Discussion
Our study showed that using a deep learning approach to analyze MIR imaging data from unstained histological slides can distinguish between benign and malignant lymphoid tissue and aid in classifying types of lymphomas. MIR imaging on tissue sections results in large and complex data sets that must be analyzed and interpreted. Focussing on possible use in routine diagnostics, the data acquisition and the subsequent analysis must take place within a narrow time window to offer an advantage over standard pathological diagnostics or assist the pathologist during the diagnostic procedure. A deep learning approach using NNs, offers such a possibility. Our NN, once trained, analyzed a MIR image in less than 25 ms. However, training NNs using a deep learning approach takes considerable time and requires graphical processing units (GPUs) and high-performance machines to process digital images [28, 30]. The training, however has to be performed only once, and the NN can then be used to classify IR data without further training. This even allows for real-time spectrum analysis.
NNs have already been implemented in medicine and used in various studies in the fields of radiology, cardiology, neurology, and pathology [30-34]. But several caveats prevent such techniques from being widely used. One of the most important is undoubtedly the ”black box” character of such analyses [29, 30]. The decisions of such algorithms are not easy for the human user to understand and interpret. Therefore, a certain scepticism about such approaches is understandable, especially in medicine, where comprehensible decisions with sometimes severe consequences for patients must be made and justified [28, 29].
Another problem is the need for large datasets to train NNs for a specific problem [28, 30]. Small datasets result in poor performance of NNs, while on the other hand, one has to bear in mind not to ”overfit” a NN [29, 30]. In the case of pathological diagnoses, obtaining such datasets is a significant problem, especially regarding rare diseases. But one has also to consider the extreme variability of histopathological patterns derived from various tissue types, building up organs [28]. Additionally, data quality is essential, especially when dealing with pixel-based data (images) in pathology, where artefacts might pose serious problems [28, 35, 36]. In fact, concerning MIR imaging, there are currently no available datasets that could be used, and it is also questionable if there will be any in the future. Overall, it is doubtful that any deep learning approach using NNs, combined with MIR microscopic imaging, will replace pathologists in the foreseeable future. It is more likely, that deep learning-driven approaches will be used to assist a pathologist during the diagnostic procedure [37].
Finally, besides technical questions and dataset availability, ethical and legal questions are also associated with the use of deep learning in decision-making processes in pathology. These fundamental questions range from concern about data privacy to the question of responsibility for a wrong decision based on a deep learning approach [29]. However, when there are fewer and fewer pathologists, deep learning techniques may assist as a diagnostic tool to support the pathologist in stratifying patients, identifying urgent cases, and thus better directing the routine workflow.
Finally, the financial side of introducing such technologies must also be considered. The financial pressure on pathology laboratories is already a challenge because of the increasing digitization and subsequent data storage [28]. The acquisition of access to the appropriate hard- and software, such as GPU clusters, as a must to train deep learning algorithms in practice, could fail due to a lack of funding [28, 38].
Our results demonstrate that a diagnosis and subtyping of human lymphoma could be streamlined based on MIR microscopy and a deep learning approach for data analysis. This might be a complementary pathway for a quick preliminary assessment of the type and aggressiveness of the disease and could probably help in advance to identify urgent diagnoses and, given the increasing shortage of pathologists, to prioritize these patients in the routine workflow.