Temporal dynamics of isolation calls emitted by pups in environmental and genetic mouse models of autism spectrum disorder

Introduction

Environmental and genetic factors contribute to the increased risk for neurodevelopmental disorders, including deficits in the development of social communication. In the mouse, ultrasonic vocalizations emitted by the pup stimulate maternal retrieval and potentiate maternal care. Therefore, isolation induced ultrasonic vocalization emitted by pups provides a means to evaluate deficits in communication during early development, before other ways of communication are apparent. Previous studies in our labs showed that gestational exposure to the pesticide chlorpyrifos (CPF) and the Methylenetetrahydrofolate (Mthfr)-knock-out mice are associated with impaired social preference, restricted or repetitive behavior and altered spectral properties of pups' ultrasonic vocalization. In this study, we explore the temporal dynamics of pups' vocalization in these Autism spectrum disorder (ASD) models.

Methods

We utilized the maternal potentiation protocol and analyzed the time course of pup vocalizations following isolation from the nest. Two models of ASD were studied: gestational exposure to the pesticide CPF and the Mthfr-knock-out mice.

Results

Vocalization emitted by pups of both ASD models were dynamically modified in quantity and spectral structure within each session and between the two isolation sessions. The first isolation session was characterized by a buildup of call quantity and significant effects of USV spectral structure variables, and the second isolation session was characterized by enhanced calls and vocalization time, but minute effect on USV properties. Moreover, in both models we described an increased usage of harmonic calls with time during the isolation sessions.

Discussion

Communication between two or more individuals requires an interplay between the two sides and depends on the response and the time since the stimulus. As such, the presence of dynamic changes in vocalization structure in the control pups, and the alteration observed in the pups of the ASD models, suggest impaired regulation of vocalization associated with the environmental and genetic factors. Last, we propose that temporal dynamics of ultrasonic vocalization communication should be considered in future analysis in rodent models of ASD to maximize the sensitivity of the study of vocalizations.

Isolation-Induced Ultrasonic Vocalization in Environmental and Genetic Mice Models of Autism

Studies in rodent models suggest that calls emitted by isolated pups serve as an early behavioral manifestation of communication deficits and autistic like behavior. Previous studies in our labs showed that gestational exposure to the pesticide chlorpyrifos (CPF) and the Mthfr-knock-out mice are associated with impaired social preference and restricted or repetitive behavior. To extend these studies, we examine how pup communication via ultrasonic vocalizations is altered in these ASD models. We implemented an unsupervised hierarchical clustering method based on the spectral properties of the syllables in order to exploit syllable classification to homogeneous categories while avoiding over-categorization. Comparative exploration of the spectral and temporal aspects of syllables emitted by pups in two ASD models point to the following: (1) Most clusters showed a significant effect of the ASD factor on the start and end frequencies and bandwidth and (2) The highest percent change due to the ASD factor was on the bandwidth and duration. In addition, we found sex differences in the spectral and temporal properties of the calls in both control groups as well as an interaction between sex and the gene/environment factor. Considering the basal differences in the characteristics of syllables emitted by pups of the C57Bl/6 and Balb/c strains used as a background in the two models, we suggest that the above spectral-temporal parameters start frequency, bandwidth, and duration are the most sensitive USV features that may represent developmental changes in ASD models.

Transfer Representation Learning using Inception-V3 for the Detection of Masses in Mammography

Breast cancer is the most prevalent cancer among women. The most common method to detect breast cancer is mammography. However, interpreting mammography is a challenging task that requires high skills and is timeconsuming. In this work, we propose a computer-aided diagnosis (CAD) scheme for mammography based on transfer representation learning using the Inception-V3 architecture. We evaluate the performance of the proposed scheme using the INBreast database, where the features are extracted from different layers of the architecture. In order to cope with the small dataset size limitation, we expand the training dataset by generating artificial mammograms and employing different augmentation techniques. The proposed scheme shows great potential with a maximal area under the receiver operating characteristics curve of 0.91.

A new computer-aided detection scheme based on assessment of local bilateral mammographic feature asymmetry - a preliminary evaluation

Accurate segmentation of breast lesions depicting on two-dimensional projection mammograms has been proven very difficult and unreliable. In this study we investigated a new approach of a computer-aided detection (CAD) scheme of mammograms without lesion segmentation. Our scheme was developed based on the detection and analysis of region-of-interest (ROI)-based bilateral mammographic tissue or feature asymmetry. A bilateral image registration, image feature selection process, and naïve Bayes linear classifier were implemented in CAD scheme. CAD performance predicting the likelihood of either an ROI or a subject (case) being abnormal was evaluated using 161 subjects from the mini-MIAS database and a leave-one-out testing method. The results showed that areas under receiver operating characteristic (ROC) curves were 0.87 and 0.72 on the ROI-based and case-based evaluation, respectively. The study demonstrated that using ROI-based bilateral mammographic tissue asymmetry can provide supplementary information with high discriminatory power in order to improve CAD performance.

Engineered spatial inversion symmetry breaking in an oxide heterostructure built from isosymmetric room-temperature magnetically ordered components

Weak ferromagnetism and piezoelectricity are combined in an oxide heterostructure at room temperature.

Classification of multichannel EEG patterns using parallel hidden Markov models

In this paper, a parallel hidden-Markov-model (PHMM)-based approach is proposed for the problem of multichannel electroencephalogram (EEG) patterns classification. The approach is based on multi-channel representation of the EEG signals using a parallel combination of HMMs, where each model represents a particular channel. The performance of the proposed algorithm is studied using an artificial EEG database, and two real EEG databases: a database of two classes of EEGs elicited during a task of imagery of hand upward and downward movements of a computer screen cursor (db Ia), and a database of two classes of sensorimotor EEGs elicited during a feedback-regulated left-right motor imagery task (db III). The results show that the proposed algorithm outperforms other commonly used methods with classification rate improvement of 2 and 10% for db Ia and db III, respectively. In addition, the proposed method outperforms a support vector machine classifier with a linear kernel, when both classifiers utilize the same feature set. The results also show that a model architecture which includes a left-to-right scheme with no skips, five states and three Gaussians, outperforms the other tested architectures due to the fact that it allows a better modeling of the temporal sequencing of the EEG components.

Improving performance of computer-aided detection of masses by incorporating bilateral mammographic density asymmetry: an assessment

RATIONALE AND OBJECTIVES

Bilateral mammographic density asymmetry is a promising indicator in assessing risk of having or developing breast cancer. This study aims to assess the performance improvement of a computer-aided detection (CAD) scheme in detecting masses by incorporating bilateral mammographic density asymmetrical information.

MATERIALS AND METHODS

A testing dataset containing 2400 full-field digital mammograms (FFDM) acquired from 600 examination cases was established. Among them, 300 were positive cases with verified cancer associated with malignant masses and 300 were negative cases. Two computerized schemes were applied to process images of each case. The first single-image based CAD scheme detected suspicious mass regions and the second scheme computed average and difference of mammographic tissue density depicted between the left and right breast. A fusion method based on rotation of the CAD scoring projection reference axis was then applied to combine CAD-generated mass detection scores and either the computed average or difference (asymmetry) of bilateral mammographic density scores. The CAD performance levels with and without incorporating mammographic density information were evaluated and compared using a free-response receiver operating characteristic type data analysis method.

RESULTS

CAD achieved a case-based mass detection sensitivity of 0.74 and a region-based sensitivity of 0.56 at a false-positive rate of 0.25 per image. By fusing the CAD and bilateral mammographic density asymmetry scores, the case-based and region-based sensitivity levels of the CAD scheme were increased to 0.84 and 0.69, respectively, at the same false-positive rate. Fusion with average mammographic density only slightly increased CAD sensitivity to 0.75 (case-based) and 0.57 (region-based).

CONCLUSIONS

This study indicated that 1) bilateral mammographic density asymmetry was a stronger indicator of the case depicting suspicious masses than the average density computed from two breasts and 2) fusion between the conventional CAD scores and bilateral mammographic density asymmetry information could substantially increase CAD performance in mass detection.

Improving the performance of computer-aided detection of subtle breast masses using an adaptive cueing method

Current computer-aided detection (CAD) schemes for detecting mammographic masses have several limitations including high correlation with radiologists' detection and cueing most subtle masses only on one view. To increase CAD sensitivity in cueing more subtle masses that are likely missed and/or overlooked by radiologists without increasing false-positive rates, we investigated a new case-dependent cueing method by combining the original CAD-generated detection scores with a computed bilateral mammographic density asymmetry index. Using the new method, we adaptively raise the CAD-generated scores of the regions detected on 'high-risk' cases to cue more subtle mass regions and reduce the CAD scores of the regions detected on 'low-risk' cases to discard more false-positive regions. A testing dataset involving 78 positive and 338 negative cases was used to test this adaptive cueing method. Each positive case involves two sequential examinations in which the mass was detected in 'current' examination and missed in 'prior' examination but detected in a retrospective review by radiologists. Applying to this dataset, a pre-optimized CAD scheme yielded 75% case-based and 55% region-based sensitivity on 'current' examinations at a false-positive rate of 0.25 per image. CAD sensitivity was reduced to 42% (case based) and 27% (region based) on 'prior' examinations. Using the new cueing method, case-based and region-based sensitivity could maximally increase 9% and 33% on the 'prior' examinations, respectively. The percentages of the masses cued on two views also increased from 27% to 65%. The study demonstrated that using this adaptive cueing method enabled us to help CAD cue more subtle cancers without increasing the false-positive cueing rate.

Automatic computerized endotracheal tube position verification: an animal model evaluation

BACKGROUND

Improper endotracheal tube positioning carries a high risk for morbidity and mortality; verification and confirmation of correct placement is necessary. We propose a computer-automated identification of endotracheal tube positioning using image analysis. The end product will not retain a monitor; rather, the acquired image will be automatically analyzed by a mini electronic processor.

METHODS

An algorithm that automatically analyzes images has been developed: it classifies images into esophagus, trachea, and carina. Image processing includes converting the image to grayscale and extracting and classifying into 1 class, on the basis of similarity to pretrained patterns. A prototypical video sensor mounted on an intubating stylet has also been assembled. This stylet was introduced into 10 bovine throats, and video images were gathered. Videos were analyzed and classified as carina, trachea, or esophagus. The videos were then introduced to the new algorithm. In each test cycle, 9 videos were used to train the algorithm, and the 10th was used as a benchmark. This procedure was repeated 10 times so that each video was used 9 times for teaching and 1 time for testing.

RESULTS

Ten videos were recorded, of which 1600 images were extracted (trachea: 490 images; carina: 550 images; and esophagus: 560 images). Only 1 esophageal image was classified as trachea (false positive 0.001%). Two carinal images and 22 tracheal images were recognized as esophagus (false negative 0.041%), sensitivity 0.98 and specificity 0.99. Twenty images of the carina were identified as trachea, and 25 images of the trachea were identified as the carina (false positive 0.045%, false negative 0.041%, sensitivity 0.96 and specificity 0.95).

CONCLUSION

A potential tube position verification system was assessed. High accuracy of the analysis algorithm was shown using nonperfused biological tissue, justifying further research.

A GMM-based breast cancer risk stratification using a resonance-frequency electrical impedance spectroscopy

PURPOSE

The authors developed and tested a multiprobe-based resonance-frequency-based electrical impedance spectroscopy (REIS) system. The purpose of this study was to preliminarily assess the performance of this system in classifying younger women into two groups, those ultimately recommended for biopsy during imaging-based diagnostic workups that followed screening and those rated as negative during mammography.

METHODS

A seven probe-based REIS system was designed, assembled, and is currently being tested in the breast imaging facility. During an examination, contact is made with the nipple and six concentric points on the breast skin. For each measurement channel between the center probe and one of the six external probes, a set of electrical impedance spectroscopy (EIS) signal sweeps is performed and signal outputs ranging from 200 to 800 kHz at 5 kHz interval are recorded. An initial subset of 174 examinations from an ongoing prospective clinical study was selected for this preliminary analysis. An initial set of 35 features, 33 of which represented the corresponding EIS signal differences between the left and right breasts, was established. A Gaussian mixture model (GMM) classifier was developed to differentiate between "positive" (biopsy recommended) cases and "negative" (nonbiopsy) cases. Selecting an optimal feature set was performed using genetic algorithms with an area under a receiver operating characteristic curve (AUC) as the fitness criterion.

RESULTS

The recorded EIS signal sweeps showed that, in general, negative (nonbiopsy) examinations have a higher level of electrical impedance symmetry between the two breasts than positive (biopsy) examinations. Fourteen features were selected by genetic algorithm and used in the optimized GMM classifier. Using a leave-one-case-out test, the GMM classifier yielded a performance level of AUC = 0.78, which compared favorably to other three widely used classifiers including support vector machine, classification tree, and linear discriminant analysis. These results also suggest that the REIS signal based GMM classifier could be used as a prescreening tool to correctly identify a fraction of younger women at higher risk of developing breast cancer (i.e., 47% sensitivity at 90% specificity).

CONCLUSIONS

The study confirms that asymmetry in electrical impedance characteristics between two breasts provides valuable information regarding the presence of a developing breast abnormality; hence, REIS data may be useful in classifying younger women into two groups of "average" and "significantly higher than average" risk of having or developing a breast abnormality that would ultimately result in a later imaging-based recommendation for biopsy.

Computerized prediction of risk for developing breast cancer based on bilateral mammographic breast tissue asymmetry

This study developed and assessed a computerized scheme to detect breast abnormalities and predict the risk of developing cancer based on bilateral mammographic tissue asymmetry. A digital mammography database of 100 randomly selected negative cases and 100 positive cases for having high-risk of developing breast cancer was established. Each case includes four images of cranio-caudal (CC) and medio-lateral oblique (MLO) views of the left and right breast. To detect bilateral mammographic tissue asymmetry, a pool of 20 computed features was assembled. A genetic algorithm was applied to select optimal features and build an artificial neural network based classifier to predict the likelihood of a test case being positive. The leave-one-case-out validation method was used to evaluate the classifier performance. Several approaches were investigated to improve the classification performance including extracting asymmetrical tissue features from either selected regions of interests or the entire segmented breast area depicted on bilateral images in one view, and the fusion of classification results from two views. The results showed that (1) using the features computed from the entire breast area, the classifier yielded the higher performance than using ROIs, and (2) using a weighted average fusion method, the classifier achieved the highest performance with the area under ROC curve of 0.781±0.023. At 90% specificity, the scheme detected 58.3% of high-risk cases in which cancers developed and verified 6-18 months later. The study demonstrated the feasibility of applying a computerized scheme to detect cases with high risk of developing breast cancer based on computer-detected bilateral mammographic tissue asymmetry.

Computer-aided detection of breast masses depicted on full-field digital mammograms: a performance assessment

OBJECTIVES

To investigate the feasibility of converting a computer-aided detection (CAD) scheme for digitised screen-film mammograms to full-field digital mammograms (FFDMs) and assessing CAD performance on a large database.

METHODS

The database included 6478 FFDM images acquired on 1120 females, with 525 cancer cases and 595 negative cases. The database was divided into five case groups: (1) cancer detected during screening, (2) interval cancers, (3) "high-risk" recommended for surgical excision, (4) recalled but negative and (5) negative (not recalled). A previously developed CAD scheme for masses depicted on digitised images was converted and re-optimised for FFDM images while keeping the same image-processing structure. CAD performance was analysed on the entire database.

RESULTS

The case-based sensitivity was 75.6% (397/525) for the current mammograms and 40.8% (42/103) for the prior mammograms deemed negative during clinical interpretation but "visible" during retrospective review. The region-based sensitivity was 58.1% (618/1064) for the current mammograms and 28.4% (57/201) for the prior mammograms. The CAD scheme marked 55.7% (221/397) and 35.7% (15/42) of the masses on both views of the current and the prior examinations, respectively. The overall CAD-cued false-positive rate was 0.32 per image, ranging from 0.29 to 0.51 for the five case groups.

CONCLUSION

This study indicated that (1) digitised image-based CAD can be converted for FFDMs while performing at a comparable, or better, level; (2) CAD detects a substantial fraction of cancers depicted on prior examinations, albeit most having been marked only on one view; and (3) CAD tends to mark more false-positive results on "difficult" negative cases that are more visually difficult for radiologists to interpret.

A preliminary evaluation of multi-probe resonance-frequency electrical impedance based measurements of the breast

RATIONALE AND OBJECTIVES

The aim of this study was to preliminarily assess the performance of a new, resonance-frequency electrical impedance spectroscopy (REIS) system in identifying young women who were recommended to undergo breast biopsy following imaging.

MATERIALS AND METHODS

A seven-probe REIS system was designed and assembled and is currently being prospectively tested. During examination, contact is made with the nipple and six concentric points on the breast skin. Signal sweeps are performed, and outputs ranging from 200 to 800 kHz at 5-kHz intervals are recorded. An initial set of 140 patients, including 56 who eventually had biopsies, 63 who had negative results on screening mammography, and 21 recalled for additional imaging but later determined to have negative results, was used. An initial set of 35 features, 33 representing impedance signal differences between breasts and two representing participant age and average breast density, was assembled and reduced by a genetic algorithm to 14. The performance of an artificial neural network-based classifier was assessed using a case-based leave-one-out method.

RESULTS

The substantially greater asymmetry between signals of mirror-matched regions ascertained from biopsy ("positive") compared to nonbiopsy ("negative") cases resulted in an artificial neural network classifier performance (area under the curve) of 0.830 ± 0.023. At 90% specificity, this classifier, optimized for "recommendation for biopsy" rather than "cancer," detected 30 REIS-positive cases (54%), including six of nine (67%) actual cancer cases and six of nine women (67%) recommended for surgical excision of high-risk lesions.

CONCLUSIONS

Asymmetry in impedance measurements between bilateral breasts may provide valuable discriminatory information regarding the presence of highly suspicious imaging-based findings.

Computer-aided detection of early interstitial lung diseases using low-dose CT images

This study aims to develop a new computer-aided detection (CAD) scheme to detect early interstitial lung disease (ILD) using low-dose computed tomography (CT) examinations. The CAD scheme classifies each pixel depicted on the segmented lung areas into positive or negative groups for ILD using a mesh-grid-based region growth method and a multi-feature-based artificial neural network (ANN). A genetic algorithm was applied to select optimal image features and the ANN structure. In testing each CT examination, only pixels selected by the mesh-grid region growth method were analyzed and classified by the ANN to improve computational efficiency. All unselected pixels were classified as negative for ILD. After classifying all pixels into the positive and negative groups, CAD computed a detection score based on the ratio of the number of positive pixels to all pixels in the segmented lung areas, which indicates the likelihood of the test case being positive for ILD. When applying to an independent testing dataset of 15 positive and 15 negative cases, the CAD scheme yielded the area under receiver operating characteristic curve (AUC = 0.884 ± 0.064) and 80.0% sensitivity at 85.7% specificity. The results demonstrated the feasibility of applying the CAD scheme to automatically detect early ILD using low-dose CT examinations.

Improving breast cancer risk stratification using resonance-frequency electrical impedance spectroscopy through fusion of multiple classifiers

This study aims to improve breast cancer risk stratification. A seven-probe resonance-frequency-based electrical impedance spectroscopy (REIS) system was designed, assembled, and utilized to establish a data set of examinations from 174 women. Three classifiers, including artificial neural network (ANN), support vector machine (SVM), and Gaussian mixture model (GMM), were independently developed to predict the likelihood of each woman to be recommended for biopsy. The performances of these classifiers were compared, and seven fusion methods for integrating these classifiers were investigated. The results showed that among the three classifiers, the ANN yielded the highest performance with an area under the curve (AUC) of 0.81 for the receiver operating characteristic (ROC), while SVM and GMM achieved AUCs of 0.80 and 0.78, respectively. Improvements of up to 3% were obtained using fusion of the three classifiers, with the largest improvement obtained using either a "minimum score" rule or a "weighted sum" rule. Comparing different combinations of two out of the three classifiers, the weighted sum rule provided the most robust and consistent results, with AUCs of 0.81, 0.83, and 0.82 for the different combinations of ANN and SVM, ANN and GMM, and SVM and GMM, respectively. Furthermore, at 90% specificity, the ANN, the weighted sum- and min rule-based classifiers, all detected 67% of the verified cancer cases as compared with 50, 50, and 60% detection of the high risk cases, respectively. The study demonstrated that REIS examinations provide relevant information for developing breast cancer risk stratification tools and that using fusion of several not-fully-correlated classifiers can improve classification performance.

Computer-aided detection; the effect of training databases on detection of subtle breast masses

RATIONALE AND OBJECTIVES

Lesion conspicuity is typically highly correlated with visual difficulty for lesion detection, and computer-aided detection (CAD) has been widely used as a "second reader" in mammography. Hence, increasing CAD sensitivity in detecting subtle cancers without increasing false-positive rates is important. The aim of this study was to investigate the effect of training database case selection on CAD performance in detecting low-conspicuity breast masses.

MATERIALS AND METHODS

A full-field digital mammographic image database that included 525 cases depicting malignant masses was randomly partitioned into three subsets. A CAD scheme was applied to detect all initially suspected mass regions and compute region conspicuity. Training samples were iteratively selected from two of the subsets. Four types of training data sets-(1) one including all available true-positive mass regions in the two subsets ("all"), (2) one including 350 randomly selected mass regions ("diverse"), (3) one including 350 high-conspicuity mass regions ("easy"), and (4) one including 350 low-conspicuity mass regions ("difficult")-were assembled. In each training data set, the same number of randomly selected false-positive regions as the true-positives were also included. Two classifiers, an artificial neural network (ANN) and a k-nearest neighbor (KNN) algorithm, were trained using each of the four training data sets and tested on all suspected regions in the remaining data set. Using a threefold cross-validation method, the performance changes of the CAD schemes trained using one of the four training data sets were computed and compared.

RESULTS

CAD initially detected 1025 true-positive mass regions depicted on 507 cases (97% case-based sensitivity) and 9569 false-positive regions (3.5 per image) in the entire database. Using the all training data set, CAD achieved the highest overall performance on the entire testing database. However, CAD detected the highest number of low-conspicuity masses when the difficult training data set was used for training. Results did agree for both ANN-based and KNN-based classifiers in all tests. Compared to the use of the all training data set, the sensitivity of the schemes trained using the difficult data set decreased by 8.6% and 8.4% for the ANN and KNN algorithm on the entire database, respectively, but the detection of low-conspicuity masses increased by 7.1% and 15.1% for the ANN and KNN algorithm at a false-positive rate of 0.3 per image.

CONCLUSIONS

CAD performance depends on the size, diversity, and difficulty level of the training database. To increase CAD sensitivity in detecting subtle cancer, one should increase the fraction of difficult cases in the training database rather than simply increasing the training data set size.

Computerized detection of breast tissue asymmetry depicted on bilateral mammograms: a preliminary study of breast risk stratification

RATIONALE AND OBJECTIVES

Assessment of the breast tissue pattern asymmetry depicted on bilateral mammograms is routinely used by radiologists when reading and interpreting mammograms. The purpose of this study is to develop an automated scheme to detect breast tissue asymmetry depicted on bilateral mammograms and use the computed asymmetric features to predict the likelihood (or the risk) of women having or developing breast abnormalities or cancer.

MATERIALS AND METHODS

A testing dataset was selected from a large and diverse full-field digital mammography image database, which includes 100 randomly selected negative cases (not recalled during the screening) and 100 positive cases for having or developing breast abnormalities or cancer. Among these positive cases 40 were recalled (biopsy) because of suspicious findings in which 8 were determined as high risk with the lesions surgically removed and the remaining were proven to be benign, and 60 cases were acquired from examinations that were interpreted as negative (without dominant masses or microcalcifications) but the cancers were detected 6-18 months later. A computerized scheme was developed to detect asymmetry of mammographic tissue density represented by the related feature differences computed from bilateral images. Initially, each of 20 features was tested to classify between the positive and the negative cases. To further improve the classification performance, a genetic algorithm (GA) was applied to select a set of optimal features and build an artificial neural network (ANN). The leave-one-case-out validation method was used to evaluate the ANN classification performance.

RESULTS

Using a single feature, the maximum classification performance level measured by the area under the receiver operating characteristic curve (AUC) was 0.681 ± 0.038. Using the GA-optimized ANN, the classification performance level increased to an AUC = 0.754 ± 0.024. At 90% specificity, the ANN classifier yielded 42% sensitivity, in which 42 positive cases were correctly identified. Among them, 30 were the "prior" examinations of the cancer cases and 12 were recalled benign cases, which represent 50% and 30% sensitivity levels in these two subgroups, respectively.

CONCLUSIONS

This study demonstrated that using the computerized detected feature differences related to the bilateral mammographic breast tissue asymmetry, an automated scheme is able to classify a set of testing cases into the two groups of positive or negative of having or developing breast abnormalities or cancer. Hence, further development and optimization of this automated method may eventually help radiologists identify a fraction of women at high risk of developing breast cancer and ultimately detect cancer at an early stage.

Endotracheal intubation confirmation based on video image classification using a parallel GMMs framework: a preliminary evaluation

In this paper, the problem of endotracheal intubation confirmation is addressed. Endotracheal intubation is a complex procedure which requires high skills and the use of secondary confirmation devices to ensure correct positioning of the tube. A novel confirmation approach, based on video images classification, is introduced. The approach is based on identification of specific anatomical landmarks, including esophagus, upper trachea and main bifurcation of the trachea into the two primary bronchi ("carina"), as indicators of correct or incorrect tube insertion and positioning. Classification of the images is performed using a parallel Gaussian mixture models (GMMs) framework, which is composed of several GMMs, schematically connected in parallel, where each GMM represents a different imaging angle. The performance of the proposed approach was evaluated using a dataset of cow-intubation videos and a dataset of human-intubation videos. Each one of the video images was manually (visually) classified by a medical expert into one of three categories: upper-tracheal intubation, correct (carina) intubation, and esophageal intubation. The image classification algorithm was applied off-line using a leave-one-case-out method. The results show that the system correctly classified 1517 out of 1600 (94.8%) of the cow-intubation images, and 340 out of the 358 human images (95.0%). The classification results compared favorably with a "standard" GMM approach utilizing textural based features, as well as with a state-of-the-art classification method, tested on the cow-intubation dataset.

Novel automatic endotracheal position confirmation system: mannequin model algorithm evaluation

OBJECTIVE

A novel endotracheal intubation accurate positioning confirmation system based on image classification algorithm is introduced and evaluated using a mannequin model.

METHODS

The system comprises a miniature complementary metal oxide silicon sensor (CMOS) attached to the tip of a semi rigid stylet and connected to a digital signal processor (DSP) with an integrated video acquisition component. Video signals acquired and processed by an algorithm implemented on the processor. During mannequin intubations, video signals were continuously recorded. A total of 10 videos were recorded. From each video, 7 images of esophageal intubation and 8 images of endotracheal intubation (in which the carina could be clearly seen) were extracted, yielding a total of 150 images taken from arbitrary positions and angles which were processed by the confirmation algorithm.

RESULTS

The performance of the confirmation algorithm was evaluated using a leave-one-out method: in each iteration, 149 images were used to train the system and estimate the models, and the remaining image was used to test the system. This process was repeated 150 times such that each image participated once in testing. The system correctly identified 80 out of 80 endotracheal intubations and 70 out of 70 esophageal intubations.

CONCLUSIONS

This fully automatic image recognition system was used successfully to discriminate airway carina and non-carina endotracheal tube positioning. The system had a 100% success rate using a mannequin model and therefore further investigation including live tissue model and human research should follow.

Acoustic Monitoring of Double-Lumen Ventilated Lungs for the Detection of Selective Unilateral Lung Ventilation

One-lung intubation (OLI) is among the most common complications of endotracheal intubation. None of the monitoring tools now available has proved effective for its early detection. In this study we investigated the efficacy of acoustic analysis for the detection of OLI. We collected lung sounds from 11 patients undergoing thoracic surgery requiring the placement of a double-lumen tube. Recordings of separate lung ventilation were performed after induction and confirmation of adequate tube positioning, before surgery. Samples of lung sounds were collected by three piezoelectric microphones, one on each side of the chest and one on the right forearm, for background noise sampling. The samples were filtered, the signals' energy envelopes were calculated, and segmentation to breath and rest periods was performed. Each respiration was classified into one of the three categories: bilateral ventilation, selective right-lung ventilation, or selective left-lung ventilation, on the basis of the ratio between the energy signals of each lung. OLI was accurately identified in 10 of the 11 patients during right OLI and in all 11 patients during left OLI. This study suggests that acoustic monitoring is effective for the detection of selective lung ventilation and may be useful for early diagnosis of OLI.

Exchange-induced anisotropies at ferromagnetic-antiferromagnetic interfaces above and below the Néel temperature

The exchange bias and magnetic anisotropies in a Co layer on a single-crystalline FeF2 film have been determined between 30 and 300 K. By postulating that the coupling between the ferromagnet and the antiferromagnet persists above the Néel temperature (T(N)) we develop a model that quantitatively describes the exchange bias and the anisotropies over the whole temperature range, both above and below T(N). Using only the measured low temperature exchange bias and a distribution of blocking temperatures we explain (i) the temperature dependence of the bias, (ii) the magnitude of the anisotropies, (iii) the opposite sign of the first and second order anisotropies, (iv) the observed 1/T and 1/T(3) temperature dependencies of the first and second order uniaxial anisotropies above T(N), and (v) the decrease of the anisotropies below T(N).

Order parameter criticality of the d = 3 random-field Ising antiferromagnet Fe0.85Zn0.15F2

The critical exponent beta=0.16+/-0.02 for the random-field Ising model order parameter is determined using extinction-free magnetic x-ray scattering for Fe0.85Zn0.15F2 in magnetic fields of 10 and 11 T. The observed value is consistent with other experimental random-field critical exponents, but disagrees sharply with Monte Carlo and exact ground state calculations on finite-sized systems.

Squamous odontogenic tumor. Report of three cases including the first extraosseous case

Squamous odontogenic tumor is a distinct entity-separate from the more aggressive ameloblastoma. Only 33 squamous odontogenic tumors have been reported since the first description in 1975. We report three additional cases including the first completely extraosseous case.

Gingival hyperplasia associated with nifedipine therapy. Report of a case

A case of gingival hyperplasia associated with the administration of nifedipine is reported. Clinically and histologically, the tissue resembled that seen in hyperplasia induced by phenytoin (Dilantin). We believe this to be the first reported case of gingival hyperplasia associated with this drug.