BACKGROUND: Significant interest has been recently shown for using monolithic scintillation crystals in molecular imaging systems, such as positron emission tomography (PET) scanners. Monolithic-based PET scanners result in a lower cost and higher sensitivity, in contrast to systems based on the more conventional pixellated configuration. The monolithic design allows one to retrieve depth-of-interaction information of the impinging 511 keV photons without the need for additional hardware materials or complex positioning algorithms. However, the so-called edge-effect inherent to monolithic-based approaches worsens the detector performance toward the crystal borders due to the truncation of the light distribution, thus decreasing positioning accuracy. PURPOSE: The main goal of this work is to experimentally demonstrate the detector performance improvement when machine-learning artificial neural-network (NN) techniques are applied for positioning estimation in multiple monolithic scintillators optically coupled side-by-side. METHODS: In this work, we show the performance evaluation of two LYSO crystals of 33 × 25.4 × 10 mm3 optically coupled by means of a high refractive index adhesive compound (Meltmount, refractive index n = 1.70). A 12 × 12 silicon photomultiplier array has been used as photosensor. For comparison, the same detector configuration was tested for two additional coupling cases: (1) optical grease (n = 1.46) in between crystals, and (2) isolated crystals using black paint with an air gap at the interface (named standard configuration). Regarding 2D photon positioning (XY plane), we have tested two different methods: (1) a machine-learning artificial NN algorithm and (2) a squared-charge (SC) centroid technique. RESULTS: At the interface region of the detector, the SC method achieved spatial resolutions of 1.7 ± 0.3, 2.4 ± 0.3, and 2.6 ± 0.4 mm full-width at half-maximum (FWHM) for the Meltmount, grease, and standard configurations, respectively. These values improve to 1.0 ± 0.2, 1.2 ± 0.2, and 1.2 ± 0.3 mm FWHM when the NN algorithm was employed. Regarding energy performance, resolutions of 18 ± 2%, 20 ± 2%, and 23 ± 3% were obtained at the interface region of the detector for Meltmount, grease, and standard configurations, respectively. CONCLUSIONS: The results suggest that optically coupling together scintillators with a high refractive index adhesive, in combination with an NN algorithm, reduces edge-effects and makes it possible to build scanners with almost no gaps in between detectors.