Efficiently improving the Wi-Fi-based human activity recognition, using auditory features, autoencoders, and fine-tuning.

Human activity recognition (HAR) based on Wi-Fi signals has attracted significant attention due to its convenience and the availability of infrastructures and sensors. Channel State Information (CSI) measures how Wi-Fi signals propagate through the environment. However, many scenarios and applications have insufficient training data due to constraints such as cost, time, or resources. This poses a challenge for achieving high accuracy levels with machine learning techniques. In this study, multiple deep learning models for HAR were employed to achieve acceptable accuracy levels with much less training data than other methods. A pretrained encoder trained from a Multi-Input Multi-Output Autoencoder (MIMO AE) on Mel Frequency Cepstral Coefficients (MFCC) from a small subset of data samples was used for feature extraction. Then, fine-tuning was applied by adding the encoder as a fixed layer in the classifier, which was trained on a small fraction of the remaining data. The evaluation results (K-fold cross-validation and K = 5) showed that using only 30% of the training and validation data (equivalent to 24% of the total data), the accuracy was improved by 17.7% compared to the case where the encoder was not used (with an accuracy of 79.3% for the designed classifier, and an accuracy of 90.3% for the classifier with the fixed encoder). While by considering more calculational cost, achieving higher accuracy using the pretrained encoder as a trainable layer is possible (up to 2.4% improvement), this small gap demonstrated the effectiveness and efficiency of the proposed method for HAR using Wi-Fi signals.

A novel artificial intelligence model for diagnosing Acanthamoeba keratitis through confocal microscopy.

PURPOSE: To develop an artificial intelligence (AI) model to diagnose Acanthamoeba keratitis (AK) based on in vivo confocal microscopy (IVCM) images extracted from the Heidelberg Retinal Tomograph 3 (HRT 3). METHODS: This retrospective cohort study utilized HRT 3 IVCM images from patients who had received a culture-confirmed diagnosis of AK between 2013 and 2021 at Massachusetts Eye and Ear. Two cornea specialists independently labeled the images as AK or nonspecific finding (NSF) in a blind manner. Deep learning tasks were then conducted through Python and TensorFlow. Distinguishing between AK and NSF was designed as the task and completed through a devised convolutional neural network. RESULTS: A dataset of 3312 confocal images from 17 patients with a culture-confirmed diagnosis of AK was used in this study. The inter-rater agreement for identifying the presence or absence of AK in IVCM images was 84 %, corresponding to a total of 2782 images on which both observers agreed and which were included in the model. 1242 and 1265 images of AK and NSF, respectively, were utilized in the training and validation sets, and 173 and 102 images of AK and NSF, respectively, were utilized in the evaluation set. Our model had an accuracy, sensitivity, and specificity of 76 % each, and a precision of 78 %. CONCLUSIONS: We developed an HRT-based IVCM AI model for AK diagnosis utilizing culture-confirmed cases of AK. We achieved good accuracy in diagnosing AK and our model holds significant promise in the clinical application of AI in improving early AK diagnosis.

Semi-supervised segmentation of retinoblastoma tumors in fundus images.

Retinoblastoma is a rare form of cancer that predominantly affects young children as the primary intraocular malignancy. Studies conducted in developed and some developing countries have revealed that early detection can successfully cure over 90% of children with retinoblastoma. An unusual white reflection in the pupil is the most common presenting symptom. Depending on the tumor size, shape, and location, medical experts may opt for different approaches and treatments, with the results varying significantly due to the high reliance on prior knowledge and experience. This study aims to present a model based on semi-supervised machine learning that will yield segmentation results comparable to those achieved by medical experts. First, the Gaussian mixture model is utilized to detect abnormalities in approximately 4200 fundus images. Due to the high computational cost of this process, the results of this approach are then used to train a cost-effective model for the same purpose. The proposed model demonstrated promising results in extracting highly detailed boundaries in fundus images. Using the Sørensen-Dice coefficient as the comparison metric for segmentation tasks, an average accuracy of 93% on evaluation data was achieved.

From the diagnosis of infectious keratitis to discriminating fungal subtypes; a deep learning-based study.

Infectious keratitis (IK) is a major cause of corneal opacity. IK can be caused by a variety of microorganisms. Typically, fungal ulcers carry the worst prognosis. Fungal cases can be subdivided into filamentous and yeasts, which shows fundamental differences. Delays in diagnosis or initiation of treatment increase the risk of ocular complications. Currently, the diagnosis of IK is mainly based on slit-lamp examination and corneal scrapings. Notably, these diagnostic methods have their drawbacks, including experience-dependency, tissue damage, and time consumption. Artificial intelligence (AI) is designed to mimic and enhance human decision-making. An increasing number of studies have utilized AI in the diagnosis of IK. In this paper, we propose to use AI to diagnose IK (model 1), differentiate between bacterial keratitis and fungal keratitis (model 2), and discriminate the filamentous type from the yeast type of fungal cases (model 3). Overall, 9329 slit-lamp photographs gathered from 977 patients were enrolled in the study. The models exhibited remarkable accuracy, with model 1 achieving 99.3%, model 2 at 84%, and model 3 reaching 77.5%. In conclusion, our study offers valuable support in the early identification of potential fungal and bacterial keratitis cases and helps enable timely management.