberculosis lesion classification [33], lesion detection and classification [34], and parenchyma pulmonary disease classification [35].

Typical ILD patterns in (HR) CT images are reticulation (RE), honeycombing (HC), ground-glass opacity (GGO), consolidation (CD), micro-nodules (MN), emphysema (EM), or combinations of the above. The difficulty appears when the results are combined or inconclusive (Fig. 3)

CNNs need large image samples because normal lung or different tissue categories could exhibit similar appearances (Fig. 3a, b or c, d), while significant variations might be seen between different subjects for the same tissue class (Fig. 3c–e).

Anthimopoulos M. et al [36] designed and trained one of the first CNN to classify the most common ILD patterns, achieving an 85.5% classification performance, therefore demonstrating the DL recognition capacity for lung tissue idiosyncrasy. Experienced radiologists annotated 120 HRCT by excluding ambivalent lung areas and the bronco-vascular tree which were used afterwards for training and testing the CNN. The proposed algorithm achieved superior performance compared with the state-of-the-art methods at that time (Alex Net, VGG-Net-D), mainly because of the hyper-parameters developed for the ILD pattern characterization. However, a misclassification rate was found between HC and RE due to their common fibrotic nature as shown in Annex 1 in the Supplementary Material. The combination between GGO/RE and individual GGO and RE pattern had also a high misclassification rate due to the overlapping appearance. Clinically, this reflects the significance in differentiating idiopathic pulmonary fibrosis (IPF) vs. non-specific interstitial pneumonia, as an accurate description of texture apart of gray-scale intensity value.

Dealing with these challenges, Christodoulidis S. et al [21] presented a CNN architecture that could extract the textural variability of ILD patterns. Using transfer learning from multiple non-medical source databases, they achieved an unsatisfactory increase of only 2% in the CNN performance. The big downfall in [21] is the usage of CT images, instead of HRCT.

Applications that use HRCT are only a few, like [37‐39], all using CNNs for categorization. Even if Li et al [38] and Li et al [39] go a step further by using a custom architecture in an unsupervised algorithm, their performance is not as good as other options (e.g., [21, 40]).

One major conundrum in developing high-accuracy deep learning algorithms for fibrotic lung disease diagnostics is the lack of large imaging datasets for training. To overcome this problem, a centralized imaging repository needs to be created through an international collaborative effort. Not only that, but the images need to be unified under a desirable format, HRCT. There are too few algorithms that deal with these image types like [35, 37‐39] and since the 1-mm-thick slices can show lesions otherwise omitted, this format is a necessity. Since the images will, more than likely, be gathered from multiple sources, the resolution, gray-scale, and annotations need a unified format, also.

A handle-able CNN that could be deployed on any computer station and accessible to non-academic centers is the “Next Generation” of AI in clinical practice. Namely, in the field of ILDs, AI can help to differentiate and early diagnose the patients with the most severe forms, i.e., IPF. Early diagnosis in IPF will lead to targeted antifibrotic treatment which substantially prolongs the survival and reduces acute exacerbations which are not only deadly but also costly [52‐56]. In order to reach such high expectations, a hybrid algorithm should be developed. Since some algorithms show exquisite accuracy in some areas, like [42], a combination of multiple CNN can be the answer to reducing the costs spent on the social and healthcare aspects [57, 58]. The CNN combination could have different configurations and start forming the same input or, on the contrary, have similar structures and different inputs. Even more interesting would be a combination of different AI techniques, like CNN with clusterization and classification algorithms, maybe in different stages, parallel or subsequent. No matter what the approach is, the purpose is to keep local area computing to a minimum, leaning therefore towards a cloud architecture style. In this case, legal aspects, like data privacy and security, begin to play an important role and the communication between the local and computational nodes could be obstructed by security protocols.

In this review, we describe the deep learning algorithm developments and their implication in the medical field, especially in the ILD diagnostic. We highlighted the challenges, but also the implementation options that would, one day, lead to daily practice, with a clinical implication in early diagnosis of ILDs. Developing a CAD that could be deployed on any computer station and be accessible to non-academic centers is the next frontier in the early diagnosis of IPF.