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Infrared spectral histopathology for cancer diagnosis: a novel approach for automated pattern recognition of colon adenocarcinoma

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ANALYST 2014年第16期139卷 4005-4015页

作者： Nallala, Jayakrupakar Diebold, Marie-Daniele Gobinet, Cyril Bouche, Olivier Sockalingum, Ganesh Dhruvananda Piot, Olivier Manfait, Michel Univ Reims MeDIAN Biophoton & Technol Sante UFR Pharm F-51096 Reims France CNRS MEDyC UMR7369 Reims France CHU Reims Lab Cent Anat Pathol F-51092 Reims France CHU Reims Serv Hepato Gastroenterol & Cancerol Digest F-51092 Reims France

Histopathology remains the gold standard method for colon cancer diagnosis. Novel complementary approaches for molecular level diagnosis of the disease are need of the hour. Infrared (IR) imaging could be a promising candidate method as it probes the intrinsic chemical bonds present in a tissue, and provides a "spectral fingerprint" of the biochemical composition. To this end, IR spectral histopathology, which combines IR imaging and data processing techniques, was employed on seventy seven paraffinized colon tissue samples (48 tumoral and 29 non-tumoral) in the form of tissue arrays. To avoid chemical deparaffinization, a digital neutralization of the spectral interference of paraffin was implemented. Clustering analysis was used to partition the spectra and construct pseudo-colored images, for assigning spectral clusters to various tissue structures (normal epithelium, malignant epithelium, connective tissue etc.). Based on the clustering results, linear discriminant analysis was then used to construct a stringent prediction model which was applied on samples without a priori histopathological information. The predicted spectral images not only revealed common features representative of the colonic tissue biochemical make-up, but also highlighted additional features like tumor budding and tumor-stroma association in a label-free manner. This novel approach of IR spectral imaging on paraffinized tissues showed 100% sensitivity and allowed detection and differentiation of normal and malignant colonic features based purely on their intrinsic biochemical features. This nondestructive methodology combined with multivariate statistical image analysis appears as a promising tool for colon cancer diagnosis and opens up the way to the concept of numerical spectral histopathology.

关键词： histopathology recognition adenocarcinoma Tissue Microarray histopathology Infrared imaging neoplasm diagnosis Early Diagnosis of Cancer Cancer of Colon breast neoplasm diagnosis Adenocarcinoma Adenoma automated pattern recognition adenocarcinoma of the colon additional features

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A novel way to evaluate autoantibody interference in samples with mixed antinuclear antibody patterns in the HEp-2 cell based indirect immunofluorescence assay and comparison of conventional microscopic and computer-aided pattern recognition

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CLINICA CHIMICA ACTA 2024年 553卷 117747页

作者： Nagy, Gabor Foldesi, Roza Csipo, Istvan Tarr, Tuende Szucs, Gabriella Szanto, Antonia Buban, Tamas Szekanecz, Zoltan Papp, Maria Kappelmayer, Janos Antal-Szalmas, Peter Univ Debrecen Fac Med Dept Lab Med Debrecen Hungary Univ Debrecen Fac Med Dept Clin Immunol Inst Internal Med Debrecen Hungary Univ Debrecen Fac Med Dept Rheumatol Inst Internal Med Debrecen Hungary Univ Debrecen Fac Med Dept Gastroenterol Debrecen Hungary European Reference Network Hepatol Dis ERN RAREL LIVER Debrecen Hungary Univ Debrecen Fac Med Dept Lab Med Nagyerdei Str 98 H-4032 Debrecen Hungary Univ Debrecen Gyula Petranyi Doctoral Sch Allergy & Clin Immunol Debrecen Hungary

Background: A major challenge of the HEp-2 cell-based indirect immunofluorescence (IIF) assays is the correct identification of the individual anti-nuclear antibodies (ANAs) if more than one is present in a sample. We created artificial mixes by pooling two different samples with a single autoantibody in different titers. Comparison of the expected and observed patterns and titers clarifies the interference between the two tested ANAs. Methods: Serum samples with a single homogeneous or speckled ANA pattern were serially diluted and mixed in 16 combinations, providing end-point titers of 1:5,120 to 1:80 for both patterns. These mixes were tested by a HEp-2 IIF assay and were evaluated by conventional evaluation, the EUROpattern (EPa) system and on-screen analysis. Results: Homogeneous pattern can alter the identification of the speckled pattern much more than vice versa, but both has an interfering effect on the other. The effect of the interfering on the tested pattern was higher if the titer of the former one was higher. The pattern recognition efficacy of conventional and the on-screen evaluation was similar and superior compared to the EPa analysis. Conclusions: The application of artificial mixed samples can help the evaluation of the efficacy of manual and computer-aided ANA HEp-2 pattern recognition.

关键词： ANA HEp-2 test Indirect immunofluorescence Mixed patterns Multiple patterns automated pattern recognition

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Unveiling the sound of the cognitive status: Machine Learning-based speech analysis in the Alzheimer's disease spectrum

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ALZHEIMERS RESEARCH & THERAPY 2024年第1期16卷 26页

作者： Garcia-Gutierrez, Fernando Alegret, Montserrat Marquie, Marta Munoz, Nathalia Ortega, Gemma Cano, Amanda De Rojas, Itziar Garcia-Gonzalez, Pablo Olive, Claudia Puerta, Raquel Garcia-Sanchez, Ainhoa Capdevila-Bayo, Maria Montrreal, Laura Pytel, Vanesa Rosende-Roca, Maitee Zaldua, Carla Gabirondo, Peru Tarraga, Lluis Ruiz, Agustin Boada, Merce Valero, Sergi Univ Int Catalunya Ace Alzheimer Ctr Barcelona Barcelona Spain Inst Salud Carlos III Networking Res Ctr Neurodegenerat Dis CIBERNED Madrid Spain Accexible Impacto s l Urduliz Bizkaia Spain

BackgroundAdvancement in screening tools accessible to the general population for the early detection of Alzheimer's disease (AD) and prediction of its progression is essential for achieving timely therapeutic interventions and conducting decentralized clinical trials. This study delves into the application of Machine Learning (ML) techniques by leveraging paralinguistic features extracted directly from a brief spontaneous speech (SS) protocol. We aimed to explore the capability of ML techniques to discriminate between different degrees of cognitive impairment based on SS. Furthermore, for the first time, this study investigates the relationship between paralinguistic features from SS and cognitive function within the AD ***-acoustic features were extracted from voice recordings of patients evaluated in a memory unit who underwent a SS protocol. We implemented several ML models evaluated via cross-validation to identify individuals without cognitive impairment (subjective cognitive decline, SCD), with mild cognitive impairment (MCI), and with dementia due to AD (ADD). In addition, we established models capable of predicting cognitive domain performance based on a comprehensive neuropsychological battery from Fundacio Ace (NBACE) using SS-derived *** results of this study showed that, based on a paralinguistic analysis of sound, it is possible to identify individuals with ADD (F1 = 0.92) and MCI (F1 = 0.84). Furthermore, our models, based on physical acoustic information, exhibited correlations greater than 0.5 for predicting the cognitive domains of attention, memory, executive functions, language, and visuospatial *** this study, we show the potential of a brief and cost-effective SS protocol in distinguishing between different degrees of cognitive impairment and forecasting performance in cognitive domains commonly affected within the AD spectrum. Our results demonstrate a high correspondence with protocols

关键词： Alzheimer's disease Mild cognitive impairment Early diagnosis Neuropsychological tests Machine Learning Speech acoustics automated pattern recognition

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A personalized semi-automatic sleep spindle detection (PSASD) framework

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JOURNAL OF NEUROSCIENCE METHODS 2024年 407卷 110064页

作者： Kafashan, Mohammadmehdi Gupte, Gaurang Kang, Paul Hyche, Orlandrea Luong, Anhthi H. Prateek, G. V. Ju, Yo-El S. Palanca, Ben Julian A. Washington Univ Dept Anesthesiol Sch Med St Louis 660 S Euclid AveBox 8054 St Louis MO 63110 USA Washington Univ St Louis Ctr Biol Rhythms & Sleep St Louis MO USA Washington Univ St Louis Dept Biomed Engn St Louis MO USA Calico Life Sci LLC South San Francisco CA USA Washington Univ Dept Neurol Div Sleep Med Sch Med St Louis St Louis MO USA Washington Univ Dept Psychiat Sch Med St Louis St Louis MO USA Washington Univ Div Biol & Biomed Sci Sch Med St Louis St Louis MO USA

Background: Sleep spindles are distinct electroencephalogram (EEG) patterns of brain activity that have been posited to play a critical role in development, learning, and neurological disorders. Manual scoring for sleep spindles is labor-intensive and tedious but could supplement automated algorithms to resolve challenges posed with either approaches alone. New methods: A Personalized Semi-Automatic Sleep Spindle Detection (PSASD) framework was developed to combine the strength of automated detection algorithms and visual expertise of human scorers. The underlying model in the PSASD framework assumes a generative model for EEG sleep spindles as oscillatory components, optimized to EEG amplitude, with remaining signals distributed into transient and low-frequency components. Results: A single graphical user interface (GUI) allows both manual scoring of sleep spindles (model training data) and verification of automatically detected spindles. A grid search approach allows optimization of parameters to balance tradeoffs between precision and recall measures. Comparison with existing methods: PSASD outperformed DETOKS in F1-score by 19% and 4% on the DREAMS and P-DROWS-E datasets, respectively. It also outperformed YASA in F1-score by 25% in the P-DROWS-E dataset. Further benchmarking analysis showed that PSASD outperformed four additional widely used sleep spindle detectors in F1-score in the P-DROWS-E dataset. Titration analysis revealed that four 30-second epochs are sufficient to fine-tune the model parameters of PSASD. Associations of frequency, duration, and amplitude of detected sleep spindles matched those previously reported with automated approaches. Conclusions: Overall, PSASD improves detection of sleep spindles in EEG data acquired from both younger healthy and older adult patient populations.

关键词： Electroencephalogram (EEG) Polysomnography Wireless EEG devices Sleep Non-rapid eye movement sleep Sleep spindle automated pattern recognition Signal processing

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Comparison of four convolutional neural networks for histopathological diagnosis of salivary gland carcinomas

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HNO 2023年第3期71卷 170-176页

作者： Schulz, Tobias Becker, Christoph Kayser, Gian Albert Ludwigs Univ Freiburg Med Fak Klin Hals Nasen Ohrenheilkunde Kopf & Halschirurgie Freiburg Germany Gemeinschaftspraxis Pathol Freiburg Germany Albert Ludwigs Univ Freiburg Med Fak Klin Hals Nasen Ohrenheilkunde Kopf & Halschirurgie Killian str 5 D-79106 Freiburg Germany

Background: Malignant salivary gland tumors represent a particular diagnostic challenge due to the large number of histopathological entities, their rare occurrence, and the diverse clinical and histological presentations. The aim of this work is to investigate and compare convolutional neural networks (CNNs) as a diagnostic tool for histological diagnosis of salivary gland ***: From salivary gland cancer preparations of 68 patients, 118 histological slides were digitized at high resolution. These virtual sections were then divided into small image sections, and the resultant 83,819 images were sorted into four categories: background, connective tissue, non-neoplastic salivary gland tissue, and salivary gland cancer tissue. The latter category grouped the entities adenoid cystic carcinoma, adenocarcinoma (not otherwise specified), acinar cell carcinoma, basal cell carcinoma, mucoepidermoid carcinoma, and myoepithelial carcinoma. The categorized images were then processed in a training, validation, and test run by the ImageNet pretrained CNN frameworks (Inception ResNet v2, Inception v3, ResNet152, Xception) in different pixel ***: Accuracy values ranged from 18.8% to 84.7% across all network architectures and pixel sizes, with the Inception v3 network achieving the highest value at 500 x 500 pixels. The recall values/sensitivity reached up to 85% for different pixel sizes (Inception v3 network at 1000x 1000 pixels). The minimum F1 score achieved was 0.07 for the Inception ResNet v2 and the Inception v3 at 100 x 100 pixels each, the maximum F1 score achieved was 0.72 for the Xception at 1000x 1000 pixels. Inception v3 was the network with the shortest training times, and was superior to all other networks at any pixel ***: The current work was able to demonstrate the applicability of CNNs for histopathological analysis of salivary gland tumors for the first time and provide a comparison of the performance of different network architec

关键词： Machine learning Algorithms Artificial intelligence automated pattern recognition Classification

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Artificial intelligence in orthopaedic and trauma surgery imaging

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ORTHOPADIE 2022年第9期51卷 748-756页

作者： Rohde, Stefan Muennich, Nico Klinikum Dortmund gGmbH Klin Radiol & Neuroradiol Beurhausstr 40 D-44137 Dortmund Germany Univ Witten Herdecke Fak Gesundheit Witten Germany

Artificial intelligence (AI) is playing an increasing role in radiological imaging in orthopaedics and trauma surgery. The algorithms available to date are predominantly used in the detection of (occult) fractures and in length and angle measurements in conventional X-ray images. However, current AI solutions also enable the analysis and pattern recognition of CT datasets, e.g. in the detection of rib or vertebral body fractures. A special application is EOS (TM) (ATEC Spine Group, Paris, France), which enables a 3-D simulation of the axial skeleton and semi-automatic length and angle calculations based on a digital 2-D X-ray image. In this paper, the current spectrum of AI applications for orthopaedics and trauma surgery is presented and discussed.

关键词： automated pattern recognition Bone fractures Computational intelligence Emergency medicine Machine learning

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Artificial intelligence for right whale photo identification: from data science competition to worldwide collaboration

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MAMMALIAN BIOLOGY 2022年第3期102卷 1025-1042页

作者： Khan, Christin Blount, Drew Parham, Jason Holmberg, Jason Hamilton, Philip Charlton, Claire Christiansen, Fredrik Johnston, David Rayment, Will Dawson, Steve Vermeulen, Els Rowntree, Victoria Groch, Karina Levenson, J. Jacob Bogucki, Robert NOAA Northeast Fisheries Sci Ctr Woods Hole MA 02543 USA Wild Me Portland OR USA Anderson Cabot Ctr Ocean Life New England Aquariu Boston MA USA Rensselaer Polytech Inst Troy NY USA Curtin Univ Ctr Marine Sci & Technol Bentley WA Australia Aarhus Inst Adv Studies Hoegh Guldbergs Gade 6B DK-8000 Aarhus C Denmark Aarhus Univ Dept Biol Zoophysiol CF Mollers Alle 3 DK-8000 Aarhus C Denmark Murdoch Univ Ctr Sustainable Aquat Ecosyst Harry Butler Inst Murdoch WA 6150 Australia Univ Otago Marine Sci Dept Tari Putaiao Taimoana Otago New Zealand Univ Pretoria Mammal Res Inst Whale Unit Hatfield South Africa Univ Utah Sch Biol Sci Dept Biol Salt Lake City UT USA Inst Conservac Ballenas Buenos Aires Buenos Aires Argentina Southern Right Whale Project Inst Australis Imbituba SC Brazil US Dept Interior Bur Ocean Energy Management Sterling VA USA deepsense ai Al Jerozolimskie 162A PL-02342 Warsaw Poland

Photo identification is an important tool in the conservation management of endangered species, and recent developments in artificial intelligence are revolutionizing existing workflows to identify individual animals. In 2015, the National Oceanic and Atmospheric Administration hosted a Kaggle data science competition to automate the identification of endangered North Atlantic right whales (Eubalaena glacialis). The winning algorithms developed by *** were able to identify individuals with 87% accuracy using a series of convolutional neural networks to identify the region of interest, create standardized photographs of uniform size and orientation, and then identify the correct individual. Since that time, we have brought in many more collaborators as we moved from prototype to production. Leveraging the existing infrastructure by Wild Me, the developers of Flukebook, we have created a web-based platform that allows biologists with no machine learning expertise to utilize semi-automated photo identification of right whales. New models were generated on an updated dataset using the winning *** algorithms. Given the morphological similarity between the North Atlantic right whale and closely related southern right whale (Eubalaena australis), we expanded the system to incorporate the largest long-term photo identification catalogs around the world including the United States, Canada, Australia, South Africa, Argentina, Brazil, and New Zealand. The system is now fully operational with multi-feature matching for both North Atlantic right whales and southern right whales from aerial photos of their heads (Deepsense), lateral photos of their heads (Pose Invariant Embeddings), flukes (CurvRank v2), and peduncle scarring (HotSpotter). We hope to encourage researchers to embrace both broad data collaborations and artificial intelligence to increase our understanding of wild populations and aid conservation efforts.

关键词： automated matching automated pattern recognition Computer vision Individual identification Machine learning Right whale

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Artificial intelligence in modern breast diagnostics

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GYNAKOLOGIE 2022年第10期55卷 771-782页

作者： Banys-Paluchowski, Maggie Molinos, Laura Dussan Ruebsamen, Marcus Toellner, Thilo Rody, Achim Fehm, Tanja Buendgen, Nana Krawczyk, Natalia Univ Klinikum Schleswig Holstein Klin Frauenheilkunde & Geburtshilfe Campus LubeckRatzeburger Allee 160 D-23538 Lubeck Germany Marien Hosp Inst Radiol Hamburg Germany Klin Dr Hancken Stade Germany Heinrich Heine Univ Dusseldorf Univ Klinikum Dusseldorf Frauenklin Dusseldorf Germany

The use of artificial intelligence (AI) is currently under investigation in various fields of medicine. In breast diagnostics the use of AI-guided systems is already reality, especially in the screening setting. It is hoped that in this way the workflow can be simplified and the radiologist can be supported through automatic assessment of the breast density and the risk of malignancy. The use of AI in breast ultrasound focuses mainly on automated breast ultrasound (ABUS). An estimation of the dignity of a region of interest selected by the investigator on a B-mode picture is also possible using modern systems. Furthermore, in magnetic resonance imaging (MRI) of the breast AI could contribute to the development of a useful algorithm. The so-called Kaiser score is available online for radiologists and helps to estimate the risk of malignancy from MRI findings.

关键词： automated pattern recognition Diagnostic techniques obstetrical and gynecological Breast ultrasonography Breast cancer Screening

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A Systematic Review of automated Segmentation Methods and Public Datasets for the Lung and its Lobes and Findings on Computed Tomography Images

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Yearbook of Medical Informatics 2022年第1期31卷 277-295页

作者： Carmo, Diedre Ribeiro, Jean Dertkigil, Sergio Appenzeller, Simone Lotufo, Roberto Rittner, Leticia School of Electrical and Computer Engineering University of Campinas Brazil School of Medical Sciences University of Campinas Brazil

Objectives: automated computational segmentation of the lung and its lobes and findings in X-Ray based computed tomography (CT) images is a challenging problem with important applications, including medical research, surgical planning, and diagnostic decision support. With the increase in large imaging cohorts and the need for fast and robust evaluation of normal and abnormal lungs and their lobes, several authors have proposed automated methods for lung assessment on CT images. In this paper we intend to provide a comprehensive summarization of these methods. Methods: We used a systematic approach to perform an extensive review of automated lung segmentation methods. We chose Scopus, PubMed, and Scopus to conduct our review and included methods that perform segmentation of the lung parenchyma, lobes or internal disease related findings. The review was not limited by date, but rather by only including methods providing quantitative evaluation. Results: We organized and classified all 234 included articles into various categories according to methodological similarities among them. We provide summarizations of quantitative evaluations, public datasets, evaluation metrics, and overall statistics indicating recent research directions of the field. Conclusions: We noted the rise of data-driven models in the last decade, especially due to the deep learning trend, increasing the demand for high-quality data annotation. This has instigated an increase of semi-supervised and uncertainty guided works that try to be less dependent on human annotation. In addition, the question of how to evaluate the robustness of data-driven methods remains open, given that evaluations derived from specific datasets are not general. © 2022 IMIA and Georg Thieme Verlag KG.

关键词： automated pattern recognition Lung lung diseases medical image segmentation x-ray computed tomography

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Artificial intelligence and simulation in prenatal medicine-What we can learn from machines

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GYNAKOLOGIE 2022年第10期55卷 746-758页

作者： Steinhard, J. Freundt, P. Janzing, P. Popov, V Menkhaus, R. Ross, L. Zentrum Pranatale Med Munster Hohenzollernring 70 D-48145 Munster Germany Fetale Kardiol Herz & Diabeteszentrum NRW Bad Oeynhausen Germany Zentrum Kinderwunschbehandlung & Pranataldiagnost Minden Germany St Franziskus Hosp Klin Gynakol & Geburtshilfe Munster Germany

Objective The aim of the work was to provide an overview of currently available applications of artificial intelligence (AI) and simulation techniques in prenatal medicine with a focus on potentially improving education and training. Methods Existing AI applications and simulators in prenatal medicine are presented based on examples and studies. Conclusion Applications of AI are increasingly being integrated into the clinical routine and can become interesting for education and training in the future. Structured simulation-based ultrasound training should be integrated into a training curriculum.

关键词： automated pattern recognition Ultrasound Congenital heart defects Simulators Education

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