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The family Pteromalidae (Hymenoptera: Chalcidoidea) is reviewed with the goal of providing nomenclatural changes and morphological diagnoses in preparation for a new molecular phylogeny and a book on world fauna that will contain keys to identification. Most subfamilies and some tribes of Pteromalidae are elevated to family level or transferred elsewhere in the superfamily. The resulting classification is a compromise, with the aim of preserving the validity and diagnosability of other, well-established families of Chalcidoidea. The following former subfamilies and tribes of Pteromalidae are elevated to family rank: Boucekiidae, Ceidae, Cerocephalidae, Chalcedectidae, Cleonymidae, Coelocybidae, Diparidae, Epichrysomallidae, Eunotidae, Herbertiidae, Hetreulophidae, Heydeniidae, Idioporidae, Lyciscidae, Macromesidae, Melanosomellidae, Moranilidae, Neodiparidae, Ooderidae, Pelecinellidae (senior synonym of Leptofoeninae), Pirenidae, Spalangiidae, and Systasidae. The following subfamilies are transferred from Pteromalidae: Chromeurytominae and Keiraninae to Megastigmidae, Elatoidinae to Neodiparidae, Nefoeninae to Pelecinellidae, and Erotolepsiinae to Spalangiidae. The subfamily Sycophaginae is transferred to Pteromalidae. The formerly incertae sedis tribe Lieparini is abolished and its single genus Liepara is transferred to Coelocybidae. The former tribe Tomocerodini is transferred to Moranilidae and elevated to subfamily status. The former synonym Tridyminae (Pirenidae) is treated as valid. The following former Pteromalidae are removed from the family and, due to phylogenetic uncertainty, placed as incertae sedis subfamilies or genera within Chalcidoidea: Austrosystasinae, Ditropinotellinae, Keryinae, Louriciinae, Micradelinae, Parasaphodinae, Rivasia , and Storeyinae. Within the remaining Pteromalidae, Miscogastrinae and Ormocerinae are confirmed as separate from Pteromalinae, the former tribe Trigonoderini is elevated to subfamily status, the former synonym Pachyneurinae is recognized as a distinct subfamily, and as the senior synonym of Austroterobiinae. The tribe Termolampini is synonymized under Pteromalini, and the tribe Uzkini is synonymized under Colotrechnini. Most former Otitesellinae, Sycoecinae, and Sycoryctinae are retained in the tribe Otitesellini, which is transferred to Pteromalinae, and all other genera of Pteromalinae are treated as Pteromalini. Eriaporidae is synonymized with Pirenidae, with Eriaporinae and Euryischiinae retained as subfamilies. Other nomenclatural acts performed here outside of Pteromalidae are as follows: Calesidae: elevation to family rank. Eulophidae: transfer of Boucekelimini and Platytetracampini to Opheliminae, and abolishment of the tribes Elasmini and Gyrolasomyiini. Baeomorphidae is recognized as the senior synonym of Rotoitidae. Khutelchalcididae is formally excluded from Chalcidoidea and placed as incertae sedis within Apocrita. Metapelmatidae and Neanastatidae are removed from Eupelmidae and treated as distinct families. Eopelma is removed from Eupelmidae and treated as an incertae sedis genus in Chalcidoidea. The following subfamilies and tribes are described as new: Cecidellinae (in Pirenidae), Enoggerinae ( incertae sedis in Chalcidoidea), Erixestinae (in Pteromalidae), Eusandalinae (in Eupelmidae), Neapterolelapinae ( incertae sedis in Chalcidoidea), Solenurinae (in Lyciscidae), Trisecodinae (in Systasidae), Diconocarini (in Pteromalidae: Miscogastrinae), and Trigonoderopsini (in Pteromalidae: Colotrechninae). A complete generic classification for discussed taxa is provided.

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Background/aimsHuman grading of digital images from diabetic retinopathy (DR) screening programmes represents a significant challenge, due to the increasing prevalence of diabetes. We evaluate the performance of an automated artificial intelligence (AI) algorithm to triage retinal images from the English Diabetic Eye Screening Programme (DESP) into test-positive/technical failure versus test-negative, using human grading following a standard national protocol as the reference standard.MethodsRetinal images from 30 405 consecutive screening episodes from three English DESPs were manually graded following a standard national protocol and by an automated process with machine learning enabled software, EyeArt v2.1. Screening performance (sensitivity, specificity) and diagnostic accuracy (95% CIs) were determined using human grades as the reference standard.ResultsSensitivity (95% CIs) of EyeArt was 95.7% (94.8% to 96.5%) for referable retinopathy (human graded ungradable, referable maculopathy, moderate-to-severe non-proliferative or proliferative). This comprises sensitivities of 98.3% (97.3% to 98.9%) for mild-to-moderate non-proliferative retinopathy with referable maculopathy, 100% (98.7%,100%) for moderate-to-severe non-proliferative retinopathy and 100% (97.9%,100%) for proliferative disease. EyeArt agreed with the human grade of no retinopathy (specificity) in 68% (67% to 69%), with a specificity of 54.0% (53.4% to 54.5%) when combined with non-referable retinopathy.ConclusionThe algorithm demonstrated safe levels of sensitivity for high-risk retinopathy in a real-world screening service, with specificity that could halve the workload for human graders. AI machine learning and deep learning algorithms such as this can provide clinically equivalent, rapid detection of retinopathy, particularly in settings where a trained workforce is unavailable or where large-scale and rapid results are needed.