Fig. 1. Schematics of typical application scenarios of intelligence teleconsultation system for skin tumors built by our team. (a) Skin tumor self-screening; (b) tele-consultation

Fig. 2. Intelligence teleconsultation system for skin tumors. (a) System hardware composition; (b) schematic of AI-CPI module; (c) schematic of principle

Fig. 3. Software design of intelligence teleconsultation system for skin tumors. (a) User interface; (b) workflow under network condition

Fig. 4. Quantification experiment of projection accuracy of AI-CPI module. (a) Schematic of experiment; (b) projection errors at different working distances; (c) projection errors at different working angles

Fig. 5. Quantification experiment of resolution and color accuracy of AI-CPI module. (a) USAF 1951 resolution target plate captured by camera; (b) re-projected target plate; (c) changes in intensity of two regions of interest (ROI) along dotted line; (d) X-rite color card images taken by camera; (e) color card images after color calibration; (f) CIEDE2000 histogram before and after color correction

Fig. 6. Teleconsultation experiment of skin tumor using proposed system. (a) Subject and system at local site; (b) experienced expert is viewing images of skin tumor; (c) deep learning algorithm deployed in system diagnoses lesion as basal cell carcinoma through dermoscopic images; (d) expert confirms that diagnosis result of algorithm is correct, and draws annotations to discuss surgical plan; annotations projected on subject skin surface by proposed system are used to indicate (e) area of tissue removed and (f) area of skin flap reconstruction

Table 1. Comparison of diagnosis results of algorithm in system with those of three dermatologists

Table 2. Comparison of diagnostic results of dermatologists in two groups with or without AI prompts