Artificial Intelligence in Multiphoton Tomography: Atopic Dermatitis Diagnosis

The diagnostic possibilities of multiphoton tomography (MPT) in dermatology have already been demonstrated. Nevertheless, the analysis of MPT data is still time-consuming and operator dependent. We propose a fully automatic approach based on convolutional neural networks (CNNs) to fully realize the potential of MPT. In total, 3,663 MPT images combining both morphological and metabolic information were acquired from atopic dermatitis (AD) patients and healthy volunteers. These were used to train and tune CNNs to detect the presence of living cells, and if so, to diagnose AD, independently of imaged layer or position. The proposed algorithm correctly diagnosed AD in 97.0 ± 0.2% of all images presenting living cells. The diagnosis was obtained with a sensitivity of 0.966 ± 0.003, specificity of 0.977 ± 0.003 and F-score of 0.964 ± 0.002. Relevance propagation by deep Taylor decomposition was used to enhance the algorithm’s interpretability. Obtained heatmaps show what aspects of the images are important for a given classification. We showed that MPT imaging can be combined with artificial intelligence to successfully diagnose AD. The proposed approach serves as a framework for the automatic diagnosis of skin disorders using MPT

Translation of two-photon microscopy to the clinic: multimodal multiphoton CARS tomography of in vivo human skin

Two-photon microscopes have been successfully translated into clinical imaging tools to obtain high-resolution optical biopsies for in vivo histology. We report on clinical multiphoton coherent anti-Stokes Raman spectroscopy (CARS) tomography based on two tunable ultrashort near-infrared laser beams for label-free in vivo multimodal skin imaging. The multiphoton biopsies were obtained with the compact tomograph “MPTflex-CARS” using a photonic crystal fiber, an optomechanical articulated arm, and a four-detector-360 deg measurement head. The multiphoton tomograph has been employed to patients in a hospital with diseased skin. The clinical study involved 16 subjects, 8 patients with atopic dermatitis, 4 patients with psoriasis vulgaris, and 4 volunteers served as control. Two-photon cellular autofluorescence lifetime, second harmonic generation (SHG) of collagen, and CARS of intratissue lipids/proteins have been detected with single-photon sensitivity, submicron spatial resolution, and picosecond temporal resolution. The most important signal was the autofluorescence from nicotinamide adenine dinucleotide [NAD(P)H]. The SHG signal from collagen was mainly used to detect the epidermal–dermal junction and to calculate the ratio elastin/collagen. The CARS/Raman signal provided add-on information. Based on this view on the disease-affected skin on a subcellular level, skin areas affected by dermatitis and by psoriasis could be clearly identified. Multimodal multiphoton tomographs may become important label-free clinical high-resolution imaging tools for in vivo skin histology to realize rapid early diagnosis as well as treatment control

Prediction and diagnosis of bladder cancer recurrence based on urinary content of <it>hTERT</it>, <it>SENP1</it>, <it>PPP1CA</it>, and <it>MCM5 </it>transcripts

Abstract Background Identification of urinary biomarkers for detection of bladder cancer recurrence would be beneficial to minimize the frequency of cystoscopy. Our objective was to determine the usability of urine content of mRNA in the detection and prediction of bladder cancer recurrence. Methods We analyzed 123 prospectively cross-sectional collected urine samples from 117 patients with bladder cancer (12 incident cancers and 111 control visits). We used biopsies from cystoscopies as diagnostic criteria for recurrence, and followed the patients for a median time of 28.5 months (range 0-44 months). We measured the levels of hTERT, SENP1, PPP1CA, and MCM5 mRNA in urine by q-RT- PCR. Results We found significant differences in urinary content of hTERT (p SENP1 (p MCM5 (p PPP1CA (p hTERT: 63/73, SENP1: 56/78, MCM5: 63/66, and PPP1CA: 69/63, respectively. Including follow-up data resulted in sensitivity and specificity values for hTERT: 62/84, SENP1:53/84, MCM5: 61/73, and PPP1CA: 65/66. Interestingly, at non-tumor visits the urinary content of especially hTERT (p = 0.0001) and MCM5 (p = 0.02) were significantly associated with subsequent tumour recurrence. Combining the markers with cytology improved the detection. The best combination was hTERT and cytology with a sensitivity of 71% and a specificity of 86% after follow-up. Further prospective validation or registration studies needs to be carried out before clinical use. Conclusions We could use the urinary content of hTERT, SENP1, PPP1CA, and MCM5 to detect bladder cancer recurrence. All markers showed a higher sensitivity than cytology. The detection rate improved when including cytology results, but also the combination of hTERT and MCM5 increased the detection rate. Furthermore, hTERT and MCM5 levels predicted subsequent tumor recurrences.</p