The nociceptive spinal reflex system performs highly precise sensorimotor transformations that require functionally specified synaptic strengths. The specification is gradually attained during early development and appears to be learning dependent. Here we determine the time course of this specification for heat-nociceptive tail withdrawal reflexes and analyze which types of primary afferents are important for the learning by applying various forms of noninvasive sensory deprivations. The percentage of erroneous heat-nociceptive tail withdrawal reflexes (i.e., movements directed toward the stimulation) decreased gradually from 64.1 +/- 2.5% (mean +/- SEM) to <10% during postnatal days 10-21. This improvement was completely blocked by anesthetizing the tail during the adaptation period, confirming that an experience-dependent mechanism is involved in the specification of synaptic strengths. However, the results show that the adaptation occurs to a significant extent despite local analgesia and protection of the tail from noxious input, provided that tactile sensitivity is preserved. Therefore, it appears that a nociceptive input is not necessary for the adaptation, and that input from tactile receptors can be used to guide the nociceptive synaptic organization during development. Sensory deprivation in the adult rat failed to affect the heat-nociceptive withdrawal reflex system, indicating that the adaptation has a "critical period" during early development. These findings provide a key to the puzzle of how pain-related systems can be functionally adapted through experience despite the rare occurrence of noxious input during early life.

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Objectives Prostate volume (PV) in combination with prostate specific antigen (PSA) yields PSA density which is an increasingly important biomarker. Calculating PV from MRI is a time-consuming, radiologist-dependent task. The aim of this study was to assess whether a deep learning algorithm can replace PI-RADS 2.1 based ellipsoid formula (EF) for calculating PV. Methods Eight different measures of PV were retrospectively collected for each of 124 patients who underwent radical prostatectomy and preoperative MRI of the prostate (multicenter and multi-scanner MRI’s 1.5 and 3 T). Agreement between volumes obtained from the deep learning algorithm (PVDL) and ellipsoid formula by two radiologists (PVEF1 and PVEF2) was evaluated against the reference standard PV obtained by manual planimetry by an expert radiologist (PVMPE). A sensitivity analysis was performed using a prostatectomy specimen as the reference standard. Inter-reader agreement was evaluated between the radiologists using the ellipsoid formula and between the expert and inexperienced radiologists performing manual planimetry. Results PVDL showed better agreement and precision than PVEF1 and PVEF2 using the reference standard PVMPE (mean difference [95% limits of agreement] PVDL: −0.33 [−10.80; 10.14], PVEF1: −3.83 [−19.55; 11.89], PVEF2: −3.05 [−18.55; 12.45]) or the PV determined based on specimen weight (PVDL: −4.22 [−22.52; 14.07], PVEF1: −7.89 [−30.50; 14.73], PVEF2: −6.97 [−30.13; 16.18]). Inter-reader agreement was excellent between the two experienced radiologists using the ellipsoid formula and was good between expert and inexperienced radiologists performing manual planimetry. Conclusion Deep learning algorithm performs similarly to radiologists in the assessment of prostate volume on MRI. Key Points • A commercially available deep learning algorithm performs similarly to radiologists in the assessment of prostate volume on MRI. • The deep-learning algorithm was previously untrained on this heterogenous multicenter day-to-day practice MRI data set.