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Automated Pain Spots Recognition Algorithm Provided by a Web Service–Based Platform: Instrument Validation Study

Automated Pain Spots Recognition Algorithm Provided by a Web Service–Based Platform: Instrument Validation Study

There are different methods for scanning PDs, including using a flatbed scanner, a device that scans flat, thin documents placed on a glass window; a handheld scanner, a portable device that can scan images while being moved over them; a drum scanner, a high-end scanner that uses a rotating cylinder to capture the image; a multifunctional printer scanner, a printer that also includes a scanner function; and a virtual scanner, a software that can use a camera to scan images.

Corrado Cescon, Giuseppe Landolfi, Niko Bonomi, Marco Derboni, Vincenzo Giuffrida, Andrea Emilio Rizzoli, Paolo Maino, Eva Koetsier, Marco Barbero

JMIR Mhealth Uhealth 2024;12:e53119

Scanxiety Conversations on Twitter: Observational Study

Scanxiety Conversations on Twitter: Observational Study

“Scanxiety,” or scan-associated anxiety, was a term first coined by a patient writing for Time magazine to describe the distress before, during, or after a scan [1]. Scans are often routine in cancer care [2] regardless of cancer type or stage. They are performed for screening, diagnosis, surveillance, and monitoring of cancer and may occur on a regular schedule or in response to new symptoms, signs, or other investigation results.

Kim Tam Bui, Zoe Li, Haryana M Dhillon, Belinda E Kiely, Prunella Blinman

JMIR Cancer 2023;9:e43609

The Cole Relaxation Frequency as a Parameter to Identify Cancer in Lung Tissue: Preliminary Animal and Ex Vivo Patient Studies

The Cole Relaxation Frequency as a Parameter to Identify Cancer in Lung Tissue: Preliminary Animal and Ex Vivo Patient Studies

If the CRF value (fc) is in the range of 105 Hz-2.1 x 106 Hz (defined as the cancerous range) [8], then the scan is characterized as “cancer,” otherwise “no cancer.” If there is no CRF value (fc) present, then the scan is characterized as “no cancer,” solely based on Nyquist path analysis results. Typically, multiple scans are taken from each sample at slightly different locations. If at least one scan is cancer, then the sample is characterized as “cancer.”

Les Bogdanowicz, Onur Fidaner, Donato Ceres, Alexander Grycuk, Martina Guidetti, David Demos

JMIR Biomed Eng 2022;7(1):e35346

Prediction of COVID-19 Severity Using Chest Computed Tomography and Laboratory Measurements: Evaluation Using a Machine Learning Approach

Prediction of COVID-19 Severity Using Chest Computed Tomography and Laboratory Measurements: Evaluation Using a Machine Learning Approach

Patchy ipsilateral pulmonary consolidations are visible on a computerized tomography (CT) scan initially, during the early course of COVID-19. As the infection progresses, the consolidations are reduced and appear as bilateral ground-glass opacities, marking the prominent radiological features of COVID-19 [8]. The “white lung” radiograph, a characteristic finding suggesting that the patient urgently requires oxygen inhalation, has only been observed in a few critical patients with ARDS [9-11].

Daowei Li, Qiang Zhang, Yue Tan, Xinghuo Feng, Yuanyi Yue, Yuhan Bai, Jimeng Li, Jiahang Li, Youjun Xu, Shiyu Chen, Si-Yu Xiao, Muyan Sun, Xiaona Li, Fang Zhu

JMIR Med Inform 2020;8(11):e21604

COVID-19 Pneumonia Diagnosis Using a Simple 2D Deep Learning Framework With a Single Chest CT Image: Model Development and Validation

COVID-19 Pneumonia Diagnosis Using a Simple 2D Deep Learning Framework With a Single Chest CT Image: Model Development and Validation

A chest computed tomography (CT) scan can be used as an important tool to diagnose COVID-19 in cases with false negative results by RT-PCR [6-9]. Recently, a multinational consensus statement from the Fleischner Society was issued to guide chest imaging during the COVID-19 pandemic in different clinical settings [6].

Hoon Ko, Heewon Chung, Wu Seong Kang, Kyung Won Kim, Youngbin Shin, Seung Ji Kang, Jae Hoon Lee, Young Jun Kim, Nan Yeol Kim, Hyunseok Jung, Jinseok Lee

J Med Internet Res 2020;22(6):e19569