Development of an explainable AI system using routine clinical parameters for rapid differentiation of inflammatory conditions.

Introduction: Inflammatory conditions in patients have various causes and require different treatments. Bacterial infections are treated with antibiotics, while these medications are ineffective against viral infections. Autoimmune diseases and graft-versus-host disease (GVHD) after allogeneic stem cell transplantation, require immunosuppressive therapies such as glucocorticoids, which may be contraindicated in other inflammatory states. In this study, we employ a combination of straightforward blood tests to devise an explainable artificial intelligence (XAI) for distinguishing between bacterial infections, viral infections, and autoimmune diseases/graft-versus-host disease. Patients and methods: We analysed peripheral blood from 80 patients with inflammatory conditions and 38 controls. Complete blood count, CRP analysis, and a rapid flow cytometric test for myeloid activation markers CD169, CD64, and HLA-DR were utilized. A two-step XAI distinguished firstly with C5.0 rules pruned by ABC analysis between controls and inflammatory conditions and secondly between the types of inflammatory conditions with a new bivariate decision tree using the Simpson impurity function. Results: Inflammatory conditions were distinguished using an XAI, achieving an overall accuracy of 81.0% (95%CI 72 - 87%). Bacterial infection (N = 30), viral infection (N = 26), and autoimmune diseases/GVHD (N = 24) were differentiated with accuracies of 90.3%, 80.0%, and 79.0%, respectively. The most critical parameter for distinguishing between controls and inflammatory conditions was the expression of CD64 on neutrophils. Monocyte count and expression of CD169 were most crucial for the classification within the inflammatory conditions. Conclusion: Treatment decisions for inflammatory conditions can be effectively guided by XAI rules, straightforward to implement and based on promptly acquired blood parameters.

Prediction of Clinical Outcomes with Explainable Artificial Intelligence in Patients with Chronic Lymphocytic Leukemia.

BACKGROUND: The International Prognostic Index (IPI) is applied to predict the outcome of chronic lymphocytic leukemia (CLL) with five prognostic factors, including genetic analysis. We investigated whether multiparameter flow cytometry (MPFC) data of CLL samples could predict the outcome by methods of explainable artificial intelligence (XAI). Further, XAI should explain the results based on distinctive cell populations in MPFC dot plots. METHODS: We analyzed MPFC data from the peripheral blood of 157 patients with CLL. The ALPODS XAI algorithm was used to identify cell populations that were predictive of inferior outcomes (death, failure of first-line treatment). The diagnostic ability of each XAI population was evaluated with receiver operating characteristic (ROC) curves. RESULTS: ALPODS defined 17 populations with higher ability than the CLL-IPI to classify clinical outcomes (ROC: area under curve (AUC) 0.95 vs. 0.78). The best single classifier was an XAI population consisting of CD4+ T cells (AUC 0.78; 95% CI 0.70-0.86; p < 0.0001). Patients with low CD4+ T cells had an inferior outcome. The addition of the CD4+ T-cell population enhanced the predictive ability of the CLL-IPI (AUC 0.83; 95% CI 0.77-0.90; p < 0.0001). CONCLUSIONS: The ALPODS XAI algorithm detected highly predictive cell populations in CLL that may be able to refine conventional prognostic scores such as IPI.

Identification of critical hemodilution by artificial intelligence in bone marrow assessed for minimal residual disease analysis in acute myeloid leukemia: The Cinderella method.

Minimal residual disease (MRD) detection is a strong predictor for survival and relapse in acute myeloid leukemia (AML). MRD can be either determined by molecular assessment strategies or via multiparameter flow cytometry. The degree of bone marrow (BM) dilution with peripheral blood (PB) increases with aspiration volume causing consecutive underestimation of the residual AML blast amount. In order to prevent false-negative MRD results, we developed Cinderella, a simple automated method for one-tube simultaneous measurement of hemodilution in BM samples and MRD level. The explainable artificial intelligence (XAI) Cinderella was trained and validated with the digital raw data of a flow cytometric "8-color" AML-MRD antibody panel in 126 BM and 23 PB samples from 35 patients. Cinderella predicted PB dilution with high accordance compared to the results of the Holdrinet formula (Pearson's correlation coefficient r = 0.94, R2  = 0.89, p < 0.001). Unlike conventional neuronal networks Cinderella calculated the distributions of 12 different cell populations that were assigned to true hematopoietic counterparts as a human in the loop (HIL) approach. Besides characteristic BM cells such as myelocytes and myeloid progenitor cells the XAI identified discriminating populations, which were not specific for BM or PB (e.g., T cell/NK cell subpopulations and CD45 negative cells) and considered their frequency differences. Thus, Cinderella represents a HIL-XAI algorithm capable to calculate the degree of hemodilution in BM samples with an AML MRD immunophenotype panel. It is explicable, transparent, and paves a simple way to prevent false negative MRD reports.

A Bioinformatics View on Acute Myeloid Leukemia Surface Molecules by Combined Bayesian and ABC Analysis.

"Big omics data" provoke the challenge of extracting meaningful information with clinical benefit. Here, we propose a two-step approach, an initial unsupervised inspection of the structure of the high dimensional data followed by supervised analysis of gene expression levels, to reconstruct the surface patterns on different subtypes of acute myeloid leukemia (AML). First, Bayesian methodology was used, focusing on surface molecules encoded by cluster of differentiation (CD) genes to assess whether AML is a homogeneous group or segregates into clusters. Gene expressions of 390 patient samples measured using microarray technology and 150 samples measured via RNA-Seq were compared. Beyond acute promyelocytic leukemia (APL), a well-known AML subentity, the remaining AML samples were separated into two distinct subgroups. Next, we investigated which CD molecules would best distinguish each AML subgroup against APL, and validated discriminative molecules of both datasets by searching the scientific literature. Surprisingly, a comparison of both omics analyses revealed that CD339 was the only overlapping gene differentially regulated in APL and other AML subtypes. In summary, our two-step approach for gene expression analysis revealed two previously unknown subgroup distinctions in AML based on surface molecule expression, which may guide the differentiation of subentities in a given clinical-diagnostic context.

Flow cytometry datasets consisting of peripheral blood and bone marrow samples for the evaluation of explainable artificial intelligence methods.

Three different Flow Cytometry datasets consisting of diagnostic samples of either peripheral blood (pB) or bone marrow (BM) from patients without any sign of bone marrow disease at two different health care centers are provided. In Flow Cytometry, each cell rapidly passes through a laser beam one by one, and two light scatter, and eight surface parameters of more than 100.000 cells are measured per sample of each patient. The technology swiftly characterizes cells of the immune system at the single-cell level based on antigens presented on the cell surface that are targeted by a set of fluorochrome-conjugated antibodies. The first dataset consists of N=14 sample files measured in Marburg and the second dataset of N=44 data files measured in Dresden, of which half are BM samples and half are pB samples. The third dataset contains N=25 healthy bone marrow samples and N=25 leukemia bone marrow samples measured in Marburg. The data has been scaled to log between zero and six and used to identify cell populations that are simultaneously meaningful to the clinician and relevant to the distinction of pB vs BM, and BM vs leukemia. Explainable artificial intelligence methods should distinguish these samples and provide meaningful explanations for the classification without taking more than several hours to compute their results. The data described in this article are available in Mendeley Data [1].

Determination of CD43 and CD200 surface expression improves accuracy of B-cell lymphoma immunophenotyping.

BACKGROUND: The Matutes score (MS) was proposed to differentiate chronic lymphocytic leukemia (CLL) from other B-cell non-Hodgkin lymphomas (B-NHLs). However, ambiguous immunophenotypes are common and remain a diagnostic challenge. Therefore, we evaluated the diagnostic benefit of measuring CD200 and CD43 expression together with the standard MS antigens. METHODS: 138 lymphoma patient samples and a validation cohort of 138 additive samples were classified according to the standard MS and further assigned with one or two additional points, for high CD200 and/or CD43 expression levels. The "classical" MS and the "Matutes score-extended" (MS-e) were categorized as high (4-5/6-7), intermediate (2-3/4-5), and low (0-1/0-3). Samples were reclassified into the MS-e with focus on ambiguous cases with an intermediate "classical" MS. RESULTS: A total of 35 of 138 (25.4%) patient samples were assigned to the intermediate MS group and confirmed by histopathological reports as CLL (14/40.0%) and B-NHLs other than CLL (21/60%). MS-e analysis identified 13 of 14 (92.9%) of CLL cases (MS-e 4-5) and 18/21 (85.7%) non-CLL cases (MS-e ≤ 3) correctly. Overall, the sensitivity of the CLL diagnosis was significantly increased by application of MS-e compared to the "classical" MS (98.8% vs. 82.7%; p = 0.0009), while specificity of both methods was almost equal (94.7% vs. 98.3%; p = 0.4795). Of note, sole measurement of CD43 and CD200 on B-cells sufficiently differentiated CLL from non-CLL with a test accuracy superior to the "classical" MS (F1 score 96.2 vs. 93.6). CONCLUSION: CD200 and CD43 have a high informative value in diagnostic immunophenotyping and facilitate the separation of CLL from other B-NHLs particularly in ambiguous cases.

Clustering benchmark datasets exploiting the fundamental clustering problems.

The Fundamental Clustering Problems Suite (FCPS) offers a variety of clustering challenges that any algorithm should be able to handle given real-world data. The FCPS consists of datasets with known a priori classifications that are to be reproduced by the algorithm. The datasets are intentionally created to be visualized in two or three dimensions under the hypothesis that objects can be grouped unambiguously by the human eye. Each dataset represents a certain problem that can be solved by known clustering algorithms with varying success. In the R package "Fundamental Clustering Problems Suite" on CRAN, user-defined sample sizes can be drawn for the FCPS. Additionally, the distances of two high-dimensional datasets called Leukemia and Tetragonula are provided here. This collection is useful for investigating the shortcomings of clustering algorithms and the limitations of dimensionality reduction methods in the case of three-dimensional or higher datasets. This article is a simultaneous co-submission with Swarm Intelligence for Self-Organized Clustering [1].