Objectives: With advances in data availability and computing capabilities, artificial intelligence and machine learning technologies have evolved rapidly in recent years. Researchers have taken advantage of these developments in healthcare informatics and created reliable tools to predict or classify diseases using machine learning-based algorithms. To correctly quantify the performance of those algorithms, the standard approach is to use cross-validation, where the algorithm is trained on a training set, and its performance is measured on a validation set. Both datasets should be subject-independent to simulate the expected behavior of a clinical study. This study compares two cross-validation strategies, the subject-wise and the record-wise techniques; the subject-wise strategy correctly mimics the process of a clinical study, while the record-wise strategy does not.Methods: We started by creating a dataset of smartphone audio recordings of subjects diagnosed with and without Parkinson’s disease. This dataset was then divided into training and holdout sets using subject-wise and the record-wise divisions. The training set was used to measure the performance of two classifiers (support vector machine and random forest) to compare six cross-validation techniques that simulated either the subject-wise process or the record-wise process. The holdout set was used to calculate the true error of the classifiers.Results: The record-wise division and the record-wise cross-validation techniques overestimated the performance of the classifiers and underestimated the classification error.Conclusions: In a diagnostic scenario, the subject-wise technique is the proper way of estimating a model’s performance, and record-wise techniques should be avoided.

Objectives: This study presents PD Predict, a machine learning system for Parkinson disease classification using voice as a biomarker. Methods: We first created an original set of recordings from the mPower study, and then extracted several audio features, such as mel-frequency cepstral coefficient (MFCC) components and other classical speech features, using a windowing procedure. The generated dataset was then divided into training and holdout sets. The training set was used to train two machine learning pipelines, and their performance was estimated using a nested subject-wise cross-validation approach. The holdout set was used to assess the generalizability of the pipelines for unseen data. The final pipelines were implemented in PD Predict and accessed through a prediction endpoint developed using the Django REST Framework. PD Predict is a two-component system: a desktop application that records audio recordings, extracts audio features, and makes predictions; and a server-side web application that implements the machine learning pipelines and processes incoming requests with the extracted audio features to make predictions. Our system is deployed and accessible via the following link: https://pdpredict.herokuapp.com/. Results: Both machine learning pipelines showed moderate performance, between 65% and 75% using the nested subject-wise cross-validation approach. Furthermore, they generalized well to unseen data and they did not overfit the training set. Conclusions: The architecture of PD Predict is clear, and the performance of the implemented machine learning pipelines is promising and confirms the usability of smartphone microphones for capturing digital biomarkers of disease.

Integrating artificial intelligence (AI) with drones has emerged as a promising paradigm for advancing agriculture. This bibliometric analysis investigates the current state of research in this transformative domain by comprehensively reviewing 234 pertinent articles from Scopus and Web of Science databases. The problem involves harnessing AI-driven drones' potential to address agricultural challenges effectively. To address this, we conducted a bibliometric review, looking at critical components, such as prominent journals, co-authorship patterns across countries, highly cited articles, and the co-citation network of keywords. Our findings underscore a growing interest in using AI-integrated drones to revolutionize various agricultural practices. Noteworthy applications include crop monitoring, precision agriculture, and environmental sensing, indicative of the field’s transformative capacity. This pioneering bibliometric study presents a comprehensive synthesis of the dynamic research landscape, signifying the first extensive exploration of AI and drones in agriculture. The identified knowledge gaps point to future research opportunities, fostering the adoption and implementation of these technologies for sustainable farming practices and resource optimization. Our analysis provides essential insights for researchers and practitioners, laying the groundwork for steering agricultural advancements toward an enhanced efficiency and innovation era.

- PD group selection: If the participant is professionally diagnosed by a doctor AND he or she has a valid date of diagnosis, AND is actually a Parkinsonian, not a caretaker, AND has never had surgery to treat PD nor deep brain stimulation, AND his or her age is valid.

This article investigates the integration of the Internet of Things (IoT), digital twin (DT), and augmented reality (AR) technologies to advance digital services in the management, monitoring, and control of agricultural greenhouses, addressing the sector's technological demands. By leveraging advanced technologies, this research aims to enhance efficiency across various fields, including industry, logistics, and commerce. The core objective is to develop a prototype IoT system, incorporating DT and AR, to create a digital transformation environment. This system provides real-time visualization and monitoring of sensor data from physical IoT devices, enabling user interaction and control through microcontroller-based sensor nodes installed in the greenhouse. Data transmission is achieved using the MQTT protocol, while an AR application facilitates greenhouse visualization and control. The findings demonstrate the system's effectiveness in enabling real-time communication between IoT devices and the AR application, with users successfully performing tasks without prior experience, thereby validating the system's usability and efficiency.