## 1 Introduction
Their study aimed at the efficient and non-intrusive monitoring of animals' activity and physiological changes, also in low light conditions. The image data were captured at 60 frames per second by a thermal camera over a cage with two rats. The authors implemented and investigated two approaches: a two-stage detect-then-segment Mask R-CNN and a single-stage TensorMask network. Various training schemes and parameter setups were used, including the transfer learning of models pre-trained on public datasets. The TensorMask model produced the most accurate results when pre-trained using visible light images and then trained with thermal sequences (mean average precision over 90%). The authors also verified possible benefits from inputting alternative images created by rescaling the original thermal intensities in various automatically adjusted ranges of animal temperature. However, they did not find any such modifications improving the segmentation accuracy. In general, the single-stage methods performed better than two-stage ones if pre-trained; the opposite was true in the case of models trained from scratch. [PERSON] et al. [10] designed a prediction system for analyzing the risk of suffering amyotrophic lateral sclerosis (ALS) based on combinatorial comorbidity patterns. They employed medical history data gathered in electronic medical records (EMR) to support the identification of high-risk ALS subjects in the early stage. One of the main contributions of the study is the weighted Jaccard index (WJI) defined to balance the impact of particular comorbidities in predicting the output risk. Comprehensive patient-group-wise distributions were determined over hundreds of individual- and mid-level diseases characterized over the EMR database. This resulted in the determination of WJI weights. Four machine learning models were trained and validated: logistic regression, random forest, SVM, and XGBoost. The first two were considered most efficient based on seven canonic classification assessment measures, including accuracy exceeding 80%. In general, the individual-level disease classification turned out to be more efficient than the mid-level analysis, and the WJI proved its advantage over the pure Jaccard index in the ALS risk prediction. Finally, [PERSON] et al. [11] delivered a systematic review of the automated detection of sleep stages using deep learning models from the last decade. The authors summarize 36 studies from 2013 to 2020 that employ various deep models to analyze polysomnogram (PSG) recordings. After providing some medical and machine learning background and introducing five or six sleep stages, depending on the assumed standard, they analyze their detection and classification from different points of view. First, the models and architectures are introduced (convolutional or recurrent neural networks, long short-term memory, autoencoders, and hybrid models). Second, the available databases are described. Then, deep learning approaches addressing the sleep stage analysis are presented and compared. Finally, the authors discuss the topic in detail and draw conclusions. Even though electroencephalography (EEG) seems to be the most widely used signal in sleep stage detection, the current review states that it may not be able to work efficiently enough: automated systems should also involve other PSG recordings, e.g., electrooculography (EOG) or electromyography (EMG). The variety of topics covered by the articles in this Special Issue is yet another proof of enormous opportunities for biomedical engineers and scientists familiar with artificial intelligence, machine, and deep learning. These techniques respond to the emerging needs of computer-aided diagnosis and therapy. Their development follows the explosion of computing power; hence, the enormity of diagnostic, signal, or image data has a chance to be processed, pre-analyzed, and presented to the physician within a reasonable time.