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Artificial intelligence in neuropathology: deep learning-based assessment of tauopathy
Signaevsky M, Prastawa M, Farrell K, Tabish N, Baldwin E, Han N, Iida MA, Koll J et al.. Laboratory investigation; a journal of technical methods and pathology, 2019-02-15
Topic: Brain proteins, microglia and lab models
Accumulation of abnormal tau in neurofibrillary tangles (NFT) occurs in Alzheimer disease (AD) and a spectrum of tauopathies. These tauopathies have diverse and overlapping morphological phenotypes that obscure classification and quantitative assessments. Recently, powerful machine learning-based approaches have emerged, allowing the recognition and quantification of pathological changes from digital images. Here, we applied deep learning to the neuropathological assessment of NFT in postmortem human brain tissue to develop a classifier capable of recognizing and quantifying tau burden. The histopathological material was derived from 22 autopsy brains from patients with tauopathies. We used a custom web-based informatics platform integrated with an in-house information management system to manage whole slide images (WSI) and human expert annotations as ground truth. We utilized fully annotated regions to train a deep learning fully convolutional neural network (FCN) implemented in PyTorch against the human expert annotations. We found that the deep learning framework is capable of identifying and quantifying NFT with a range of staining intensities and diverse morphologies. With our FCN model, we achieved high precision and recall in naive WSI semantic segmentation, correctly identifying tangle objects using a SegNet model trained for 200 epochs. Our FCN is efficient and well suited for the practical application of WSIs with average processing times of 45 min per WSI per GPU, enabling reliable and reproducible large-scale detection of tangles. We measured performance on test data of 50 pre-annotated regions on eight naive WSI across various tauopathies, resulting in the recall, precision, and an F1 score of 0.92, 0.72, and 0.81, respectively. Machine learning is a useful tool for complex pathological assessment of AD and other tauopathies. Using deep learning classifiers, we have the potential to integrate cell- and region-specific annotations with clinical, genetic, and molecular data, providing unbiased data for clinicopathological correlations that will enhance our knowledge of the neurodegeneration.
Key findings
- We found that the deep learning framework is capable of identifying and quantifying NFT with a range of staining intensities and diverse morphologies.
Study details
- Evidence strengthLow
How this study scored 38/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 22 of the 58 points we could judge it on, which is 38 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Laboratory investigation; a journal of technical methods and pathology) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 2 / 5 | 2019 — within 10 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 30770886) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 22 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 22 of the 58 points we could judge it on is 38 out of 100. |
| Total | 38 / 100 | — |
What each of these 20 components measures, and what this score is not →
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APA 7th
Signaevsky, M., Prastawa, M., Farrell, K., Tabish, N., Baldwin, E., Han, N., et al. (2019). Artificial intelligence in neuropathology: deep learning-based assessment of tauopathy. Laboratory investigation; a journal of technical methods and pathology. https://doi.org/10.1038/s41374-019-0202-4
Vancouver
Signaevsky M, Prastawa M, Farrell K, Tabish N, Baldwin E, Han N, et al. Artificial intelligence in neuropathology: deep learning-based assessment of tauopathy. Laboratory investigation; a journal of technical methods and pathology. 2019. doi:10.1038/s41374-019-0202-4. PMID: 30770886.
BibTeX
@article{signaevsky2019-30770886,
author = {Signaevsky, M. and Prastawa, M. and Farrell, K. and Tabish, N. and Baldwin, E. and Han, N. and Iida, M. A. and Koll, J. and others},
title = {{Artificial intelligence in neuropathology: deep learning-based assessment of tauopathy}},
journal = {Laboratory investigation; a journal of technical methods and pathology},
year = {2019},
doi = {10.1038/s41374-019-0202-4},
pmid = {30770886},
url = {https://doi.org/10.1038/s41374-019-0202-4}
}