In recent years there has been growing interest in masking that cannot be attributed to interactions in the cochlea-so--called informational masking (IM). Similarity in the acoustic properties of target and masker and uncertainty regarding the masker are the two major factors identified with IM. These factors involve quite different manipulations of signals and are believed to entail fundamentally different processes resulting in IM. Here, however, evidence is presented that these factors affect IM through their mutual influence on a single factor-the information divergence of target and masker given by Simpson-Fitter's da [Lutfi et al. (2012). J. Acoust. Soc. Am. 132, EL109-113]. Four experiments are described involving multitone pattern discrimination, multi-talker word recognition, sound-source identification, and sound localization. In each case standard manipulations of masker uncertainty and target-masker similarity (including the covariation of target-masker frequencies) are found to have the same effect on performance provided they produce the same change in da. The function relating d(') performance to da, moreover, appears to be linear with constant slope across listeners. The overriding dependence of IM on da is taken to reflect a general principle of perception that exploits differences in the statistical structure of signals to separate figure from ground.

Two approaches to the automated detection of alarm sounds are compared, one based on a change in overall sound level (RMS), the other a change in periodicity, as given by the power of the normalized autocorrelation function (PNA). Receiver operating characteristics in each case were obtained for different exemplars of four classes of alarm sounds (bells/chimes, buzzers/beepers, horns/whistles, and sirens) embedded in four noise backgrounds (cafeteria, park, traffic, and music). The results suggest that PNA combined with RMS may be used to improve current alarm-sound alerting technologies for the hard-of-hearing.

Research on hearing has long been challenged with understanding our exceptional ability to hear out individual sounds in a mixture (the so-called cocktail party problem). Two general approaches to the problem have been taken using sequences of tones as stimuli. The first has focused on our tendency to hear sequences, sufficiently separated in frequency, split into separate cohesive streams (auditory streaming). The second has focused on our ability to detect a change in one sequence, ignoring all others (auditory masking). The two phenomena are clearly related, but that relation has never been evaluated analytically. This article offers a detection-theoretic analysis of the relation between multitone streaming and masking that underscores the expected similarities and differences between these phenomena and the predicted outcome of experiments in each case. The key to establishing this relation is the function linking performance to the information divergence of the tone sequences, DKL (a measure of the statistical separation of their parameters). A strong prediction is that streaming and masking of tones will be a common function of DKL provided that the statistical properties of sequences are symmetric. Results of experiments are reported supporting this prediction.

Background: Cytomorphology is the gold standard for quick assessment of peripheral blood (PB) and bone marrow samples in hematological neoplasms and is used to orchestrate specific diagnostics. Artificial Intelligence (AI) promises to provide an unbiased way of interrogating blood smear data as reproducibility varies across labs. This is a prospective clinical study (ClinicalTrials.gov Identifier: NCT04466059) conducted on our approach outlined at ASH 2020. Aim: Use an AI model to classify cell images to produce differential counts of PB smears side-by-side to routine diagnostics. Methods: We enrolled 10,082 patient samples which were sent to our lab between 01/2021 and 07/2021 for cytomorphology with a suspected hematologic neoplasm. Blood smears were differentiated by highly skilled technicians (median 5y in lab) and all were reviewed by hematologists. In parallel, all samples were scanned on a MetaSystems (Altlussheim, Germany) Metafer Scanning System (Zeiss (Oberkochen, Germany) Axio Imager.Z2 microscope, automatic slide feeder). Areas of interest were defined and leukocyte positions were flagged by pre-scan in 10x magnification followed by high resolution scan in 40x to generate cell images for analysis. We set up a supervised Machine Learning model based on ImageNet-pretrained Xception using Amazon Sagemaker (AS) and trained it on 8,425 carefully annotated color images to identify 21 predefined classes (including 1 garbage class). Overall accuracy of this model against hold-out-set (10%) was 96%. The algorithm consumes 144x144pixel cell images and produces probability scores (PS) for each class in every image. Results: For routine diagnostics in median 100 cells/sample (range 82 - 103) were differentiated manually, overall 988,130. The automated process gathered 500 cell images/sample (range 101 - 500), overall 4,937,389. Average capture times for 500 cells: 4:37 min. Cropped images were uploaded to a cloud storage and exposed to an AS endpoint to initiate classification and the computation of a PS for each of the predefined 21 classes in the model. For the study we only considered images with a probability of at least 90% (n=3,781,670/4,937,389) and excluded normoblasts, smudge cells and images identified as garbage (together n=2,120,258). Final diagnosis included: no lymphoma detectable (2,186), MDS (1,152), AML (369), in these 11 APL, MPN (658), CLL (558), other mature B-cell neoplasms (377), CML (326), multiple myeloma (155), but also rare entities such as hairy cell leukemia variant (2) or PPBL (3). Comparing the benign normal cells in peripheral blood we identified (all values normalized) segmented neutrophils (manual (M): 516,648=52% vs AI: 882,538=53%), eosinophils (M: 24,860=2.52% vs. AI: 55,699=3.36%), basophils (M: 7159=0,72% vs. AI: 11,957=0,72%), monocytes (M: 74,113=7.5% vs. AI: 110,126=6.64%), lymphocytes (M: 313,518=31.7% vs. AI: 399,249=24%). Pathogenic blasts were detected in 16,048 (0.97%) images by AI (M: 16,290=1.65%). In routine diagnostics 536 cases with blast cells, including "questionable blasts" were identified. The AI identified 493 (91%) of these cases. At least one atypical/malignant lymphocyte was found in 2,323 samples manually, out of which the AI identified 2,279 (98%). In few cases manual differentiation relies on the number of pathogenic cells from an immunophenotyping analysis, which the AI does not had. During the course of the study by chance we identified at least 3 instances, were the AI detected pathogenic cells (blasts, atypical promyelocytes (APL) or bilobulated promyelocytes (APL-v)) which were initially missed manually (in some case WBC below .5 G/l) or flagged during subsequent immunophenotyping/molecular genetic analysis. Upon manually revisiting the smear, we could verify the presence of the AI-anticipated cells, revealing the higher sensitivity of the 5 time increase in cells/sample investigated by AI and power of algorithms. Conclusion: We present data of a prospective, blinded clinical study comparing blood smear analysis between humans and AI head-to-head. The concordance is extremely high with 95% for pathogenic cases. Misclassified cells are used for retraining to continuously improve the model and benefit from large datasets even for rare cell types. The model's cloud based implementation makes it easy to connect scanning devices for automated, unbiased classification. Disclosures Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Kern: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership.

Background: Cytomorphology is the gold standard for quick assessment of peripheral blood and bone marrow samples in hematological neoplasms. It is a broadly-accepted method for orchestrating more specific diagnostics including immunophenotyping or genetics. Inter-/intra-observer-reproducibility of single cell classification is only 75 to 90%. Only a limited number of cells (100 - 500 cells/smear) is read in a time-consuming procedure. Machine learning (ML) is more reliable where human skills are limited, i.e. in handling large amounts of data or images. We here tested ML to differentiate peripheral blood leukocytes in a high throughput hematology laboratory. Aim: To establish an ML-based cell classifier capable of identifying healthy and pathologic cells in digitalized peripheral blood smear scans at an accuracy competitive with or outperforming human expert level. Methods: We selected >2,600 smears out of our unique archive of > 250,000 peripheral blood smears from hematological neoplasms. Depending on quality, we scanned up to 1,000 single cell images per smear. For image acquisition, a Metafer Scanning System (Zeiss Axio Imager.Z2 microscope, automatic slide feeder and automatic oiling device) from MetaSystems (Altlussheim, GER) was used. Areas of interest were defined by pre-scan in 10x magnification followed by high resolution scan in 40x to generate cell images for analysis. Average capture times for 300/500 cells were 3:43/4:37 min We set up a supervised ML-learning model using colour images (144x144 pixels) as input, outputting predicted probabilities of 21 predefined classes. We used ImageNet-pretrained Xception as our base model. We trained, evaluated and deployed the model using Amazon SageMaker on a subset of 82,974 images randomly selected from 514,183 cells captured and labelled for this study. 20 different cell types and one garbage class were classified. We included cell type categories referring to the critical importance of detecting rare leukemia subtypes (e.g. APL). Numbers of images from respective 21 classes ranged from 1,830 to 14,909 (median: 2,945). Minority classes were up-sampledto handle imbalances. Each picture was labelled by highly skilled technicians (median years practicing in this laboratory: 5) and two independent hematologists (median years at microscope: 20). Results: On a separate test set of 8,297 cells, our classifier was able to predict any of the five cell types occurring in the peripheral blood of healthy individuals (PMN, lymphocytes, monocytes, eosinophils, basophils) at very high median accuracy (97.0%) Median prediction accuracy of 15 rare or pathological cell types was 91.3%. For six critical pathological cell forms (myeloblasts, atypical/bilobulated promyelocytes in APL/APLv, hairy cells, lymphoma cells,plasma cells), median accuracy was 93.4% (sensitivity 93.8%). We saw a very high "T98 accuracy" for these cell types (98.5%) which is the accuracy of cell type predictions with prediction probability >0.98 (achieved in 2231/2417 cases), implicating that critical cells predicted with probability <0.98 should be flagged for human expert validation with priority. For all 21 classes median accuracy was 91.7%. Accuracy was lower for cells representing consecutive steps of maturation, e.g. promyelo-/myelo-/metamyelocytes, reproducing inconsistencies from the human-built phenotypic classification system (s.Fig.). Conclusions: We demonstrate an automated workflow using automatic microscopic cell capturing and ML-driven cell differentiation in samples of hematologic patients. Reproducibility, accuracy, sensitivity and specificity are above 90%, for many cell types above 98%. By flagging suspicious cells for humanvalidation, this tool can support even experienced hematology professionals, especially in detecting rare cell types. Given an appropriate scanning speed, it clearly outperforms human investigators in terms of examination time and number of differentiated cells. An ML-based intelligence can make its skills accessible to hematology laboratories on site or after upload of scanned cell images, independent of time/location. A cloud-based infrastructure is available. A prospective head to head challenge between ML-based classifier and human experts comparing sensitivity and accuracy for detection of all cell classes in peripheral blood will be tested to proof suitability for routine use (NCT 4466059). Figure Disclosures Heo: AWS: Current Employment. Wetton:AWS: Current Employment. Drescher:MetaSystems: Current Employment. Hänselmann:MetaSystems: Current Employment. Lörch:MetaSystems: Current equity holder in private company.