Computer vision system for counting crustacean larvae by detection by Chen Rothschild, Eliahu David Aflalo, Inbar Kedem, Guy Farjon, Yitzhak Yitzhaky, Amir Sagi, Yael Edan

“…For the iPhone 11 camera, two different illumination conditions were tested, and in each condition, 110 images were acquired. …”
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Effectiveness of an image analyzing AI-based Digital Health Technology to identify Non-Melanoma Skin Cancer and other skin lesions: results of the DERM-003 study by Helen Marsden, Caroline Morgan, Stephanie Austin, Claudia DeGiovanni, Marcello Venzi, Polychronis Kemos, Jack Greenhalgh, Dan Mullarkey, Ioulios Palamaras

“…Suspicious skin lesions that were suitable for photography were photographed with 3 smartphone cameras (iPhone 6S, iPhone 11, Samsung 10) with a DL1 dermoscopic lens attachment. …”
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Towards Using Police Officers’ Business Smartphones for Contactless Fingerprint Acquisition and Enabling Fingerprint Comparison against Contact-Based Datasets by Christof Kauba, Dominik Söllinger, Simon Kirchgasser, Axel Weissenfeld, Gustavo Fernández Domínguez, Bernhard Strobl, Andreas Uhl

“…The insights gained throughout this study serve as guidance for future work towards developing a contactless mobile fingerprint solution based on the iPhone 11, working without any additional hardware. …”
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Machine Learning Techniques for Estimating Soil Moisture from Smartphone Captured Images by Muhammad Riaz Hasib Hossain, Muhammad Ashad Kabir

“…Therefore, 629 images of 38 soil samples were taken from seven areas in Sydney, Australia, and split into four datasets based on the image-capturing devices used (iPhone 6s and iPhone 11 Pro) and the lighting circumstances (direct and indirect sunlight). …”
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Comparison of Various Devices Used in the Evaluation of Vertical Jump Height by Halil Korkmaz, Ahmet Alptekin, Özlem Köklü

“…The countermovement jump performances were captured using an iPhone 11 (Apple Inc., USA). The experimental setup involved using three high-speed cameras, specifically the My Jump 2 and SIMI Motion 7.5. …”
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Dual-Track Lifelong Machine Learning-Based Fine-Grained Product Quality Analysis by Xianbin Hong, Sheng-Uei Guan, Nian Xue, Zhen Li, Ka Lok Man, Prudence W. H. Wong, Dawei Liu

“…The extensive experimental results show that the proposed dual-track approach can provide reasonable fine-grained sentiment analyses to product reviews and remarkably achieve a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>133</mn><mo>%</mo></mrow></semantics></math></inline-formula> promotion of the Macro-F1 score on the Twitter sentiment classification task and a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>27.12</mn><mo>%</mo></mrow></semantics></math></inline-formula> promotion of the Macro-F1 score on an Amazon iPhone 11 sentiment classification task, respectively.…”
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Smartphone-Based Artificial Intelligence–Assisted Prediction for Eyelid Measurements: Algorithm Development and Observational Validation Study by Hung-Chang Chen, Shin-Shi Tzeng, Yen-Chang Hsiao, Ruei-Feng Chen, Erh-Chien Hung, Oscar K Lee

“…Six orbital photographs (bilateral primary gaze, up-gaze, and down-gaze) were taken using a smartphone (iPhone 11 Pro Max). The gold-standard measurements and normalized eye photographs were obtained from these orbital photographs and compiled using AI-assisted software to create MRD1, MRD2, and LF models. …”
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Smartphone Slit Lamp Imaging—Usability and Quality Assessment by Daniel Rudolf Muth, Frank Blaser, Nastasia Foa, Pauline Scherm, Wolfgang Johann Mayer, Daniel Barthelmes, Sandrine Anne Zweifel

“…The participant group used an Apple <i>iPhone 11</i> mounted on a slit lamp via a removable <i>SlitREC</i> smartphone adapter (Custom Surgical GmbH, Munich, Germany). …”
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Multi-Barley Seed Detection Using iPhone Images and YOLOv5 Model by Yaying Shi, Jiayi Li, Zeyun Yu, Yin Li, Yangpingqing Hu, Lushen Wu

“…We captured images of these original barley seeds using an iPhone 11 Pro. This study used two mixed datasets, including a single-barley seed dataset and a multi-barley seed dataset, to improve the detection accuracy of multi-barley seeds. …”
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Validation of three-dimensional facial imaging captured with smartphone-based photogrammetry application in comparison to stereophotogrammetry system by James Andrews, Abdulraheem Alwafi, Yashodhan M. Bichu, Benjamin T. Pliska, Nesrine Mostafa, Bingshuang Zou

“…Purpose: This study was designed to validate both the trueness and precision of the iPhone 11 Pro smartphone TrueDepth NIR scanner in conjunction with the Bellus3D Face app in capturing 3D facial images in a sample of adult participants in comparison to the conventional 3dMDface stereophotogrammetry system. …”
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