Artificial intelligence in Dermatopathology

Ever evolving research in medical field has reached an exciting stage with advent of newer technologies. With the introduction of digital microscopy, pathology has transitioned to become more digitally oriented speciality. The potential of artificial intelligence (AI) in dermatopathology is to aid the diagnosis, and it requires dermatopathologists' guidance for efficient functioning of artificial intelligence.


| INTRODUC TI ON
Ever evolving medical research has reached an exciting stage with advent of new technologies which range from virtual reality, genetic analysis, stem cell therapy, and robotics to artificial intelligence (AI).
With the invention of newer technologies, our approach to a dermatological condition, in terms of diagnosis or management, is rapidly evolving. Dermatology being a visually oriented speciality has taken a pivotal position in AI implementation, because of its large clinicodermoscopic-histopathological image database. With the rapid development of digital microscopy, which facilitates digitalization of histopathological slides and with the advent of whole slide imaging (WSI), pathology has recently transitioned to become more digitally oriented medical speciality. Currently, the potential of use of AI in dermatopathology is to mainly complement the diagnosis.
Guidance of dermatopathologist is enormous in training and efficient development of AI algorithms. Accumulation of large number of digital images represents an essential resource not only for education and research but also for efficient training of AI algorithms to improve workflow efficiency of a dermatopathologist. Several studies have evaluated the role of AI in dermatology. 1 Schaumberg 2 et al presented a machine learning method for disease prediction and immunostaining using thousands of images obtained from various articles. The use of AI in dermatopathology is considered as one of the most important and potential dermatological applications of AI. 3 Dermatopathologists play an important role, as their guidance is essential for training and development of AI algorithms. Hence, it is equally essential to know about the attitude of dermatopathologists toward the rapid increase in the use of AI in this field. In this review, we provide an overview about use of AI in dermatopathology, attitude of pathologists toward AI, its challenges and opportunities along with its future potential.

| AI IN DERMATOPATHOLOGY
Machine learning, a subset of AI, is the study of algorithms and data programming instructions that the computer learns automatically and utilizes to perform a task. 4 These machine learning methods can be supervised or unsupervised, based on whether the input data are fed with or without defined answer, respectively. 5 These methods are constrained by the amount of data available for learning. Reinforcement learning helps to maximize data analysis, so that the system learns from the environment in addition to the input data. 4 Neural networks are the most popular technique of machine learning, and the two popular subtypes of it are deep learning and convolutional neural networks (CNN). 6,7 Deep learning is a type of machine learning that utilizes artificial neural networks. These neural networks are mathematical models that use algorithms comprising of input, hidden and output layers. In simple neural networks, the intermediate or the hidden layer is only one but with the emerging computational power, it is possible to increase the hidden intermediate layer of the neural network infinitely to obtain more sensitive and a specific output. This multilayered neural network is involved in deep learning. 4 CNN is a type of artificial neural network that is primarily useful in image recognition, processing, classification, and segmentation. CNN is a deep neural network that is considered as an ideal tool in dermatopathological image analysis. Here, an image is disintegrated into a collection of pixels for effective image analysis. The three layers in CNN are convolutional layer, which forms fundamental component of the architecture performing extraction of the image, where each node is assigned a specific feature like color, shape, and size. 8 Then, the pooling layer classifies the input images by enabling CNN to accept inputs of different sizes and reduces the number of parameters to be studied. Then, finally the fully connected layer is involved in displaying the accurate output as per the input. A subclass of CNN known as region based CNN is involved in identification of a particular object within the image like location of the lesion.

| US E OF AI IN D IAG NOS TIC DERMATOPATHOLOGY
The concept of AI in dermatopathology was first described in 1987 with a system called TEGUMENT, 9 which was designed to identify about 986 histopathological diagnosis using light microscopic images with an accuracy of 91.8%. However, it required traditional medical source of information to be reorganized into the system.
Hence, it was more of a computer aided human diagnosis than primarily a machine based analysis. However, with the advent of newer computational resources, direct machine based image analysis can now be a reality. 10

| AI IN THE D IAG NOS IS OF MEL ANOC Y TIC NEOPL A S M
Melanoma is one of the primary causes of skin cancer related mortality, worldwide. 11 Like other cancers, it is primarily diagnosed by tissue biopsy. The emphasis on digital WSI is to augment the pathologist's intelligence that cannot be gleaned by manual examination. The most important role of AI is to distinguish malignant and benign pigmented lesions as the approach to treatment varies significantly based on the classification. In a study, Hekler 12 et al.
used a total of 695 melanocytic neoplasms and classified them into nevus or melanoma. All stages of melanoma and the entire spectrum of nevi were represented. The digital images obtained were scanned, fragmented, segmented, and analyzed using the image database of CNN. In this study, CNN significantly outperformed (p = 0.016) pathologists in the accurate diagnosis of nevi and melanoma. The diagnostic discordance between dermatopathologists and AI was 20% for nevi, 18% for melanoma, and overall it was 19% but the discordance among dermatopathologists was around 25%-26%. A study by Logu FD et al. 13 developed an AI to recognize histopathological images of cutaneous melanoma. Here, 791 patches of normal skin and 1122 patches of pathological tissue were used for testing the diagnostic accuracy, sensitivity, and specificity of CNN and compared it with diagnostic accuracy by expert dermatologists as a reference. The results showed a high diagnostic accuracy of 96.5%, sensitivity of 95.7%, and specificity of 97.7% and concluded that deep learning system trained to recognize melanoma achieve higher accuracy compared to expert diagnosis. Deep learning is also been used in predilection of recurrence rate of distant metastasis and also disease specific survival rate in early melanoma. In a retrospective study, Kulkarni et al. 14 used scanned histopathological slides from 108 patients, and the images were divided into pixels, grids, and sequences, to be processed by CNN to obtain an accurate output. The output was further processed by recurrent neural network, which included

| LI M ITATI O N S
Although promising, current CNN models in dermatopathology have a narrow classification. Dermatopathologists are capable of recognizing various morphological variants of diseases and also can exclude wide number of differential diagnosis but most CNN can only identify if the image is positive/negative for a diagnosis. 26 Due to the potential of high inter-observer-variability among dermatopathologists, it is difficult to develop and accurately train CNN which is in par with dermatopathologist in classifying various skin lesions.
At present, the data of images for various dermatosis are insufficient; also, the degree of image sharing among sources is poor and the quality of images is not uniform. For effective functioning of deep learning algorithms, there is a need for substantial quantity of diverse and high quality data to improve diagnostic efficacy. Though AI is known to improve efficiency of dermatopathologists, it is not feasible in resource-poor setting. There is an essential need for multidisciplinary involvement for accurate and efficient functioning of AI. Legal, ethical, and data privacy issues also need to be sorted out.
Moreover, the complete humanistic or holistic approach cannot be achieved by AI.

| CHALLENG E S FOR US E OF AI IN DERMATOPATHOLOGY
As histopathologic scans comprise of millions of pixels and are of higher dimensions, there are lot of technical challenges like lack of labeled data, the types of tissues could be infinite, the need for high quality extraction ultimately leads to higher computational expenses. Only histopathologic images based on deep learning algorithms may not perform better than dermatologists who have access to patient's medical history and clinical information.
Inter-class similarities and intra-class dissimilarities can lead to diagnostic challenges as deep learning algorithms need extensive training to reliably distinguish these lesions. There is a communication barrier between AI and dermatologists making it hard to interpret the decisions made by deep learning algorithms. 27

| OPP ORTUNITIE S FOR IMPROVING PERFORMAN CE OF AI
We are in an exciting time of AI with researchers claiming their systems outperforming dermatologists. However, the real life diagnostic are still performed by clinicians in diagnosing dermatological disorders especially skin cancers. In spite of these drawbacks, deep learning algorithms can perform very well in future especially with respect to diagnosing skin cancers. 27 Other opportunities include creating balanced and diverse datasets and proper selection of cases and development of computer aided diagnosis which results in evolution of WSI in digital pathology, thus improving the performance of AI. 28,29 Data augmentation with image transformations such as rotation, random crop, horizontal or vertical flip, shear, and translation may improve diagnostic accuracy. There is need for semantic explanation of the inferences obtained help in assisting the clinicians in their practice. Multiple deep learning algorithms help in evaluating different aspects of the lesions and generate a final conclusion.
Additionally, multimodality solution and combination of clinical data and imaging features need to be incorporated to develop fusion data algorithms to provide accurate final prediction 31 .

| CON CLUS ION
With introduction of WSI and newer computational resources, AI in dermatopathology has gained a lot of attention during the recent years. AI is well trained in diagnosing melanoma, which is a binary simple classification. However, diagnosis of NMSC is difficult due to vast intra-class variability and interclass similarities. However, with further research and by incorporating the given opportunities, in future, AI can do very well not only for skin cancers but also for other dermatoses. Finally, it is very essential for dermatologists and pathologists to welcome AI and to realize that AI has the potential to support clinicians positively and will be dependent on clinicians for its efficient functioning and can never replace them.

ACK N OWLED G M ENT
Open Access funding enabled and organized by Projekt DEAL.

CO N FLI C T O F I NTE R E S T
None.

DATA AVA I L A B I L I T Y S TAT E M E N T
Data sharing not applicable to this article as no datasets were generated or analysed during the current study.