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General General

Real-time artificial intelligence-based histological classification of colorectal polyps with augmented visualization.

In Gastrointestinal endoscopy ; h5-index 72.0

BACKGROUND AND AIMS : Artificial intelligence (AI)-based computer-aided diagnostic (CADx) algorithms are a promising approach for real-time histology (RTH) of colonic polyps. Our aim is to present a novel in situ CADx approach that seeks to increase transparency and interpretability of results by generating an intuitive augmented visualization of the model's predicted histology over the polyp surface.

METHODS : We developed a deep learning (DL) model using semantic segmentation to delineate polyp boundaries, and a DL model to classify subregions within the segmented polyp. These subregions were classified independently, and subsequently aggregated to generate a histology map of the polyp's surface. We used 740 high-magnification narrow-band images from 607 polyps in 286 patients, and over 65,000 subregions, to train and validate the model.

RESULTS : The model achieved a sensitivity of 0.96, specificity of 0.84, negative predictive value (NPV) of 0.91, and high-confidence rate (HCR) of 0.88, distinguishing 171 neoplastic polyps from 83 non-neoplastic polyps of all sizes. Among 93 neoplastic and 75 non-neoplastic polyps ≤5 mm, the model achieved a sensitivity of 0.95, specificity of 0.84, NPV of 0.91 and HCR of 0.86.

CONCLUSIONS : The CADx model is capable of accurately distinguishing neoplastic from non-neoplastic polyps and provides a histology map of the spatial distribution of localized histologic predictions along the delineated polyp surface. This capability may improve interpretability and transparency of AI-based RTH and offer intuitive, accurate, and user-friendly guidance in real time for the clinical management and documentation of optical histology results.

Rodriguez-Diaz Eladio, Baffy György, Lo Wai-Kit, Mashimo Hiroshi, Vidyarthi Gitanjali, Mohapatra Shyam S, Singh Satish K


artificial intelligence, augmented visualization, colorectal neoplasm, colorectal polyps, computer-aided diagnosis, deep learning, endoscopy, histology map, machine learning, near-focus narrow-band imaging, optical biopsy, real-time polyp histology

General General

Using Machine Learning to Predict Rehabilitation Outcomes in Post-acute Hip Fracture Patients.

In Archives of physical medicine and rehabilitation ; h5-index 61.0

OBJECTIVE : To use machine learning-based methods in designing a predictive model of rehabilitation outcomes for post-acute hip-fractured patients.

DESIGN : A retrospective analysis using linear models, AdaBoost, CatBoost, ExtraTrees, K-Nearest Neighbors, RandomForest, Support vector machine, XGBoost, and voting of all models to develop and validate a predictive model.

SETTING : A university-affiliated 300-bed major post-acute geriatric rehabilitation center.

PARTICIPANTS : Consecutive hip-fractured patients (n=1625) admitted to an acute rehabilitation department.

MAIN OUTCOME MEASURES : The Functional Independence Measure (FIM) instrument, motor-FIM (mFIM), and the relative functional gain on mFIM (mFIM effectiveness) as a continuous and binary variable. Ten predictive models were created: base models (linear/logistic regression), and eight machine learning models (AdaBoost, CatBoost, ExtraTrees, K-Nearest Neighbors, RandomForest, Support vector machine, XGBoost, and a voting ensemble). R-squared was used to evaluate their performance in predicting a continuous outcome variable, and the area under the receiver operating characteristic curve was used to evaluate the binary outcome. A paired two-tailed t-test compared the results of the different models.

RESULTS : Machine learning-based models yielded better results than the linear/logistic regression models in predicting rehabilitation outcomes. The three most important predictors of the mFIM effectiveness score were: the MMSE, pre-fracture mFIM scores, and age; of the discharge mFIM score: the admission mFIM, MMSE and pre-fracture mFIM scores. The most contributing factors for favorable outcomes (mFIM effectiveness> median) with higher prediction confidence level were: high MMSE (25.7±2.8), high pre-facture mFIM (81.5±7.8) and high admission mFIM (48.6±8) scores. We present a simple prediction instrument for estimating the expected performance of post-acute hip-fractured patients.

CONCLUSION : Use of machine learning models to predict rehabilitation outcomes of post-acute hip-fractured patients is superior to the linear/logistic regression models. The higher the MMSE, pre-fracture mFIM and admission mFIM scores are, the higher the confidence levels of the predicted parameters.

Shtar Guy, Rokach Lior, Shapira Bracha, Nissan Ran, Hershkovitz Avital


Hip fracture, Machine learning, Outcome, Rehabilitation

General General

Inner speech.

In Wiley interdisciplinary reviews. Cognitive science

Inner speech travels under many aliases: the inner voice, verbal thought, thinking in words, internal verbalization, "talking in your head," the "little voice in the head," and so on. It is both a familiar element of first-person experience and a psychological phenomenon whose complex cognitive components and distributed neural bases are increasingly well understood. There is evidence that inner speech plays a variety of cognitive roles, from enabling abstract thought, to supporting metacognition, memory, and executive function. One active area of controversy concerns the relation of inner speech to auditory verbal hallucinations (AVHs) in schizophrenia, with a common proposal being that sufferers of AVH misidentify their own inner speech as being generated by someone else. Recently, researchers have used artificial intelligence to translate the neural and neuromuscular signatures of inner speech into corresponding outer speech signals, laying the groundwork for a variety of new applications and interventions. This article is categorized under: Philosophy > Foundations of Cognitive Science Linguistics > Language in Mind and Brain Philosophy > Consciousness Philosophy > Psychological Capacities.

Langland-Hassan Peter


inner speech, language, auditory verbal hallucination, metacognition, inner voice

General General

Non-Invasive Setup for Grape Maturation Classification using Deep Learning.

In Journal of the science of food and agriculture

BACKGROUND : The San Francisco Valley region from Brazil is known worldwide for its fruit production and exportation, especially grapes and wines. The grapes have high quality due not only to the excellent morphological characteristics, but also to the pleasant taste of their fruits. Such features are obtained because of the climatic conditions present in the region. In addition to the favorable climate for grape cultivation, harvesting at the right time interferes with fruit properties.

RESULTS : This work aims to define grape maturation stage of Syrah and Cabernet Sauvignon cultivars with the aid of deep learning models. The idea of working with these algorithms came from the fact that the techniques commonly used to find the ideal harvesting point are invasive, expensive, and take a long time to get their results. In this work, Convolutional Neural Networks (CNNs) were used in an image classification system, in which grape images were acquired, pre-processed and classified based on their maturation stage. Images were acquired with varying illuminants that were considered as parameters of the classification models, as well as the different post-harvesting weeks. The best models achieved maturation classification accuracy of 93.41% and 72.66% for Syrah and Cabernet Sauvignon, respectively.

CONCLUSIONS : It was possible to correctly classify wine grapes using computational intelligent algorithms with high accuracy, regarding the harvesting time, corroborating chemometric results. This article is protected by copyright. All rights reserved.

Ramos Rodrigo Pereira, Gomes Jéssica Santana, Prates Ricardo Menezes, Simas Filho Eduardo F, Teruel Mederos Barbara Janet, Costa Daniel Dos Santos


Deep learning, Grape maturation, Image processing, Post-harvest

Radiology Radiology

Deep learning object detection of maxillary cyst-like lesions on panoramic radiographs: preliminary study.

In Oral radiology

OBJECTIVES : This study aimed to examine the performance of deep learning object detection technology for detecting and identifying maxillary cyst-like lesions on panoramic radiography.

METHODS : Altogether, 412 patients with maxillary cyst-like lesions (including several benign tumors) were enrolled. All panoramic radiographs were arbitrarily assigned to the training, testing 1, and testing 2 datasets of the study. The deep learning process of the training images and labels was performed for 1000 epochs using the DetectNet neural network. The testing 1 and testing 2 images were applied to the created learning model, and the detection performance was evaluated. For lesions that could be detected, the classification performance (sensitivity) for identifying radicular cysts or other lesions were examined.

RESULTS : The recall, precision, and F-1 score for detecting maxillary cysts were 74.6%/77.1%, 89.8%/90.0%, and 81.5%/83.1% for the testing 1/testing 2 datasets, respectively. The recall was higher in the anterior regions and for radicular cysts. The sensitivity was higher for identifying radicular cysts than for other lesions.

CONCLUSIONS : Using deep learning object detection technology, maxillary cyst-like lesions could be detected in approximately 75-77%.

Watanabe Hirofumi, Ariji Yoshiko, Fukuda Motoki, Kuwada Chiaki, Kise Yoshitaka, Nozawa Michihito, Sugita Yoshihiko, Ariji Eiichiro


Deep learning, Maxillary cysts, Object detection, Panoramic radiography, Radicular cysts

Surgery Surgery

Prediction of early recurrence of hepatocellular carcinoma after resection using digital pathology images assessed by machine learning.

In Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc

Hepatocellular carcinoma (HCC) is a representative primary liver cancer caused by long-term and repetitive liver injury. Surgical resection is generally selected as the radical cure treatment. Because the early recurrence of HCC after resection is associated with low overall survival, the prediction of recurrence after resection is clinically important. However, the pathological characteristics of the early recurrence of HCC have not yet been elucidated. We attempted to predict the early recurrence of HCC after resection based on digital pathologic images of hematoxylin and eosin-stained specimens and machine learning applying a support vector machine (SVM). The 158 HCC patients meeting the Milan criteria who underwent surgical resection were included in this study. The patients were categorized into three groups: Group I, patients with HCC recurrence within 1 year after resection (16 for training and 23 for test); Group II, patients with HCC recurrence between 1 and 2 years after resection (22 and 28); and Group III, patients with no HCC recurrence within 4 years after resection (31 and 38). The SVM-based prediction method separated the three groups with 89.9% (80/89) accuracy. Prediction of Groups I was consistent for all cases, while Group II was predicted to be Group III in one case, and Group III was predicted to be Group II in 8 cases. The use of digital pathology and machine learning could be used for highly accurate prediction of HCC recurrence after surgical resection, especially that for early recurrence. Currently, in most cases after HCC resection, regular blood tests and diagnostic imaging are used for follow-up observation; however, the use of digital pathology coupled with machine learning offers potential as a method for objective postoprative follow-up observation.

Saito Akira, Toyoda Hidenori, Kobayashi Masaharu, Koiwa Yoshinori, Fujii Hiroki, Fujita Koji, Maeda Atsuyuki, Kaneoka Yuji, Hazama Shoichi, Nagano Hiroaki, Mirza Aashiq H, Graf Hans-Peter, Cosatto Eric, Murakami Yoshiki, Kuroda Masahiko