Tau-related neurodegenerative disorders, the tauopathies, comprise a heterogeneous group of disorders with a clinical spectrum that includes primary motor symptoms, movement disorder, psychiatric dysfunction, and cognitive impairment [1]. Histomorphologically, tauopathies are characterized by intracellular deposition of hyperphosphorylated tau protein. Various isoform compositions, morphology, and anatomical distributions of intracellular tau represent distinct diagnostic features of tauopathies [1,2,3]. How pathological tau causes neuronal dysfunction and degeneration is unclear. Several mechanisms have been implicated, including both genetic and environmental risk factors, but most cases are idiopathic [1, 3,4,5]. Sporadic tauopathies, such as the vast majority of Alzheimer disease (AD) and progressive supranuclear palsy (PSP) cases, are associated with common genetic risk alleles [1, 3]. Rare highly penetrant mutations in the microtubule-associated protein tau gene are associated with some forms of frontotemporal lobar degeneration [6]. Environmental factors, such as traumatic brain injury in the case of chronic traumatic encephalopathy (CTE) or putative neurotoxins, have also been implicated [7, 8]. Pathological changes in tau metabolism and post-translational modifications result in the accumulation of toxic forms of misfolded tau aggregates in neurons and glial cells in various brain regions. These misfolded aggregates are associated with loss of function and ultimately cell death [1, 2].

Pathological tau forms inclusions in neurons and glia with histomorphologically distinguishable features. In neurons, these take the form of the classical flame-shaped intracellular neurofibrillary tangles (NFTs), granular pre-NFTs, extracellular “ghost” tangles, ring tangles, and globose tangles, among others [9]. In glia, there is a spectrum of characteristic histomorphological forms that are commonly associated with specific diseases, including glial plaques of corticobasal degeneration, tufted astrocytes of PSP, globular astroglial inclusions in globular glial tauopathy, ramified astrocytes of Pick disease, and thorn-shaped astrocytes as well as granular fuzzy astrocytes of aging-related tau astrogliopathy [9,10,11]. One recently proposed classification scheme codifies seven primary tauopathies, and two secondary tauopathies under the umbrella of neurodegenerative diseases, each with a unique constellation of regional vulnerability and histomorphology of tau aggregates that define them [1, 2]. Pathological accumulation of hyperphosphorylated tau is also described in various infectious/post-infectious, metabolic, genetic/chromosomal, neoplastic/hamartomatous, and myopathic diseases [12]. Given the complexity and morphological overlap, diagnosing these diseases is a challenge for neuropathologists, and commands a high degree of expertise.

Microscopic analysis of stained postmortem sections by a trained expert remains the only modality of confirmatory diagnosis of tauopathies. Despite the continuous effort and improvements in the field, the analyses required for definitive diagnosis and subtyping of neurodegenerative diseases remain highly time- and cost-consuming and are subject to a substantial degree of inter- and intra-observer variability, thus lacking overall accuracy and precision. The gold standard for histomorphological assessment of tau burden and progression in Alzheimer’s disease is the Braak staging system, which focuses on the hierarchical sequence of tau accumulation, but not a quantitative measurement of tau burden, although distribution and qualitative NFT and thread density are correlated in this staging system [13]. Despite this limitation, the Braak staging system has been widely accepted and adopted for decades for its simplicity and robustness. Recent interest in differential semi-quantitative assessment of tau burden in AD is exemplified in the work of Jellinger [14]. Further, various stages of intracellular pathologic tau accumulation are described (e.g., pre-tangles, mature NFTs, and so-called “ghost” tangles—the remnants of the tau fibrillary scaffold after neuronal cell death; Fig. 1). The Braak staging approach does not address these features, and thus inherently lacks granularity and quantification. At the same time, the field of diagnostic neuropathology is facing challenges related to the overall lack of accuracy, demanded by the ever-evolving research and healthcare standards, and discrepancies with clinicopathological correlations, with a recognized need to address these issues [15].

Fig. 1 Morphological forms of neurofibrillary tangles (NFT) and progression. Tau is a microtubule-associated protein with normal roles in cytoskeletal stability and synaptic function. Early in disease progression, abnormal hyperphosphorylated tau aggregates (“oligomers”) accumulate as pre-NFT visible by immunohistochemistry as fine granular puncta in neurons. Tau oligomers are proposed to propagate from cell to cell. Aggregates coalesce to form fibrillary inclusions as mature intracellular NFT (iNFT). Neurites begin to die back or collapse and synaptic failure ensues. Cell death leaves only the extracellular aggregate remnant (eNFT), sometimes referred to as a tombstone or ghost tangle Full size image

Recently, there has been an increasing interest in developing computational methods to assist the pathologist in histological analysis via digital microscopic whole slide images (WSI). This is primarily intended to reduce the human error rate and bring about uniformity and accuracy in pathological diagnosis [16]. One of the approaches that has been anticipated and sought after for nearly half a century is artificial intelligence (AI) [17, 18]. The most advanced AI, called deep learning (DL), is now used for complex tasks such as speech recognition, language translation, and image recognition and interpretation [19,20,21]. Litjens et al. provide a comprehensive survey of published studies on the use of AI/DL in medical image analysis including WSI in pathology [17]. Although machine learning-based methods have had limited application in diagnostic pathology to date, due to the variability of laboratory standards and outcomes, and lack of reliable computer-backed platforms, advances have been made recently. The relevance and potential of automated classification algorithms in surgical pathology are exemplified by its application to the histologic grading and progression of breast and prostate cancer [17, 22, 23]. These endeavors pave a way toward increased use of machine learning for improving stratification, characterization, and quantification for many other disease processes, including the neuropathological assessment of tauopathies and AD cohorts. To date, no datasets derived from the application of machine-based learning to neurodegenerative disease are available.

We aimed to develop and test a novel DL algorithm using convolutional neural networks [20] that would be able to recognize, classify, and quantify diagnostic elements of tauopathies on WSI of postmortem human brain tissue specimens from patients with tau-associated neurodegenerative conditions in order to better stratify patients for clinical and other correlative studies (Fig. 2). In this study, we focused on the development, validation, and testing of the DL algorithms for recognition and quantification of NFT in an array of tauopathies. This will allow us to apply these trained networks for larger disease-specific cohorts and to generate quantitative data for clinicopathological correlations, as well as for molecular and genetic studies, and enable further diagnostic and therapeutic strategies.