Deep Learning-Based Nuclear Morphometry Reveals an Independent Prognostic Factor in Mantle Cell Lymphoma.

Blastoid/pleomorphic morphology is associated with short survival in mantle cell lymphoma (MCL), but its prognostic value is overridden by Ki-67 in multivariate analysis. Herein, a nuclear segmentation model was developed using deep learning, and nuclei of tumor cells in 103 MCL cases were automatically delineated. Eight nuclear morphometric attributes were extracted from each nucleus. The mean, variance, skewness, and kurtosis of each attribute were calculated for each case, resulting in 32 morphometric parameters. Compared with those in classic MCL, 17 morphometric parameters were significantly different in blastoid/pleomorphic MCL. Using univariate analysis, 16 morphometric parameters (including 14 significantly different between classic and blastoid/pleomorphic MCL) emerged as significant prognostic factors. Using multivariate analysis, Biologic MCL International Prognostic Index (bMIPI) risk group (P = 0.025), low skewness of nuclear irregularity (P = 0.020), and high mean of nuclear irregularity (P = 0.047) emerged as independent adverse prognostic factors. Additionally, a morphometric score calculated from the skewness and mean of nuclear irregularity (P = 0.0038) was an independent prognostic factor in addition to bMIPI risk group (P = 0.025), and a summed morphometric bMIPI score was useful for risk stratification of patients with MCL (P = 0.000001). These results demonstrate, for the first time, that a nuclear morphometric score is an independent prognostic factor in MCL. It is more robust than blastoid/pleomorphic morphology and can be objectively measured.

Two-tiered deep-learning-based model for histologic diagnosis of Helicobacter gastritis.

AIMS: Helicobacter pylori (HP) infection is the most common cause of chronic gastritis worldwide. Due to the small size of HP and limited resolution, diagnosing HP infections is more difficult when using digital slides. METHODS AND RESULTS: We developed a two-tier deep-learning-based model for diagnosing HP gastritis. A whole-slide model was trained on 885 whole-slide images (WSIs) with only slide-level labels (positive or negative slides). An auxiliary model was trained on 824 areas with HP in nine positive WSIs and 446 negative WSIs for localizing HP. The whole-slide model performed well, with an area under the receiver operating characteristic curve (AUC) of 0.9739 (95% confidence interval [CI], 0.9545-0.9932). The calculated sensitivity and specificity were 93.3% and 90.1%, respectively, whereas those of pathologists were 93.3% and 84.2%, respectively. Using the auxiliary model, the highlighted areas of the localization maps had an average precision of 0.5796. CONCLUSIONS: HP gastritis can be diagnosed on haematoxylin-and-eosin-stained WSIs with human-level accuracy using a deep-learning-based model trained on slide-level labels and an auxiliary model for localizing HP and confirming the diagnosis. This two-tiered model can shorten the diagnostic process and reduce the need for special staining.

Deep Learning for Automatic Hyoid Tracking in Videofluoroscopic Swallow Studies.

The hyoid bone excursion is one of the most important gauges of larynx elevation in swallowing, contributing to airway protection and bolus passage into the esophagus. However, the implications of various parameters of hyoid bone excursion, such as the horizontal or vertical displacement and velocity, remain elusive and raise the need for a tool providing automatic kinematics analysis. Several conventional and deep learning-based models have been applied automatically to track the hyoid bone, but previous methods either require partial manual localization or do not transform the trajectory by anatomic axis. This work describes a convolutional neural network-based algorithm featuring fully automatic hyoid bone localization and tracking and spine axis determination. The algorithm automatically estimates the hyoid bone trajectory and calculates several physical quantities, including the average velocity and displacement in horizontal or vertical anatomic axis. The model was trained in a dataset of 365 videos of videofluoroscopic swallowing from 189 patients in a tertiary medical center and tested using 44 videos from 44 patients with different dysphagia etiologies. The algorithm showed high detection rates for the hyoid bone. The results showed excellent inter-rater reliability for hyoid bone detection, good-to-excellent inter-rater reliability for calculating the maximal displacement and the average velocity of the hyoid bone in horizontal or vertical directions, and moderate-to-good reliability in calculating the average velocity in horizontal direction. The proposed algorithm allows for complete automatic kinematic analysis of hyoid bone excursion, providing a versatile tool with high potential for clinical applications.

Automatic recognition of whole-spine sagittal alignment and curvature analysis through a deep learning technique.

PURPOSE: Artificial intelligence based on deep learning (DL) approaches enables the automatic recognition of anatomic landmarks and subsequent estimation of various spinopelvic parameters. The locations of inflection points (IPs) and apices (APs) in whole-spine lateral radiographs could be mathematically determined by a fully automatic spinal sagittal curvature analysis system. METHODS: We developed a DL model for automatic spinal curvature analysis of whole-spine lateral plain radiographs by using 1800 annotated images of various spinal disease etiologies. The DL model comprised a landmark localizer to detect 25 vertebral landmarks and a numerical algorithm for the generation of an individualized spinal sagittal curvature. The characteristics of the spinal curvature, including the IPs, APs, and curvature angle, could thus be analyzed using mathematical definitions. The localization error of each landmark was calculated from the predictions of 300 test images to evaluate the performance of the landmark localizer. The interrater reliability among a senior orthopedic surgeon, a radiologist, and the DL model was assessed using the intraclass correlation coefficient (ICC). RESULTS: The accuracy of the landmark localizer was within an acceptable range (median error: 1.7-4.1 mm), and the interrater reliabilities between the proposed DL model and each expert were good to excellent (all ICCs > 0.85) for the measurement of spinal curvature characteristics. CONCLUSION: The interrater reliability between the proposed DL model and human experts was good to excellent in predicting the locations of IPs, APs, and curvature angles. Future applications should be explored to validate this system and improve its clinical efficiency.

Self-assembly of biological networks via adaptive patterning revealed by avian intradermal muscle network formation.

Networked structures integrate numerous elements into one functional unit, while providing a balance between efficiency, robustness, and flexibility. Understanding how biological networks self-assemble will provide insights into how these features arise. Here, we demonstrate how nature forms exquisite muscle networks that can repair, regenerate, and adapt to external perturbations using the feather muscle network in chicken embryos as a paradigm. The self-assembled muscle networks arise through the implementation of a few simple rules. Muscle fibers extend outward from feather buds in every direction, but only those muscle fibers able to connect to neighboring buds are eventually stabilized. After forming such a nearest-neighbor configuration, the network can be reconfigured, adapting to perturbed bud arrangement or mechanical cues. Our computational model provides a bioinspired algorithm for network self-assembly, with intrinsic or extrinsic cues necessary and sufficient to guide the formation of these regenerative networks. These robust principles may serve as a useful guide for assembling adaptive networks in other contexts.

Identification of nodal micrometastasis in colorectal cancer using deep learning on annotation-free whole-slide images.

Detection of nodal micrometastasis (tumor size: 0.2-2.0 mm) is challenging for pathologists due to the small size of metastatic foci. Since lymph nodes with micrometastasis are counted as positive nodes, detecting micrometastasis is crucial for accurate pathologic staging of colorectal cancer. Previously, deep learning algorithms developed with manually annotated images performed well in identifying micrometastasis of breast cancer in sentinel lymph nodes. However, the process of manual annotation is labor intensive and time consuming. Multiple instance learning was later used to identify metastatic breast cancer without manual annotation, but its performance appears worse in detecting micrometastasis. Here, we developed a deep learning model using whole-slide images of regional lymph nodes of colorectal cancer with only a slide-level label (either a positive or negative slide). The training, validation, and testing sets included 1963, 219, and 1000 slides, respectively. A supercomputer TAIWANIA 2 was used to train a deep learning model to identify metastasis. At slide level, our algorithm performed well in identifying both macrometastasis (tumor size > 2.0 mm) and micrometastasis with an area under the receiver operating characteristics curve (AUC) of 0.9993 and 0.9956, respectively. Since most of our slides had more than one lymph node, we then tested the performance of our algorithm on 538 single-lymph node images randomly cropped from the testing set. At single-lymph node level, our algorithm maintained good performance in identifying macrometastasis and micrometastasis with an AUC of 0.9944 and 0.9476, respectively. Visualization using class activation mapping confirmed that our model identified nodal metastasis based on areas of tumor cells. Our results demonstrate for the first time that micrometastasis could be detected by deep learning on whole-slide images without manual annotation.

Self-organization process in newborn skin organoid formation inspires strategy to restore hair regeneration of adult cells.

Organoids made from dissociated progenitor cells undergo tissue-like organization. This in vitro self-organization process is not identical to embryonic organ formation, but it achieves a similar phenotype in vivo. This implies genetic codes do not specify morphology directly; instead, complex tissue architectures may be achieved through several intermediate layers of cross talk between genetic information and biophysical processes. Here we use newborn and adult skin organoids for analyses. Dissociated cells from newborn mouse skin form hair primordia-bearing organoids that grow hairs robustly in vivo after transplantation to nude mice. Detailed time-lapse imaging of 3D cultures revealed unexpected morphological transitions between six distinct phases: dissociated cells, cell aggregates, polarized cysts, cyst coalescence, planar skin, and hair-bearing skin. Transcriptome profiling reveals the sequential expression of adhesion molecules, growth factors, Wnts, and matrix metalloproteinases (MMPs). Functional perturbations at different times discern their roles in regulating the switch from one phase to another. In contrast, adult cells form small aggregates, but then development stalls in vitro. Comparative transcriptome analyses suggest suppressing epidermal differentiation in adult cells is critical. These results inspire a strategy that can restore morphological transitions and rescue the hair-forming ability of adult organoids: (i) continuous PKC inhibition and (ii) timely supply of growth factors (IGF, VEGF), Wnts, and MMPs. This comprehensive study demonstrates that alternating molecular events and physical processes are in action during organoid morphogenesis and that the self-organizing processes can be restored via environmental reprogramming. This tissue-level phase transition could drive self-organization behavior in organoid morphogenies beyond the skin.

Self-organizing hair peg-like structures from dissociated skin progenitor cells: New insights for human hair follicle organoid engineering and Turing patterning in an asymmetric morphogenetic field.

Human skin progenitor cells will form new hair follicles, although at a low efficiency, when injected into nude mouse skin. To better study and improve upon this regenerative process, we developed an in vitro system to analyse the morphogenetic cell behaviour in detail and modulate physical-chemical parameters to more effectively generate hair primordia. In this three-dimensional culture, dissociated human neonatal foreskin keratinocytes self-assembled into a planar epidermal layer while fetal scalp dermal cells coalesced into stripes, then large clusters, and finally small clusters resembling dermal condensations. At sites of dermal clustering, subjacent epidermal cells protruded to form hair peg-like structures, molecularly resembling hair pegs within the sequence of follicular development. The hair peg-like structures emerged in a coordinated, formative wave, moving from periphery to centre, suggesting that the droplet culture constitutes a microcosm with an asymmetric morphogenetic field. In vivo, hair follicle populations also form in a progressive wave, implying the summation of local periodic patterning events with an asymmetric global influence. To further understand this global patterning process, we developed a mathematical simulation using Turing activator-inhibitor principles in an asymmetric morphogenetic field. Together, our culture system provides a suitable platform to (a) analyse the self-assembly behaviour of hair progenitor cells into periodically arranged hair primordia and (b) identify parameters that impact the formation of hair primordia in an asymmetric morphogenetic field. This understanding will enhance our future ability to successfully engineer human hair follicle organoids.

Correction: A Novel Convolutional Neural Network for the Diagnosis and Classification of Rosacea: Usability Study.

[This corrects the article DOI: 10.2196/23415.].

Deep Residual Learning-Based Classification with Identification of Incorrect Predictions and Quantification of Cellularity and Nuclear Morphological Features in Digital Pathological Images of Common Astrocytic Tumors.

Interobserver variations in the pathology of common astrocytic tumors impact diagnosis and subsequent treatment decisions. This study leveraged a residual neural network-50 (ResNet-50) in digital pathological images of diffuse astrocytoma, anaplastic astrocytoma, and glioblastoma to recognize characteristic pathological features and perform classification at the patch and case levels with identification of incorrect predictions. In addition, cellularity and nuclear morphological features, including axis ratio, circularity, entropy, area, irregularity, and perimeter, were quantified via a hybrid task cascade (HTC) framework and compared between different characteristic pathological features with importance weighting. A total of 95 cases, including 15 cases of diffuse astrocytoma, 11 cases of anaplastic astrocytoma, and 69 cases of glioblastoma, were collected in Taiwan Hualien Tzu Chi Hospital from January 2000 to December 2021. The results revealed that an optimized ResNet-50 model could recognize characteristic pathological features at the patch level and assist in diagnosis at the case level with accuracies of 0.916 and 0.846, respectively. Incorrect predictions were mainly due to indistinguishable morphologic overlap between anaplastic astrocytoma and glioblastoma tumor cell area, zones of scant vascular lumen with compact endothelial cells in the glioblastoma microvascular proliferation area mimicking the glioblastoma tumor cell area, and certain regions in diffuse astrocytoma with too low cellularity being misrecognized as the glioblastoma necrosis area. Significant differences were observed in cellularity and each nuclear morphological feature among different characteristic pathological features. Furthermore, using the extreme gradient boosting (XGBoost) algorithm, we found that entropy was the most important feature for classification, followed by cellularity, area, circularity, axis ratio, perimeter, and irregularity. Identifying incorrect predictions provided valuable feedback to machine learning design to further enhance accuracy and reduce errors in classification. Moreover, quantifying cellularity and nuclear morphological features with importance weighting provided the basis for developing an innovative scoring system to achieve objective classification and precision diagnosis among common astrocytic tumors.

Glomerular disease classification and lesion identification by machine learning.

BACKGROUND: Classification of glomerular diseases and identification of glomerular lesions require careful morphological examination by experienced nephropathologists, which is labor-intensive, time-consuming, and prone to interobserver variability. In this regard, recent advance in machine learning-based image analysis is promising. METHODS: We combined Mask Region-based Convolutional Neural Networks (Mask R-CNN) with an additional classification step to build a glomerulus detection model using human kidney biopsy samples. A Long Short-Term Memory (LSTM) recurrent neural network was applied for glomerular disease classification, and another two-stage model using ResNeXt-101 was constructed for glomerular lesion identification in cases of lupus nephritis. RESULTS: The detection model showed state-of-the-art performance on variedly stained slides with F1 scores up to 0.944. The disease classification model showed good accuracies up to 0.940 on recognizing different glomerular diseases based on H&E whole slide images. The lesion identification model demonstrated high discriminating power with area under the receiver operating characteristic curve up to 0.947 for various glomerular lesions. Models showed good generalization on external testing datasets. CONCLUSION: This study is the first-of-its-kind showing how each step of kidney biopsy interpretation carried out by nephropathologists can be captured and simulated by machine learning models. The models were integrated into a whole slide image viewing and annotating platform to enable nephropathologists to review, correct, and confirm the inference results. Further improvement on model performances and incorporating inputs from immunofluorescence, electron microscopy, and clinical data might realize actual clinical use.

Deep neural network trained on gigapixel images improves lymph node metastasis detection in clinical settings.

The pathological identification of lymph node (LN) metastasis is demanding and tedious. Although convolutional neural networks (CNNs) possess considerable potential in improving the process, the ultrahigh-resolution of whole slide images hinders the development of a clinically applicable solution. We design an artificial-intelligence-assisted LN assessment workflow to facilitate the routine counting of metastatic LNs. Unlike previous patch-based approaches, our proposed method trains CNNs by using 5-gigapixel images, obviating the need for lesion-level annotations. Trained on 5907 LN images, our algorithm identifies metastatic LNs in gastric cancer with a slide-level area under the receiver operating characteristic curve (AUC) of 0.9936. Clinical experiments reveal that the workflow significantly improves the sensitivity of micrometastasis identification (81.94% to 95.83%, P < .001) and isolated tumor cells (67.95% to 96.15%, P < .001) in a significantly shorter review time (-31.5%, P < .001). Cross-site evaluation indicates that the algorithm is highly robust (AUC = 0.9829).

Peripapillary Choroid Thickness as a Predisposing Factor for Nonarteritic Anterior Ischemic Optic Neuropathy: A Semiautomated OCT Study.

Purpose: The purpose of this study was to investigate the correlations between peripapillary choroidal thickness (PCT) and nonarteritic ischemic optic neuropathy (NAION) by using semiautomated optic coherence tomography (OCT). Methods: A total of 35 NAION eyes, 29 unaffected fellow eyes, and 40 eyes from an age-matched control group were recruited. Enhanced-depth imaging OCT was performed after the resolution of disc edema. PCT was measured using a customized semiautomated MATLAB program. Regression models adjusted for multiple variables were used to inspect the correlation between mean PCT and NAION. Results: The mean PCT in NAION eyes, unaffected fellow eyes, and the control group was 197.09 ± 38.09, 196.52 ± 38.47, and 153.53 ± 29.92 µm, respectively. The mean PCT was significantly thicker both in NAION-affected eyes and fellow eyes compared with the control group (P < 0.001). No significant difference existed between NAION-affected eyes and unaffected fellow eyes. The PCT of the superior quadrant was significantly thicker than that of the inferior quadrant in all three groups. In the multivariate logistic regression, PCT was the only predisposing factor for NAION. However, the value of the PCT was not correlated with final visual outcomes. Conclusions: With a semiautomated program to alleviate the missing measurements, our study demonstrated significantly thicker PCT in both NAION-affected and unaffected eyes of patients, which indicated that peripapillary pachychoroid is a predisposing factor for NAION but may not be a prognostic factor for visual outcomes. Translational Relevance: Accurate measurement of PCT by using semiautomated OCT illustrates the correlation between choroidal vasculature and NAION.

Fabrication of Stromal Cell-Derived Factor-1 Contained in Gelatin/Hyaluronate Copolymer Mixed with Hydroxyapatite for Use in Traumatic Bone Defects

Bone defects of orthopedic trauma remain a challenge in clinical practice. Regarding bone void fillers, besides the well-known osteoconductivity of most bone substitutes, osteoinductivity has also been gaining attention in recent years. It is known that stromal cell-derived factor-1 (SDF-1) can recruit mesenchymal stem cells (MSCs) in certain circumstances, which may also play an important role in bone regeneration. In this study, we fabricated a gelatin/hyaluronate (Gel/HA) copolymer mixed with hydroxyapatite (HAP) and SDF-1 to try and enhance bone regeneration in a bone defect model. After material characterization, these Gel/HA-HAP and Gel/HA-HAP-SDF-1 composites were tested for their biocompatibility and ability to recruit MSCs in vitro. A femoral condyle bone defect model of rats was used for in vivo studies. For the assessment of bone healing, micro-CT analysis, second harmonic generation (SHG) imaging, and histology studies were performed. As a result, the Gel/HA-HAP composites showed no systemic toxicity to rats. Gel/HA-HAP composite groups both showed better bone generation compared with the control group in an animal study, and the composite with the SDF-1 group even showed a trend of faster bone growth compared with the composite without SDF-1 group. In conclusion, in the management of traumatic bone defects, Gel/HA-HAP-SDF-1 composites can be a feasible material for use as bone void fillers.

Measurement of laryngeal elevation by automated segmentation using Mask R-CNN.

ABSTRACT: The methods of measuring laryngeal elevation during swallowing are time-consuming. We aimed to propose a quick-to-use neural network (NN) model for measuring laryngeal elevation quantitatively using anatomical structures auto-segmented by Mask region-based convolutional NN (R-CNN) in videofluoroscopic swallowing study. Twelve videofluoroscopic swallowing study video clips were collected. One researcher drew the anatomical structure, including the thyroid cartilage and vocal fold complex (TVC) on respective video frames. The dataset was split into 11 videos (4686 frames) for model development and one video (532 frames) for derived model testing. The validity of the trained model was evaluated using the intersection over the union. The mean intersections over union of the C1 spinous process and TVC were 0.73 ±â€Š0.07 [0-0.88] and 0.43 ±â€Š0.19 [0-0.79], respectively. The recall rates for the auto-segmentation of the TVC and C1 spinous process by the Mask R-CNN were 86.8% and 99.8%, respectively. Actual displacement of the larynx was calculated using the midpoint of the auto-segmented TVC and C1 spinous process and diagonal lengths of the C3 and C4 vertebral bodies on magnetic resonance imaging, which measured 35.1 mm. Mask R-CNN segmented the TVC with high accuracy. The proposed method measures laryngeal elevation using the midpoint of the TVC and C1 spinous process, auto-segmented by Mask R-CNN. Mask R-CNN auto-segmented the TVC with considerably high accuracy. Therefore, we can expect that the proposed method will quantitatively and quickly determine laryngeal elevation in clinical settings.

Machine Learning Based on Morphological Features Enables Classification of Primary Intestinal T-Cell Lymphomas.

The aim of this study was to investigate the feasibility of using machine learning techniques based on morphological features in classifying two subtypes of primary intestinal T-cell lymphomas (PITLs) defined according to the WHO criteria: monomorphic epitheliotropic intestinal T-cell lymphoma (MEITL) versus intestinal T-cell lymphoma, not otherwise specified (ITCL-NOS), which is considered a major challenge for pathological diagnosis. A total of 40 histopathological whole-slide images (WSIs) from 40 surgically resected PITL cases were used as the dataset for model training and testing. A deep neural network was trained to detect and segment the nuclei of lymphocytes. Quantitative nuclear morphometrics were further computed from these predicted contours. A decision-tree-based machine learning algorithm, XGBoost, was then trained to classify PITL cases into two disease subtypes using these nuclear morphometric features. The deep neural network achieved an average precision of 0.881 in the cell segmentation work. In terms of classifying MEITL versus ITCL-NOS, the XGBoost model achieved an area under receiver operating characteristic curve (AUC) of 0.966. Our research demonstrated an accurate, human-interpretable approach to using machine learning algorithms for reducing the high dimensionality of image features and classifying T cell lymphomas that present challenges in morphologic diagnosis. The quantitative nuclear morphometric features may lead to further discoveries concerning the relationship between cellular phenotype and disease status.

Deep learning approach for automatic landmark detection and alignment analysis in whole-spine lateral radiographs.

Human spinal balance assessment relies considerably on sagittal radiographic parameter measurement. Deep learning could be applied for automatic landmark detection and alignment analysis, with mild to moderate standard errors and favourable correlations with manual measurement. In this study, based on 2210 annotated images of various spinal disease aetiologies, we developed deep learning models capable of automatically locating 45 anatomic landmarks and subsequently generating 18 radiographic parameters on a whole-spine lateral radiograph. In the assessment of model performance, the localisation accuracy and learning speed were the highest for landmarks in the cervical area, followed by those in the lumbosacral, thoracic, and femoral areas. All the predicted radiographic parameters were significantly correlated with ground truth values (all p < 0.001). The human and artificial intelligence comparison revealed that the deep learning model was capable of matching the reliability of doctors for 15/18 of the parameters. The proposed automatic alignment analysis system was able to localise spinal anatomic landmarks with high accuracy and to generate various radiographic parameters with favourable correlations with manual measurements.

An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning.

Deep learning for digital pathology is hindered by the extremely high spatial resolution of whole-slide images (WSIs). Most studies have employed patch-based methods, which often require detailed annotation of image patches. This typically involves laborious free-hand contouring on WSIs. To alleviate the burden of such contouring and obtain benefits from scaling up training with numerous WSIs, we develop a method for training neural networks on entire WSIs using only slide-level diagnoses. Our method leverages the unified memory mechanism to overcome the memory constraint of compute accelerators. Experiments conducted on a data set of 9662 lung cancer WSIs reveal that the proposed method achieves areas under the receiver operating characteristic curve of 0.9594 and 0.9414 for adenocarcinoma and squamous cell carcinoma classification on the testing set, respectively. Furthermore, the method demonstrates higher classification performance than multiple-instance learning as well as strong localization results for small lesions through class activation mapping.

A Novel Convolutional Neural Network for the Diagnosis and Classification of Rosacea: Usability Study.

BACKGROUND: Rosacea is a chronic inflammatory disease with variable clinical presentations, including transient flushing, fixed erythema, papules, pustules, and phymatous changes on the central face. Owing to the diversity in the clinical manifestations of rosacea, the lack of objective biochemical examinations, and nonspecificity in histopathological findings, accurate identification of rosacea is a big challenge. Artificial intelligence has emerged as a potential tool in the identification and evaluation of some skin diseases such as melanoma, basal cell carcinoma, and psoriasis. OBJECTIVE: The objective of our study was to utilize a convolutional neural network (CNN) to differentiate the clinical photos of patients with rosacea (taken from 3 different angles) from those of patients with other skin diseases such as acne, seborrheic dermatitis, and eczema that could be easily confused with rosacea. METHODS: In this study, 24,736 photos comprising of 18,647 photos of patients with rosacea and 6089 photos of patients with other skin diseases such as acne, facial seborrheic dermatitis, and eczema were included and analyzed by our CNN model based on ResNet-50. RESULTS: The CNN in our study achieved an overall accuracy and precision of 0.914 and 0.898, with an area under the receiver operating characteristic curve of 0.972 for the detection of rosacea. The accuracy of classifying 3 subtypes of rosacea, that is, erythematotelangiectatic rosacea, papulopustular rosacea, and phymatous rosacea was 83.9%, 74.3%, and 80.0%, respectively. Moreover, the accuracy and precision of our CNN to distinguish rosacea from acne reached 0.931 and 0.893, respectively. For the differentiation between rosacea, seborrheic dermatitis, and eczema, the overall accuracy of our CNN was 0.757 and the precision was 0.667. Finally, by comparing the CNN diagnosis with the diagnoses by dermatologists of different expertise levels, we found that our CNN system is capable of identifying rosacea with a performance superior to that of resident doctors or attending physicians and comparable to that of experienced dermatologists. CONCLUSIONS: The findings of our study showed that by assessing clinical images, the CNN system in our study could identify rosacea with accuracy and precision comparable to that of an experienced dermatologist.

Artificial Intelligence-Based Screening for Mycobacteria in Whole-Slide Images of Tissue Samples.

OBJECTIVES: This study aimed to develop and validate a deep learning algorithm to screen digitized acid fast-stained (AFS) slides for mycobacteria within tissue sections. METHODS: A total of 441 whole-slide images (WSIs) of AFS tissue material were used to develop a deep learning algorithm. Regions of interest with possible acid-fast bacilli (AFBs) were displayed in a web-based gallery format alongside corresponding WSIs for pathologist review. Artificial intelligence (AI)-assisted analysis of another 138 AFS slides was compared to manual light microscopy and WSI evaluation without AI support. RESULTS: Algorithm performance showed an area under the curve of 0.960 at the image patch level. More AI-assisted reviews identified AFBs than manual microscopy or WSI examination (P < .001). Sensitivity, negative predictive value, and accuracy were highest for AI-assisted reviews. AI-assisted reviews also had the highest rate of matching the original sign-out diagnosis, were less time-consuming, and were much easier for pathologists to perform (P < .001). CONCLUSIONS: This study reports the successful development and clinical validation of an AI-based digital pathology system to screen for AFBs in anatomic pathology material. AI assistance proved to be more sensitive and accurate, took pathologists less time to screen cases, and was easier to use than either manual microscopy or viewing WSIs.