Preoperative assessment of mental health rarely occurs within routine surgery. Any screening tool selected to form part of this process must be deemed practical, acceptable and valid by clinicians and consumers alike. This study aims to assess the acceptability and face validity of two existing mental health screening tools to select one for further development and use in the routine surgical setting.Kessler Psychological Distress Scale (K10) and the Somatic and Psychological Health Report-12 (SPHERE-12). Consensus was achieved through a rating of 4 or 5 being given by 70% or more of participants with domains able to remain unchanged. Free text responses were analysed into themes.A survey of clinicians and consumers was conducted from October 2020 to March 2021 at a tertiary hospital in Sydney, Australia. Using a Likert scale , the clinicians evaluated four domains for acceptability and two for validity  and the consumers four domains for acceptability and one for validity  on the preoperative use of the amended A total of 73 participants  were included. The K10 received consensus scores (≥ 70%) in four out of six domains for clinicians , and all five domains for consumers . The SPHERE-12 received consensus scores (≥ 70%) in three domains for clinicians , and three domains for consumers . Six qualitative themes were described including (1) amendments to tool structure and language; (2) scale response options; (3) difficulty with somatic questions; (4) practicality and familiarity with K10; (5) challenges for specific patient cohorts and (6) timing considerations for patients.Adequate acceptability was established for the K10. However further development is required to strengthen its validity for this specific surgical cohort and purpose. Future research to determine the feasibility and acceptability of implementing and using the K10 in the routine surgical setting is now needed.The online version contains supplementary material available at 10.1186/s40359-021-00672-w. In order to provide high-value care within surgery, identifying patient-specific preoperative risk factors is essential for optimising surgical treatment. Whilst screening for medical comorbidities such as cardiovascular or respiratory conditions is well embedded and occurs routinely in preparing patients for surgery, consideration of mental health is rarely, if ever, undertaken . This isGiven this known gap in surgical practise and the critical needs of these patients, it is apparent that simple mental health screening may be a beneficial mechanism for proactively identifying those patients who need further support with their mental health. Although limited, there is evidence to show that even modest preoperative psychological interventions can have favourable effects on surgical patient outcomes by matching a range of interventions to the needs of the patients . In addiA survey of clinicians and consumers was conducted on Royal Prince Alfred (RPA) Hospital campus in Sydney, Australia between October 2020 and March 2021 to measure the acceptability and face validity of two amended mental health screening tools with the intention to select one for further development and pilot testing. A focus on colorectal and cardiothoracic surgical specialties for both the clinicians and consumers was undertaken based on the higher rates of surgical patients with mental health comorbidities identified in these groups , whle (1–2) . Their fle (1–2) , and the0, whle  clarity of wording, (2) suitability of the tool length, (3) ease of use, and (4) their perception on the willingness of patients to complete the tool. Both participants groups used a five-point Likert scale to rate each domain (score range 1–5 with 1 = very unclear/unsuitable/not easy to use/unwilling to complete and 5 = very clear/suitable/easy to use/willing to complete).In addition, to further measure acceptability, the readability and comprehension difficulty of the K10, SPHERE-12 and the additional four questions added were assessed using the commonly used ‘Flesch Reading Ease’ and ‘Flesch-Kincaid grade level’ methods –16. ThesAfter reviewing the K10 and SPHERE-12, the clinicians anonymously completed two survey questions exploring the (5) usefulness and (6) thoroughness of each tool as a whole. The surgical and mental health consumers rated one domain exploring the (5) design of the tool. Again both participants groups used a five-point Likert scale to score each question (score range 1–5 with 1 = not useful/unthorough/not well designed and 5 = very useful/thorough/well designed), which was developed using other measures of face validity as a guide for this assessment , 19. ParOverall the clinicians answered six and the consumers’ five survey questions in total, for each tool Table . There wP < 0.05 was considered statistically significant for all the analyses.Survey response data was stored within a Research Electronic Data Capture (REDCap) database. A basic descriptive analysis was performed according to participant grouping of clinicians and consumers using SPSS Software version 25 .Consensus was achieved through a rating of 4 or 5 being given by 70% or more of participants, with domains able to remain unchanged.This was based on similar approaches previously described whereby between 60 and 80% of support is required , 20, 21.The free text was quantitatively described and qualitatively analysed into themes through thematic analysis as described by Braun and Clarke . This flA total of 73 participants provided feedback on the proposed use of the amended K10 and SPHERE-12 screening tools in the routine surgical setting. This included 51 clinicians, comprising seven colorectal and three cardiothoracic surgeons, 10 anaesthetists, 10 psychiatrists, 10 psychologists, and five colorectal, four mental health, one cardiothoracic and one preadmission CNC. Of the 22 consumer participants, 13 were surgical consumers and nine were mental health consumers.For the clinicians, consensus was achieved for all four acceptability domains of the K10 to remain unchanged. The SPHERE-12 received consesus in three out of the four acceptability domains. Neither the K10 or the SPHERE-12 achieved consensus in either of the two face validity domains  of clinicians and 15 (68.2%) of the consumers. For the K10, supportive comments to use the tool were provided by 9 (27.3%) clinicians and 10 (66.7%) consumers. Unsupportive comments were provided by 5 (15.2%) clinicians and 0 (0%) consumers. For the SPHERE-12, supportive comments to use the tool were provided by 5 (15.2%) clinicians and 7 (46.7%) consumers. Unsupportive comments were provided by 14 (42.4%) clinicians and 3 (20.0%) consumers.The feedback was qualitatively analysed into the following six themes, which are summarised in Table The ‘Flesch Reading Ease’ measure was 78.4 for the K10 indicating it was the simplest and easiest to read and well above the minimum scores required. The SPHERE-12 and the four additional questions added to the tools scored lower at 59.7 and 59.2 respectively, but these were still within the range considered well written and easy to follow (≥ 60). The ‘Flesch-Kincaid grade level’ measure was 5.4 for the K10 indicating the lowest reading grade level at the 5th–6th grade, 6.9 for the SPHERE-12 equivalent to the 6th–7th grade and 8.9 or equivalent to the 8th–9th grade for the four additional questions added to the tools.Feedback from both clinicians and consumers was used to measure the acceptability and face validity of two amended mental health screening tools, the K10 and SPHERE-12, for potential use in the routine surgical setting. Overall the consensus achieved on the domains of the K10 was higher than the SPHERE-12 for both clinicians and consumers. The qualitative feedback was also more supportive of using the K10. This included greater practicality and familiarity with the K10 due to it being widely used in mental health services across Australia, the larger number of response options perceived as being more comprehensive for both patients and clinicians, and the preferred style of questioning. Furthermore, the readability and comprehension difficulty of the K10 was also found to be most suitable using the ‘Flesch Reading Ease’ and ‘Flesch-Kincaid grade level’ measures. Based on this collective quantitative and qualitative feedback, the K10 tool is recommended for further development, implementation and evaluation in the routine surgical setting.It is important to note the findings presented in this study form the first phase of a larger project, which will involve pilot testing the feasibility, acceptability and accuracy of implementing the selected K10 tool into the preoperative screening process for all routine surgical patients booked for cardiothoracic and colorectal surgery. These specialties were chosen based on the results of a previous study demonstrating the higher prevalence of SMI in these groups . This piPrior to the commencement of the pilot and based on the results of this study, it is evident the K10 may benefit from further development to strengthen its face validity to be used in the routine surgical setting. This includes improving the ‘usefulness’ and ‘thoroughness’ of the tool. Interestingly three main themes from the qualitative feedback emerged that related specifically to these two domains. Firstly, the number of response options on the tool was highly polarizing amongst all participant groups. On the one hand, the five response options of the K10 was framed as being more thorough in providing patients with additional options to describe their mental health and more useful in giving clinicians greater detail about the patient. Conversely, the three response options of the SPHERE-12 was felt by both clinicians and patients to be simpler, less overwhelming and more useful for patients to articulate their symptoms. Secondly, the inclusion of somatic questions on the tools was highlighted to be problematic for surgical patients, whereby it might result in false positives, and reduce the usefulness of the screening tools for this particular cohort. Clinicians reported this as a particular concern with the use of the SPHERE-12, which includes six questions focused on somatic symptoms. Finally, the thoroughness of the tools in identifying patients with SMI was raised with conflicting feedback. Overall clinicians viewed the four additional questions as being a positive inclusion that added value and strength to the tools. In contrast, several psychiatrists indicated that due to the known mistrust of the health system and fear of stigmatization, patients with SMI will not respond truthfully to the tool no matter what is included, resulting in false negatives. Whilst these challenges are well known  and impoThe results of this study highlighted interesting variation in the scores provided by the different clinician groups that warrants further consideration. In assessing both the ‘usefulness’ and ‘thoroughness’ of the tools, the surgeons reported the highest and the psychiatrists the lowest consensus percentages for both tools with a statistically significant difference. Likewise, the ‘willingness of surgical patients to complete the tools preoperatively’ was reported with similar differences. Whilst limited inferences can be made from this data, these results possibly reflect the contrasting levels of expertise relating to mental health patients and experiences in screening them. Certainly, surgeons have previously reported a lack of confidence in managing surgical patients with mental illness  and may Whilst this study makes an important contribution to the little researched area of mental health screening in the routine surgical setting, it does have limitations. The findings should be considered within the context of the participants involved and the single campus in which it was conducted. As both the clinician and consumer participants volunteered for the study, and we were unable to determine overall response rates, it may be possible they were biased or had particular experiences that compelled them to participate. The lack of demographic data collected also made this difficult to determine. Challenges in completing the online questionnaire were encountered by the mental health consumers with many opening the online link to the survey and selecting their participant grouping, but then not answering any questions, and thus not being included. Due to the anonymous nature of the study recruitment, it is not known why this was the case however it may reflect the online survey was not user friendly or straight forward to complete. Certainly it was a missed opportunity to seek their valuable feedback. It is evident that pilot testing the REDCap survey with a patient sample beforehand may have improved the completion rates and is an approach to be incorporated in future work. Furthermore, although important input was provided via the free-text item on the survey, there would be benefit for subsequent qualitative studies to examine in greater detail the areas that participants felt required attention including major benefits and drawbacks, along with the proposed administration process. Finally, due to the tools having previously been validated, this study was a high-level assessment of the tools as a whole in regard to their use within a routine surgical setting and did not involve participants considering individual items within the tools. There is an opportunity for future studies to undertake an assessment at a more granular level to further improve the tools for use in this unique setting. Similarly, whilst the methodological approach utilised to assess the tools was in line with other studies , there aIn conclusion, this study recommends the K10 mental health screening tool for preoperative use in the routine surgical setting. Important changes were highlighted as being required within the additional questions added to make the tool fit-for-purpose and focused on patients with SMI. Further development and testing is also needed to establish and strengthen the face validity of the K10 for this specific surgical cohort and purpose.Additional file 1. Additional questions added to the recommended mental health screening tools.


Species-specific differentiation pace in vitro indicates that some aspects of neural differentiation are governed by cell intrinsic properties. Here we describe a novel in vitro human neural-rosette assay that recapitulates dorsal spinal cord differentiation but proceeds more rapidly than in the human embryo, suggesting that it lacks endogenous signalling dynamics. To test whether in vitro conditions represent an intrinsic differentiation pace, human iPSC-derived neural rosettes were challenged by grafting into the faster differentiating chicken embryonic neural tube iso-chronically, or hetero-chronically into older embryos. In both contexts in vitro differentiation pace was initially unchanged, while long-term analysis revealed iso-chronic slowed and hetero-chronic conditions promoted human neural differentiation. Moreover, hetero-chronic conditions did not alter the human neural differentiation programme, which progressed to neurogenesis, while the host embryo advanced into gliogenesis. This study demonstrates that intrinsic properties limit human differentiation pace, and that timely extrinsic signals are required for progression through an intrinsic human neural differentiation programme. ARHGAP11B, has recently been shown to promote the enhanced proliferative capacity of human basal cortical progenitors by augmenting a specific metabolic pathway  is sufficient to generate self-organising dorsal spinal cord rosettes and accompanying neural crest.YFP] sequence is fused to endogenous DCX [Dorsal spinal cord rosettes were further characterised as they progressed through their differentiation programmes . Declinenous DCX ),and  , was nowFinally, we determined the identity of neurons generated in this in vitro assay by immunofluorescence of dorsal interneuron (dIs) subtype-specific markers. In the mouse, embryonic neural tube distinct sensory interneurons arise in specific dorsoventral positions and express distinguishing marker proteins . By D8, These data document the differentiation capacity in this human spinal cord rosette assay and demonstrate that it reproducibly generates dorsal neural differentiation programmes. These begin with dorsal neural progenitors and emerging neural crest, progress to neurogenesis  and a later switch to gliogenesis: a sequence that recapitulates the temporal order of differentiation observed in the spinal cord of mouse and avian embryos .This conservation in the sequence of the dorsal neural differentiation programme contrasts with the longer duration of human embryogenesis in comparison with that observed in the mouse and chicken. Indeed, the pace of differentiation in the developing nervous system and that of body axis segmentation is slower in human embryos . These lThe pattern and timing of key marker protein expression in human embryonic spinal cord was investigated using tissue samples provided by the HDBR  and avaiThe sacral spinal cord at CS12/13 stage was composed of progenitors expressing dorsal (PAX7) and ventral (OLIG2) markers  as well In the next more rostral, lumbar region, PAX7 and OLIG2 domains have enlarged , SNAI2-eImportantly, dorsal interneuron markers LHX2 (dI1) and ISLET1/HB9 (dI3) were also not detected at thoracic levels , . This isSOX10 mRNA expression (http://hdbratlas.org), and here this indicates that these neurons appear ~4 days earlier in D10 rosettes   were differentiated into NMP-like cells and then into D6 neural rosettes as described above . MultiplThe precision of dorsal neural tube removal was evaluated in a subset of embryos immediately after operation and this showed that tissue removed included only neural crest and neural progenitors expressing respectively SNAI2 and PAX7, as found in D6 human neural rosettes  and 2. TAfter 2 days incubation, grafted cells retained apico-basal polarity as indicated by localised ZO-1 and N-cadherin expression  and the One possibility to consider is that a community effect  operatesAnalysis of neural crest differentiation in transplanted rosettes after 2 days further confirmed the asynchrony between human and host chicken cell differentiation. SNAI2-expressing neural crest cells were still found within these rosettes, while delamination of host neural crest was now complete, as indicated by absence of Snail2 positive cells in the chicken dorsal neural tube . MoreoveIntriguingly, despite this apparent lack of influence of the chick host on grafted human cells, we found some evidence that human cells altered chick neural crest differentiation. At this stage, chicken neural crest cells have normally delaminated and migrated to the dorsal root ganglia (DRGs) . HoweverTogether these findings indicate that after 2 days, differentiation in grafted human spinal cord rosettes progresses at the same pace as in vitro despite exposure to the more rapidly differentiating environment of the chicken host embryo.To address whether the differentiation pace in grafted human rosettes continues in synchrony with that in vitro, further analysis was undertaken 5 days after transplantation. At this time most human cells were incorporated within the host neuroepithelium, either as cell groups or single cells  and the Furthermore, day 5 transplanted rosettes lacked the neural crest markers , but these were detected in rosettes cultured in parallel in vitro . This inOverall, this longer-term analysis of differentiation in transplanted human rosettes revealed that rather than proceeding more rapidly in the chicken embryo, the apparently intrinsic differentiation pace of human dorsal neural progenitors and neural crest is not sustained and cells in the human rosettes largely remain in a neural progenitor cell state.To assess whether the differentiation pace of human neural rosette cells can be altered by transplantation into a more developmentally advanced chicken neuroepithelium, D6 neural rosettes were next transplanted into the dorsal spinal cord of older chicken embryos   and 9A: Strikingly, 5 days after hetero-chronic transplantation human neural rosettes now exhibited fewer PAX7-expressing cells and increased P27 and ISLET1 expressing cells in comparison to the same age rosettes in vitro (or following isochronic transplantation) . All GFPThis study presents a novel in vitro human neural rosette assay that recapitulates the temporal sequence of dorsal spinal cord differentiation observed in vertebrate embryos. The more rapid progression of this differentiation programme in vitro than in the human embryo suggests that it represents the intrinsic differentiation pace of this cell population, while lacking signalling dynamics that influence the normal duration of steps in spinal cord differentiation. The initial retention of in vitro differentiation pace in human rosettes transplanted into the faster differentiating chicken embryonic spinal cord, either iso-chronically or hetero-chronically, further supports the notion that this reflects species-specific cell intrinsic timing. Importantly, long-term analysis of transplanted rosettes revealed that the iso-chronic environment was insufficient to sustain the human neural differentiation programme, while the more developmentally advanced hetero-chronic environment accelerated neurogenesis. These findings show that after a delay, which likely reflects previously reported slower protein turnover and cell cycle of human neural progenitors, differentiation of human cells can be influenced by the chicken embryonic environment. Moreover, hetero-chronic grafting into a more differentiated neural tube while increasing neuron production in comparison with neural rosettes in vitro, did not alter the programme of human neural differentiation, which progressed to a neurogenic phase, while the host embryo advanced into gliogenesis . These eIn vitro differentiation of human pluripotent cell derived NMPs in minimal medium containing low level RA  was suffSeveral lines of evidence indicate that the in vitro human dorsal spinal cord rosettes exhibit an intrinsic differentiation pace. First, the timing of neural progenitor differentiation was found to be more rapid in vitro than in the human embryo. This was indicated by the earlier appearance of ISLET1+/HB9- expressing neurons and of NFIA expressing gliogenic progenitors in neural rosettes than in the human embryonic dorsal spinal cord; indeed, the period from migratory neural crest emergence to gliogenesis onset was ~1.5 weeks earlier in vitro than in the human embryo . AlthougOLIG2 and NKX6.1 . Moreover, by 5 days iso-chronic conditions proved insufficient to sustain human neural differentiation, while more developmentally advanced hetero-chronic conditions now promoted human neural differentiation. As a slow response takes place regardless of the extrinsic cues provided these findings suggest that the ability of transplanted human cells to respond to the chicken environment is constrained by species-specific cell intrinsic properties. Such constraint is consistent with the finding that human ventral neural progenitors have a longer cell cycle and differentiate more slowly than equivalent mouse cells cultured in similar conditions in vitro . This had NKX6.1 . This maRaldh2 is expressed within the chicken neural tube including by the roof plate as well as the meninges enclosing the spinal cord at the time of hetero-chronic grafting E3 (HH19/20)  .It is notable that the increased neuron production in heterochronic conditions follows the sequence of the human neural differentiation programme even when the host chicken embryo has progressed further and has been gliogenic for days 3–5 (from E5 to E8) . This inThis interpretation places emphasis on the signalling environment and predicts that iso-chronically grafted rosettes, which at day 5 exhibit reduced neurogenesis and arrested neural crest differentiation in comparison with in vitro conditions, will eventually differentiate. However, appropriate cues within the chicken embryo, in particular, for the neural crest, may no longer be present. Previous studies have used xenografting assays to evaluate the behaviour of human cells generated in vitro  or for pCit/Y) H1), and the human iPSC line ChiPS4 was provided by Cellartis AB (now Takara Bio). Suppliers (WiCell and Cellartis AB) provided documentation confirming cell identity. STR DNA profiling for ChiPS4, ChiPS4-pmGFP, H9 (WA09) and H1-DCX-CITRINE confirmed identity of these lines in our laboratory.Human ES cell lines were obtained from WiCell, H9 (WA09) and from the Allen Institute/ WiCell (Dr Boaz Levi) , H1-DOUBAll cell lines were expanded and banked on arrival. Prior to experiments representative lots of each cell bank were thawed and tested for post-thaw viability and to ensure sterility and absence of mycoplasma contamination. Sterility testing was performed by direct inoculation of conditioned medium into tryptic soya broth and soya bean casein broth and no contamination was observed. Mycoplasma testing was carried out by DAPI staining of fixed cultures and using the mycoalert mycoplasma detection kit (Lonza). We confirm that the cell lines used in this study were free of mycoplasma and other microorganisms.H9 (WA09) hES cells were supplied at passage 24, the cells were thawed, and cell banks prepared at passage 29: for experiments the cells were used between passage 29 and 39. H1-DOUBLECORTIN-CITRINE cells were supplied at passage 63 and cell banks prepared at passage 66: for experiments the cells were used between passage 66 and 76. ChiPS4 hiPS cells were supplied at passage nine and cell banks prepared at passage 13: for experiments the cells were used between passage 13 and 23. To make ChiPS4-pmGFP, the cells were transfected with the PiggyBac and transposase constructs at passage 17 and GFP+ ve cells selected by fluorescence activated cell sorting at passage 19. The polyclonal cell line was banked at passage 23; for experiments the cells were used between passage 23 and 33. After two passages all cell lines were tested to check for the uniform expression of pluripotency markers  and absence of differentiation markers  by immunofluorescence.Cit/Y H1 line) and hiPSCs (ChIPS4) were maintained as feeder-free cultures in DEF-based medium supplemented with bFGF  and Noggin  on Geltrex matrix coated plates , and enzymatically passaged using TryPLselect . For PSC passaging, the medium was complemented by addition of the Rho kinase inhibitor Y-27632 .hESC (H9 or DCX2 and shifted to N2B27 medium after 24 hr. Cells were then differentiated for 3 days (D) in N2B27 supplemented with 3 µM Chiron99021  and 20 ng/ml bFGF , in the presence of 50 ng/ml Noggin  and 10 ng/ml SB431542  from D2 to D4 to generate NMP-like cells, see 5 cells/cm2) on Geltrex  in the presence of Y-27632 . Cells were then cultured in N2B27 containing 100 nM all-trans-RA  for the indicated time to obtain and differentiate neural progenitors. First human neural rosettes were observed from D06 with this regime. All work with hESCs was undertaken in approval of the UK Stem Cell Bank steering committee (license number SCSC14-29).For differentiation assays, PSC were plated on Geltrex matrix at 40,000 cells/cmhttps://www.hdbr.org) following fixation in 4% paraformaldehyde (PFA) for 2 hr at 4°C and chicken embryonic neural tube at indicated stages was preserved in the same way, prior to processing for cryo-sectioning using standard procedures. hESCs or iPSCs derived neural rosettes were fixed in 1% PFA overnight at 4°C, permeabilised with 0.1% Triton X-100 in PBS. Tissue sections or rosettes grown in ibdi dishes  in vitro were subject to a standard immunofluorescence protocol using commercial primary and secondary antibodies at concentrations indicated in Key resources table. Finally, sections were stained with DAPI to visualize cell nuclei. Fluorescent images were captured using a Leica SP8 confocal using a 20X air or 63X NA1.2 APO water immersion objectives. Images of protein localisation in human embryonic spinal cord have been deposited in the HDBR atlas https://hdbratlas.org/organ-systems/nervous-system/cns/spinal-cord/storeylab.html.Human embryonic tissue was provided by the Human Developmental Biology Resource , containing the cytomegalovirus (CMV) enhancer, chicken b-actin (CAG) promoter expression and including the Invitrogen Gateway RfA cassette, allowing for phiC31-mediated recombination from Gateway Entry vectors. Finally, the CAG promoter and Gateway cassette (cargo) are flanked by two Inverted Terminal Repeat sequences  allowing the piggyback transposon action. To label the cell membrane, a plasma membrane (pm) associated palmitoylated fluorescent protein was generated by the addition of the 20-amino acid sequence of ratGAP-43 MLCCMRRTKQVEKNDEDQKI to the N-terminus of the monomeric enhanced GFP (eGFP)  through sequential PCR amplification to make a pm-eGFP sequence. This pm-eGFP sequence was then inserted into the aPX1 donor vector using the Gateway System (ThermoFisher).7 cells and a single pulse of 1100 V with 30 ms duration was delivered via the Neon transfection system. Transfected cells were then plated back on Geltrex coated dishes  and cultured in DEF-based medium supplemented with bFGF  and Noggin . In this way, a polyclonal line ChiPS4-pm-GFP was created, and this was used in the experiments reported here.HiPSCs (Cellartis hIPS4 cell line) cells were transfected using the Neon transfection System . Briefly, a mix of aPX1-pm-eGFP construct and piggyback transposase , with a Gallus gallus domesticus) from a commercial source  were incubated at 38°C to Hamburger and Hamilton (HH) stage 11–12 (13–16 somites)  were gently dissociated and implanted into the dorsal region of one side of the neural tube of stage HH 11–12 chicken embryos. For transplantation of small groups of human dorsal neural progenitors, micro-dissected single human rosettes were further sub-dissected and mechanically dissociated prior to transplantation. Before implantation, the dorsal region on one side of the neural tube was removed at the level of the most recently formed somite . Embryos were then harvested 2 or 5 days post-transplantation (Stage HH 23–24 and HH 30–31 respectively). Chicken embryos were fixed in 4% PFA overnight at 4 °C, rinsed in PBS and left in PBS/30% sucrose solution overnight at 4 °C, then embedded and in agar, frozen and cryo-sectioned at 20 μm and subjected to immunofluorescence processing (as above).Fertilized chicken eggs (somites) . ChIPS4-To create an asynchrony between the donor  and the host (chicken embryos), newly formed human neural rosettes were micro-dissected after 6 days of neural differentiation from NMP-L cells, gently dissociated and implanted into the dorsal region of one side of the neural tube of stage HH 20–21 chicken embryos. Before transfer, the dorsal region on one side of the neural tube was removed at the mid-trunk, thoracic region (just posterior to the forelimb region). Similar to the isochronic transplantation assay, embryos were then harvested 2 or 5 days post-transplantation . Chicken embryos were then fixed, processed and subjected to immunofluorescence as described above.G. gallus domesticus) from a commercial source  (wild type) or from a transgenic chicken line expressing a myristoylation sequence fused to GFP (myr-GFP) which localises to the plasma membrane  of neural progenitors forming a neural plate-like shape were selected for quantification. At later time points, human neural rosettes were selected for analysis and quantification by their shape (round with a single lumen) and size . For transplanted human rosettes at least three sections from grafts in three different embryos were used to determine the proportion of cells expressing a protein of interest. Transplanted human cells were only counted when cells were positive for GFP, immunoreactivity for protein of interest and DAPI. To avoid counting the same cells in consecutive sections, alternate sections were used for each different immunolabelling assay (four different immunofluorescence assays/transplanted embryo) . Statistical analysis was performed using non-parametric Mann-Whitney U test for non normally distributed data using Prism V8. Results are represented as means ± SEM or SD as indicated in figures and p-values are indicated with *p<0.05, **p<0.01, *** p<0.001, and ****p<0.0001.At D4 of in vitro differentiation from NMP-like cells, defined fields . public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by Marianne Bronner as the Senior Editor and Reviewing Editor. The following individual involved in review of your submission has agreed to reveal their identity: Tatiana Solovieva (Reviewer #3).Thank you for submitting your article "Rapid pace of human spinal cord differentiation in vitro is only initially maintained in a heterologous environment" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.The pace that determines differentiation of embryonic cells (and ESCs/iPS) in general, and human embryonic cells in particular, is at the forefront of stem cells and developmental biology fields, with promising translational perspectives towards regenerative medicine. In this manuscript Dady et al. used human neural rosettes derived from hESCS/iPS to test their differentiation pace in the dish. Their data elegantly show their sequential differentiation into different types of neurons and glia at distinct time windows. When compared to their corresponding cells/domain within human embryonic spinal cords, the authors discover that, although the in vitro rosettes recapitulate the temporal sequence of neural tube differentiation , they proceed much more rapidly in vitro compared to the intact embryo. This analysis led the authors to conclude that spinal cord progenitors in the in vitro conditions accelerate their developmental pace as compared to the embryonic counterparts due to lack of the endogenous environmental context.Intriguingly, when the rosettes were grafted into chick neural tube in ovo, they did not adjust to the new environment, which contains much more rapidly differentiating cells, and remained in their initial pace. Later, the grafted cells halted their development and remained progenitors. These findings led the authors to conclude that intrinsic properties of the neural rosettes, rather than extrinsic signals from the avian spinal cord, govern their differentiation pace.Essential revisions:All of the points raised by the reviewers seem both reasonable and feasible. Most importantly:1. Heterochronic transplantation is a key experiment to test the notion of intrinsic regulation of the timing of human neural stem cell differentiation.2. Please provide information on whether the cells analyzed for a given marker are present within or outside of that marker domain in the chick host. This is crucial to interpretation of the data.3. Better analysis of the stalled grafts in terms of their cell death/proliferation /glia markers.4. As a control, chick-to-chick graft of early progenitors to compare with the human graft to rule out the possibility of an experimental artifact that leads to the human cell behaviour in the chick.5. A few grafts using smaller groups of rosette derived cells to clarify whether or not the current results represent human cells that are receiving largely chick host derived signals.Reviewer #1 (Recommendations for the authors):In this interesting paper by Dady and colleagues the nature of human neural progenitor differentiation is evaluated via transplantation studies. The first part of the paper establishes the timing of neural differentiation in human IPSC model systems and in human embryonic spinal cord, showing that the relative timing of neurogenesis and gliogenesis is maintained. In the second part of the paper these human IPSC neural rosettes are transplanted into the chick spinal cord during neurogenic stages (i.e. Isochronic transplantation) and they find that neurons are generated by these transplanted populations. Analysis of transplants at later stages reveals that neurogenesis has "stalled" and is relatively reduced within the transplanted population.Overall, this is an interesting paper that uses classic approaches to answer potentially interesting questions, however there are some issues that limit its potential impact. The first two figures are recursive and show that the authors can implement an existing protocol. The transplantation studies are intriguing by fall short of offering any new insights. The key finding seems to be that at later stages post-transplantation neuronal differential is "stalled". There are many other reasons  that could explain their results. Suppose that stalling was indeed occurring, the authors offer no cellular or molecular insights into what regulates these intrinsic differences across species. At the end, I'm left asking what we have really learned about the mechanisms that control the timing (pace is just fancy and new way of saying timing) of differentiation in human neural stem cells. Below are a few experimental suggestions that could add more insight into this paper:1) Fig7, late stage transplant, should check NFIA expression. E7 is gliogenic in the chick and its possible that the rosettes are now becoming gliogenic. Perhaps the observed "stalling" is really a switch to gliogenesis. Alternatively, the apparent reduction in the proportion of neurons could be a survival issue, where older and more differentiated neurons from human rosettes cannot survive in the chick spinal cord. Thus, cell death should be evaluated. Even if there are no detectable changes in cell death, the interpretation should be dialed back a bit as its totally possible that there issues with the maintenance of human neurons in chick spinal cord and by the time they are analyzing transplants at E7 in chick the rosettes have switched to gliogenesis and are no longer making neurons (and the neurons that were produced have died off).2) The phrasing is a bit odd. Homo-chronic should be phrased as "Isochronic". "Pace" is really just timing of differentiation.3) To truly test whether there is an intrinsic mechanism at play in these transplantation studies, they should perform the heterochronic experiment: Transplant Neurogenic D6 rosettes into the E6 gliogenic chick spinal cord. Admittedly, this is a tough experiment. Therefore, as an alternative, they should perform the complementary heterochronic transplantation: Gliogenic D20 rosettes into the E2, neurogenic chick spinal cord. If there is some sort of intrinsic timing mechanism at play, then the D20 rosettes should still make glia in the neurogenic environment. This was actually already shown for mouse glial progenitors  and it would be a nice addition to this paper.4) The labeling in figure 4b makes no sense and seems arbitrarily defined. First, a better description of these regions and how they are defined needs to be included. Second, a representative cell or image from these regions should be shown at high magnification. As it stands, I'm having a very hard time deciphering what is different about these groups. Third, each image that contains this type of analysis (for example 4d) should show the respective regions.5) Figure 3 is really useful and interesting. Would be good to show the human staining alongside the chick (or mouse) staining. The diagram in 3I is nice, but side-by-side images across species would really make it more compelling.Reviewer #3 (Recommendations for the authors):1. It would be helpful to see individual channels for the markers visualised in the chick cross sections in Figure 4D and E. It appears as though the intensity of SOX2 and PAX7 in GFP+ cells is reduced relative to expression in the neighbouring chick neural tube cells, but it is difficult to tell without seeing the SOX2 and PAX7 channels separately. When counting cells positive or negative for a marker gene, is intensity of that marker within a cell factored in? Essential revisions:All of the points raised by the reviewers seem both reasonable and feasible. Most importantly:1. Heterochronic transplantation is a key experiment to test the notion of intrinsic regulation of the timing of human neural stem cell differentiation.Heterochronic transplantation of D6 human neural rosettes into the same position in the developing spinal cord but into older embryos has been undertaken. Three independent experiments were carried out, with three sections from 3 grafts per experiment analysed for each of our panel of markers at 2 days and at 5 days post-transplantation. These extensive new data are presented in a new Results section and in new figures 8 and 9, and results summary figure 10. We find that as with isochronic transplantation there is a >2-day delay before human cells respond to the chick environment, in both contexts the rosette cells retain differentiation timing found in rosettes cultured in vitro. This supports the conclusion that human cells initially follow cell instinct differentiation timing. Strikingly, by 5 days heterochronic grafts exhibit increased neurogenesis in comparison with in vitro rosettes, (while isochronic grafts showed reduced neurogenesis). This demonstrates that human cells can respond to differentiation signals in the chicken embryonic environment. Moreover, by heterochronic day 5 the chicken host has already embarked on gliogenesis, but the human cells continue to follow their normal neural differentiation programme, entering the neurogenic phase. As human cells do not obviate neurogenesis in a gliogenic environment these data further show that human cells follow an intrinsic differentiation programme.2. Please provide information on whether the cells analyzed for a given marker are present within or outside of that marker domain in the chick host. This is crucial to interpretation of the data.This is only an issue for one marker we use (ISLET1) which has a discrete localisation in the dorsoventral axis. The number of sections with GFP +ve human cells/grafts in this precise location in the host embryo is now included in the Results/ figure legends. We have provided further information about this in our detailed Response to reviewer (3) comments. While not all sections through grafted rosettes include such cells, we have examples from 3 (isochronic) or 4  grafts/embryos.3. Better analysis of the stalled grafts in terms of their cell death/proliferation /glia markers.This has been addressed by assessing expression of key marker proteins in sections of isochronic day 5 grafts. Analysis of cell death using an antibody to detect activated caspase 3 revealed one or two positive cells in only a few sections. Cell proliferation detected with an antibody that recognises Ki67 showed that almost all human rosette cells expressed this protein. These data are quantified and provided in revised figure 6 and as metadata. We conclude that cell death does not contribute to the differentiation state observed at Day 5, although we cannot formally exclude the possibility that this takes place discretely between day 2 and day 5 and so was missed in this analysis (noted in the manuscript). Our conclusion is supported by the proliferative status (Ki67) of almost all cells, which further confirms our original analysis showing that most cells express SOX2 and PAX7. We did not have enough sections to also assess the possibility that these cells have been induced into glial progenitors, but the extensive expression of the neurogenic neural progenitor marker PAX7 in these grafts supports the conclusion that this could not account for the apparent stalling of the neurogenic differentiation. Overall, these data strongly suggest that iso-chronically grafted human neural progenitors have been exposed to insufficient levels of differentiation signals to progress the neural differentiation programme. This is consistent with the slower cell cycle of human neural progenitors and slower metabolism of human cells delaying response to the signalling environment in comparison with the cells of the host chicken embryo. This conclusion is further supported by the increased neurogenesis detected in day 5 hetero-chronic grafts, where exposure to a more developmentally advanced environment now drives neurogenesis.4. As a control, chick-to-chick graft of early progenitors to compare with the human graft to rule out the possibility of an experimental artifact that leads to the human cell behaviour in the chick.This further control experiment has been undertaken using grafts of the dorsal neural tube from the myr-GFP transgenic chicken line and is presented in figure 4 supplemental figure S3. We observe normal patterns of contribution and expression of key protein markers in transplanted embryos. This confirms that transplantation operation/injury does not in itself lead to altered differentiation.5. A few grafts using smaller groups of rosette derived cells to clarify whether or not the current results represent human cells that are receiving largely chick host derived signals.These experiments have been carried out and are presented and quantified in Figure 4 Supplementary figure and as metadata. These revealed essentially the same results as previous experiments. They control for the possibility that the single cells and small cell groups in our initial analysis may have become separated at a later time and so did not represent cells that had been fully integrated into the chicken neural tube throughout the time leading up to their analysis.Reviewer #1 (Recommendations for the authors):In this interesting paper by Dady and colleagues the nature of human neural progenitor differentiation is evaluated via transplantation studies. The first part of the paper establishes the timing of neural differentiation in human IPSC model systems and in human embryonic spinal cord, showing that the relative timing of neurogenesis and gliogenesis is maintained. In the second part of the paper these human IPSC neural rosettes are transplanted into the chick spinal cord during neurogenic stages (i.e. Isochronic transplantation) and they find that neurons are generated by these transplanted populations. Analysis of transplants at later stages reveals that neurogenesis has "stalled" and is relatively reduced within the transplanted population.Overall, this is an interesting paper that uses classic approaches to answer potentially interesting questions, however there are some issues that limit its potential impact. The first two figures are recursive and show that the authors can implement an existing protocol.Our previous work established this differentiation protocol , but this is its first use to analyse by immunofluorescence the timing of neural differentiation and the appearance of specific neuronal and glial cell types in an in vitro neural rosette assay. None of the data presented is recursive and it provides a quantitative timeline of human dorsal spinal cord differentiation. Moreover, our comparison with the human embryonic spinal cord indicates that neural differentiation progresses more rapidly in vitro.The transplantation studies are intriguing by fall short of offering any new insights. The key finding seems to be that at later stages post-transplantation neuronal differential is "stalled". There are many other reasons  that could explain their results. Suppose that stalling was indeed occurring, the authors offer no cellular or molecular insights into what regulates these intrinsic differences across species. At the end, I'm left asking what we have really learned about the mechanisms that control the timing (pace is just fancy and new way of saying timing) of differentiation in human neural stem cells. Below are a few experimental suggestions that could add more insight into this paper:1) Fig7, late stage transplant, should check NFIA expression. E7 is gliogenic in the chick and its possible that the rosettes are now becoming gliogenic. Perhaps the observed "stalling" is really a switch to gliogenesis.We appreciate that the E7 chick spinal cord is gliogenic  and it is formally possible that the human rosettes start to become gliogenic in this context at an earlier time point than in vitro. We observe in vitro that the human rosettes become gliogenic quite late, at D20, while analysis of grafted D6 rosettes after 5 days takes us only to D11 in vitro when rosettes are clearly neurogenic , so the chick environment would need to be able to dramatically accelerate or divert the human neural differentiation programme. Unfortunately, this is the only experiment we have been unable to address directly as we did not have enough sections of day 5 iso-chronically grafted rosettes to assess NFIA expression. However, below we set out the evidence for why we think these late-stage iso-chronically transplanted rosettes are not gliogenic.We have effectively tested this question in a new hetero-chronic grafting experiment (undertaken in response to your further comments below), which assesses differentiation of D6 human rosettes grafted into an E3 older chick spinal cord; after 2 days we did not detect expression of glial marker (NFIA) in transplanted human rosettes, despite NFIA expression in the host chicken embryo . Further, after 5 days, we found a large increase in the number of neurons (compared to in vitro rosettes), excluding an early switch to gliogenesis at this time . These findings indicate that the transplanted human rosette cells can respond to the chicken host environment after this longer period of exposure to a more differentiated environment, indeed this is consistent with continued neurogenesis during the transition to gliogenesis that is observed in the developing chick spinal cord, with neurons still being generated at E6 . As expanded upon below, these findings support the interpretation that D6 rosettes proceed along the normal sequence of the human neural differentiation programme, generating neurons in a supportive environment, rather than being reprogrammed to the gliogenic pathway. D6 rosettes grafted iso-chronically into the earlier E2 embryo will be exposed to a less advanced neurogenic environment and we think this is not enough to sustain productive human neurogenesis, but some P27 and ISLET1 positive cells are generated from rosette cells that continue to express neurogenic progenitor protein PAX7 at day 5   and so it is unlikely that the rosette cells are gliogenic. We have now summarised the differentiation status of rosette cells in these different contexts in a new schematic figure 10.Alternatively, the apparent reduction in the proportion of neurons could be a survival issue, where older and more differentiated neurons from human rosettes cannot survive in the chick spinal cord. Thus, cell death should be evaluated. Even if there are no detectable changes in cell death, the interpretation should be dialed back a bit as its totally possible that there issues with the maintenance of human neurons in chick spinal cord and by the time they are analyzing transplants at E7 in chick the rosettes have switched to gliogenesis and are no longer making neurons (and the neurons that were produced have died off).We have evaluated cell death in day 5 iso-chronically grafted rosettes and found only a few cells expressing cleaved casapase3 . We also analysed cell proliferation and observed that most transplanted human rosette cells express Ki67 indicative of cycling cells . These data, along with the expression of SOX2 and PAX7 in almost all cells in iso-chronically transplanted human rosettes after 5 days  indicate that these retain a proliferative neural progenitor cell state, while P27 and Islet1 cell counts indicate they produce few neurons. Moreover, as noted above the same D6 rosettes grafted into a later E3 embryo generate increased numbers of neurons (greater than in vitro) by day 5, indicating that such human cells survive well in the chicken environment. However, it is formally possible that neuronal death takes place discretely between day 2 and day 5 in iso-chronically grafted rosettes and so was not detected in this analysis. We now note this in the Discussion.2) The phrasing is a bit odd. Homo-chronic should be phrased as "Isochronic". "Pace" is really just timing of differentiation.We used the word “homo-chronic” for a reason. The term iso-chronic is often used in classical experiments in which the same tissue is transplanted between two embryos at exactly the same stage of development for example to make fate maps . As we did not transplant a piece of human neural tube for evident ethical reasons, we found the term “homo-chronic” more relevant as we are comparing two similar tissues at the same stage of differentiation from two different species. However, for clarity we have now replaced the term homo-chronic with by iso-chronic. We also appreciate that timing and pace have similar meanings here.3) To truly test whether there is an intrinsic mechanism at play in these transplantation studies, they should perform the heterochronic experiment: Transplant Neurogenic D6 rosettes into the E6 gliogenic chick spinal cord. Admittedly, this is a tough experiment. Therefore, as an alternative, they should perform the complementary heterochronic transplantation: Gliogenic D20 rosettes into the E2, neurogenic chick spinal cord. If there is some sort of intrinsic timing mechanism at play, then the D20 rosettes should still make glia in the neurogenic environment. This was actually already shown for mouse glial progenitors  and it would be a nice addition to this paper.We have now carried out a heterochronic experiment. We opted to graft D6 human rosettes into an older host chick embryo at E3, as this allowed us to compare data with D6 rosettes cultured in vitro or iso-chronically transplanted, after 2 days and 5 days. Grafting into even older E6 embryos, as suggested here, was not possible because these are highly vascularised with a strong heartbeat that makes the transplantation and positioning of rosettes technically challenging and reduces embryo viability. The alternative suggestion, transplanting gliogenic D20 rosettes was not undertaken because by this stage the rosette structure has been outgrown and we would also have to characterise rosettes in vitro for a further 2 (D22) and 5 (D27) days to provide in vitro control data sets .So, as presented in detail in the paper, we transplanted D6 rosettes composed of dorsal neural progenitors into the dorsal region of the neural tube of an older E3 chicken embryo. After 2 days the E3 host embryo had reached E5 and had begun the gliogenic switch, expressing NFIA in the dorsal neural tube . Importantly, we did not detect NFIA in the transplanted rosette after 2 days. In fact, we found no difference between hetero-chronically or iso-chronically transplanted and in vitro cultured rosettes at day 2, indicating that in this timeframe transplanted human cells were not influenced by the chicken host environment. We argue in the paper that this finding is consistent with the correlation between greater protein stability / slower human cell cycle time and slower differentiation pace  and we make a case that transit through the cell cycle may be required for expression of differentiation genes and that this may explain the delay seen in both conditions in the response of human cells to the chicken environment.Importantly, 5 days after heterochronic grafting (when the human cells had been exposed to the gliogenic environment for 3 days), we found increased neuron production (both p27 and islet1 +ve cells) compared with in vitro rosettes and iso-chronic transplantation conditions . This tells us that by day 5 human cells can now respond to the chicken environment, and while in isochronic this is insufficient to drive neurogenesis, in the hetero-chronic conditions this results in the next step the neural differentiation programme, i.e. neuron production.These heterochronic grafting data therefore support the conclusion that the human neural differentiation programme progresses in an environment with an appropriate level of differentiation signals (presented over 5 days), and that the sequence of cell types generated by this programme is not altered in this older and newly gliogenic environment. This is consistent with the findings of Mukouyama, et al. in the mouse, in that they find that Olig2+ progenitors progress through a differentiation programme, first neurogenic phase and then gliogenic phase when transplanted into the chicken environment, and that this is not reversible, older gliogenic Olig2+ progenitors cannot be reprogrammed back to a neurogenic phase in younger chicken host. Here we show that the neurogenic phase of differentiating human neural progenitors is not obviated by development in an increasingly gliogenic environment. We now address this in the Discussion, noting that this supports operation of an intrinsic programme of human neural differentiation.4) The labeling in figure 4b makes no sense and seems arbitrarily defined. First, a better description of these regions and how they are defined needs to be included. Second, a representative cell or image from these regions should be shown at high magnification. As it stands, I'm having a very hard time deciphering what is different about these groups. Third, each image that contains this type of analysis (for example 4d) should show the respective regions.We thank the reviewer for noting this and have clarified this in the text, opting to replace the numbers with abbreviations to remind readers which cell configuration is indicated . Please note that examples of all three cell configuration types scored, , directly incorporated into the chick neuroepithelium , or single cells incorporated within the chicken neuroepithelium ), do not appear in all sections – so for example, while all 3 are apparent in Figures 4C and 4E, only two contiguous rosette and incorporated cell groups ( i.e. no single cells here) appear in Figure 4D.5) Figure 3 is really useful and interesting. Would be good to show the human staining alongside the chick (or mouse) staining. The diagram in 3I is nice, but side-by-side images across species would really make it more compelling.While we agree this would be nice, we do not have precisely the same immunofluorescence images with these different species to include in this figure. The point of the figure is to compare differentiation of human neural rosettes in vitro with that in the human embryonic spinal cord.Reviewer #3 (Recommendations for the authors):1. It would be helpful to see individual channels for the markers visualised in the chick cross sections in Figure 4D and E. It appears as though the intensity of SOX2 and PAX7 in GFP+ cells is reduced relative to expression in the neighbouring chick neural tube cells, but it is difficult to tell without seeing the SOX2 and PAX7 channels separately. When counting cells positive or negative for a marker gene, is intensity of that marker within a cell factored in?Separate channels for marker proteins and GFP are now provided in revised figure 4. We have noticed that PAX7 expression appears lower in human compared to chick neural progenitors, however, we do not know whether this reflects different levels of protein in different species or different detection sensitivities with this antibody, so we have not drawn any conclusions from this observation. The way we scored the positive cells for each marker is described in the material and methods section. We do not factor in the intensity of expression of the different markers. We scored cells as positive when human cells were clearly positive for GFP, a marker and DAPI.


Three-dimensional convolutional neural networks (3D CNN) of artificial intelligence (AI) are potent in image processing and recognition using deep learning to perform generative and descriptive tasks. Compared to its predecessor, the advantage of CNN is that it automatically detects the important features without any human supervision. 3D CNN is used to extract features in three dimensions where input is a 3D volume or a sequence of 2D pictures, e.g., slices in a cone-beam computer tomography scan (CBCT). The main aim was to bridge interdisciplinary cooperation between forensic medical experts and deep learning engineers, emphasizing activating clinical forensic experts in the field with possibly basic knowledge of advanced artificial intelligence techniques with interest in its implementation in their efforts to advance forensic research further. This paper introduces a novel workflow of 3D CNN analysis of full-head CBCT scans. Authors explore the current and design customized 3D CNN application methods for particular forensic research in five perspectives: (1) sex determination, (2) biological age estimation, (3) 3D cephalometric landmark annotation, (4) growth vectors prediction, (5) facial soft-tissue estimation from the skull and vice versa. In conclusion, 3D CNN application can be a watershed moment in forensic medicine, leading to unprecedented improvement of forensic analysis workflows based on 3D neural networks. Conventional forensic analysis is based on forensic expert’s manual extraction of information. Forensic expert provides opinions established on medical and other fields of current knowledge combined with personal work experience. This is not only time-consuming, albeit frequently affected by subjective factors that are tough to overcome .The main purpose of this paper is to analyze and introduce a very promising line of research applicable to forensic anthropology and various traditional sectors of forensic medicine. The application of artificial intelligence (AI) is a new trend in forensic medicine and a possible watershed moment for the whole forensic field ,3,4,5,6.Biological age determination ,29,30,31Sex determination ,38,39,40Automatized 3D cephalometric landmark annotation ,56,57,58Soft-tissue face prediction from skull and in reverse ,75,76,77Facial growth vectors prediction ,88,89,90This chapter paper explains basic terminology, principles and the current horizon of knowledge. The methodology chapter presents the novel clinical workflow based on implementing three-dimensional convolutional neural network (3D CNN) algorithms ,8,9. TheThe result of this paper is a detailed guide for forensic scientists to implement features of 3D CNN to forensic research and analyses of their own (in five themes described above). This resulting practical concept—possible workflow shall be useful for any forensic expert interested in implementing this advanced artificial intelligence feature. This study is based on the worldwide review of 3D CNN use-cases that apply to clinical aspects of forensic medicineThis article’s secondary objective is to inspire forensic experts and approximate them to implement three-dimensional convolutional neural networks (3D CNN) in their forensic research in the fields of age, sex, face and growth determination.Artificial intelligence has brought new vigor to forensic medicine, but at the same time also some challenges. AI and forensic medicine are developing collaboratively and advanced AI implementation until now required extensive interdisciplinary cooperation. In the era of big data , forensiFor many forensic experts, the current benefits of advanced AI processes are still unknown. For example, automated AI algorithms for skull damage detection from CT  or soft-A Convolutional Neural Network (CNN) is a Deep Learning algorithm that can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image, and differentiate one from the other.CNN is an efficient recognition algorithm that is widely used in pattern recognition and image processing. It has many features such as simple structure, less training parameters and adaptability. CNN is a supervised type of Deep learning, most preferable used in image recognition and computer vision a,b.Compared to its predecessors, the main advantage of CNN is that it automatically detects the crucial features without any human supervision. For example, given many pictures of cats and dogs, it learns distinctive features for each class. CNN is also computationally efficient.3D CNN is used to extract features in 3 Dimensions or establish a relationship between 3 dimensions. A 3D CNN is simply the 3D equivalent: it takes as input a 3D volume or a sequence of 2D frames .In terms of Neural Networks and Deep Learning: Convolutions are filters (matrix/vectors) with learnable parameters used to extract low-dimensional features from input data. They have the property to preserve the spatial or positional relationships between input data points.2D CNNs predict segmentation maps for DICOM slices in a single anatomical plane. 3D CNNs address this issue by using 3D convolutional kernels to make segmentation predictions for a volumetric patch of a scan .In 3D convolution, a 3D filter can move in all 3-directions . At each position, the element-wise multiplication and addition provide one number. Since the filter slides through a 3D space, the output numbers are also arranged in a 3D space. The output is then 3D data.The recognition of similar structures from the CBCT is based on their similar opacity on the X-ray classified by the Hounsfield scale. The process of defining ranges for particular tissues is called “thresholding”, which is prior to final the segmentation . SettingThe segmentation of original CBCT data can result in the definition of various 3D structures involved in 3D CNN training, or these 3D structures can serve as anchors for mapping another 3D scan, such as an intraoral optical scan or extraoral scan that includes texture. All these three data sources can be merged, and the 3D CNN network can work with unprecedented data that include wider face regions from face scan or morphological information on teeth and gums .Traditional forensic analyses of age, gender and facial appearance are based on forensic expert manually acquiring information that provides the identification established on expert`s medical and biological knowledge and mathematical calculations ,94,95. IAI usage is not without risks of undesired side effects. AI may become biased in the same way as a human forensic expert, depending on the source data used for AI training . ObermeyMany studies in forensic science have been conducted in recent years, and some recent studies are beginning to focus on neural networks . These sSeveral research teams have tried neural network learning based on X-ray images ,104,108.More studies modelled gender determination using AI. Bewes et al.  used neuGender classification was also discussed by Liew et al. . The maxIt is almost unbelievable how accurately neural networks can determine age or gender compared to commonly used methods. Therefore, we emphasize their use in forensic practice ,50,117.Regarding the Skeletal age estimation for forensic purposes, we consider ourselves useful for the direction of the 3D CNN on particular areas of the head and neck. Various experts published research on age estimation by measuring open apices in teeth, stage of teeth eruption, frequently of third molars or canine tooth/pulp ratio ,27,29,31Regarding forensic medico-legal aspects, the perspective on natural development estimated by AI algorithms is always relevant, especially in the situation of trauma or other damage that conflicted with this estimated development. AI is now used to evaluate CT scans of lungs and to predict the deterioration of COVID-19 patients in the emergency department ,128,129.In this case, 3D CNN algorithms can automatically evaluate not only hard-tissue structures and search for inapparent damage that could have been responsible for a sudden death incident ,130.Analysis of complex cranial and facial structures is a domain of orthodontics. Historically they are fundamental for proper treatment planning, and they represent lines, angles, planes on the axilla-facial structures identifiable, especially on the X-ray . There is massive research regarding cephalometric parameters and their values. Observer defines the points, and their interobserver error are the main weakness of cephalometric analysis . AnthropAs the various cephalometric parameters  were well researched, and some are proven to be related to age, sex or growth, they are a frequent springboard for many research studies focused on facial parameters. Implementation of AI in cephalometric analysis has been published ,135,136.Reconstruction of the face from the skull is an age-old desire of forensic experts. Current methods of not implementing AI are very limited. Prediction of soft tissues according to the hard tissues of the skull and vice versa can be significantly improved upon big-data training of 3D CNN with supplementary metadata about age, sex, BMI or ethnicity. New algorithms to perform facial reconstruction from a given skull has forensic application in helping the identification of skeletal remains when additional information is unavailable ,92,137. The beauty industry has seen rapid growth in multiple countries, and due to its applications in entertainment, the analysis and assessment of facial attractiveness have received attention from scientists, physicians, and artists because of digital media, plastic surgery, and cosmetics. An analysis of techniques is used to assess facial beauty that considers facial ratios and facial qualities as elements to predict facial beauty ,139,140.This paper most complex AI application is the final 5th theme—“Facial growth vectors prediction”. The authors of this paper addressed it for various reasons. Firstly, it is fundamentally different from the first four themes. Secondly, it requires the most complex implementation of AI strategies. To our knowledge, this is only the second paper in the world that handles the problem of facial growth prediction with ML methods and absolutely the first paper to consider a 3D CNN for facial growth predictions.Prediction of natural growth is compared to typically forensic topics such as human remains reconstruction and identification or age and sex determination rather less familiar topic. Mainly because despite numerous research attempts to predict facial growth, a satisfactory method has not been established yet, and the problem still poses a challenge for medical experts ,143,144.In 1971 Hirschfeld and Moyers published an article named “Prediction of craniofacial growth: the state of the art” . This waThe only study that was focused on the prediction of the facial growth direction with Machine Learning methods and has been published so far is a paper with its pre-print ,145 fromCBCT scans analyzed for this paper were made on one machine: i-CAT™ FLX V17 with the Field of View (FOV) of 23 cm × 17 cm with technical parameters and settings Medical software used for DICOM data processing and analysis was Invivo™ 6 from Anatomage Inc., Silicon Valley, Thomas Road Suite 150, Santa Clara, CA 95054, USA.Software for the AI solution base we have used the Python programming language along with 3 deep learning libraries—TensorFlow 2, PyTorch and MONAI. As for the hardware, the whole AI system is powered by multiple GPUs.Task 1—Age estimation from whole 3D CT scan imageDefinition: the task is to estimate the approximate age of a person from a whole head 3D CBCT scanProposed method: build regression model represented by a 3D deep neural network that has the current state of the art network architecture as a backboneMetrics: Mean Absolute Error (MAE) and Mean Squared Error (MSE) Task 2—Sex classification from thresholded soft and hard tissuesDefinition: the task is to classify input 3D CBCT scans  into one of 2 predefined categories—female and maleProposed method: build classification model represented by 3D deep neural network based on convolutional layers and outputs class probabilities for both targetsMetrics: Accuracy and Confusion Matrix (CM) Task 3—Automatization of cephalometric measurementsDefinition: the task is to create an automated system able to tag cephalometric landmarks on whole head 3D CT scanProposed method: build object detection model based on 3D neural network that estimates cephalometric measurements automaticallyMetrics: Mean Absolute Error (MAE) and Mean Squared Error (MSE) Task 4—Soft-tissue face prediction from skull and vice versaDefinition: the task is to create an automated system that predicts the distance of the face surface from the bone surface according to the estimated age and sex. 3D CNN to be trained on whole-head CBCTs of soft-tissue and hard-tissue pairs. *CBCTs with trauma and other unnatural deformations shall be excluded.Proposed method: build a generative model based on Generative Adversarial Network that synthesizes both soft and hard tissuesMetrics: the slice-wise Frechet Inception Distance Task 5—Facial growth predictionDefinition: the task is to create an automated system that predicts future morphological change in defined time for the face’s hard- and soft tissues. This shall be based on two CBCT input scans of the same individual in two different time points. The second CBCTs must not be deformed with therapy affecting morphology or unnatural event. This already defines the extremely challenging condition. There is a high possibility of insufficient datasets and the necessity of multicentric cooperation for successful training of 3D CNN on this task.Proposed method: In this final complex task, the proposed method builds on previous tasks. We strongly recommend adding metadata layers on gender, biological age and especially genetics or letting the CNN determine them by itself. We suggest disregarding the established cephalometric points, lines, angles and plains as these were defined in regards to lateral X-ray, emphasising good contrast of the bone structures with high reproducibility of the point and not necessarily with focus on particular structures most affected by growth. We suggest letting3D CNN establish its observations and focus areas.We also suggest allowing 3D CNN analysis of genetic predisposition in a smart way: by analysis of possibly CBCT of the biological parents or preferably non-invasive face-scan providing at least facial shell data.The processing of data in deep learning is crucial for the sufficient result of any neural network. Currently, most of the implementations depend on the dominant model-centric approach to AI, which means that developers spend most of their time improving neural networks.Loading DICOM files—the proper way of loading the DICOM file ensures that we will not lose the exact qualityPixel values to Hounsfield Units alignment—the Hounsfield Unit (HU) measures radiodensity for each body tissue. The Hounsfield scale that determines the values for various tissues usually ranges from −1000 HU to +3000 HU, and therefore, this step ensures that the pixel values for each CT scan do not exceed these thresholds.Resampling to isomorphic resolution—the distance between consecutive slices in each CT scan defines the slice thickness. This would mean a nontrivial challenge for the neural network. The thickness depends on the CT device setup, and therefore it is necessary to create equally spaced slices.[Optional] Specific part segmentation—each tissue corresponds to a specific range in the Hounsfield units scale, and in some cases, we can segment out specific parts of the CT scan by thresholding the image.Normalization and zero centering—these two steps ensure that the input data that are feed into the neural network are normalized into  interval  and are zero centered .For medical images, various preprocessing steps are recommended. In most cases, the initial steps are following :Loading Preprocessing the image dataset before feeding the CNN or other classifiers is important for all imaging modalities. Several preprocessing steps are recommended for the medical images before they are fed as input to the deep neural network model, such as (1) artefact removal, (2) normalization, (3) slice timing correction (STC), (4) image registration and (5) bias field correction. While all the steps (1) to (5), help in acquiring reliable results, STC and image registration are very important in the case of 3D medical images . Artefact removal and normalization are the most performed preprocessing steps across the modalities .This study comprises approximately 500 iCAT CBCT scans of human heads. Each CBCT scan has the spatial resolution of 768 × 768 pixels and the default device pixel spacing is [0.3 × 0.3 × 0.3] millimeters.The subjects are split by sex, with the ratio of 6:4 for female/male ranging from 8 to 72 years. The majority (90%) of the subjects are between 18 and 36 years.These dataset parameters were used in suggested considerations for 3D CNN applications concepts stated in In recent research ,14 AI-baAge estimation from dense tissue layer—we use whole skull CT scan as an input into the 3D convolutional neural network, which would serve as a regression model that estimates the continuous values of age for each CT scan separately.[Experimental] Visualization of network activations that represent regions of interest—neural network’s intermediate layers often serve as a an excellent explaining tool in order to find visual explanation heat maps  that higAs for the specific neural network architecture, we derive the backbone part from the current state of the art research. We primarily consider the EfficientNet  and DensBoth architectures base includes convolutional layers that serve as feature extraction blocks to obtain specific indicators from input x represented as a loaded DICOM image. These extracted feature maps are then forwarded to a fully-connected layer that outputs the single age estimation value:The determination of sex from human remains is a challenging task in various fields such as archeology, physical anthropology and forensics because there is no proven method that exactly leads to correct classification.The use of AI in this field is highly desirable as manual determination is often very complex and time-consuming. The objectiveness of the deep learning approach can also eliminate human bias leading to reliable software products.The sex classification is carried out similarly to the previous age estimation approach, but this task´s objective is to classify the final outputs from the neural network into 2 classes—female and male. For this purpose, we use the softmax activation function as a last operation to obtain class probabilities for both targets. The computation is following:The experimental part would include the input x consisting of 2 separate inputs—one will be the segmented skull and the other will be the segmented soft tissue (skin) which is achieved by setting different thresholds for segmentation preprocessing step.The cephalometric analysis aims to set landmarks of CT(CBCT) scans which serve as an important factor in the alignment of a skull. These measurements can also be used as surgery planning parameters or pre-and post-surgery comparisons ,150.The idea behind this approach is to use 3D convolutional neural networks for fully automated cephalometric analysis. Networks aim to output probabilistic estimations for each cephalometric landmark and then create a projection of these estimations into a real skull CT scan .Landmarks estimation in whole CT scan image—in this approach, the probability estimation for all landmarks is assigned for each pixel in the CT scanLandmarks estimation for selected regions of interest—assuming that each landmark corresponds to a specific area we could add another preprocessing step—slice cut where each slice would be a template-based region fed into a neural network. We can determine the expected landmark detection for each slice independently, which should help in the final model performanceTwo approaches come into consideration:Recently, CNNs have been successfully applied in widespread medical image analysis and achieved significant benefits ,141,151.Use whole 3D CT scan as an input into the neural network and output one value for age estimation as floating value and one for sex classification as a binary value.Segment out the mandible and use it as input into the neural network. Output is the same as in the previous task. Use a whole 3D CT scan to input into the neural network and output multiple values representing specific skull features (as discussed at the meeting last week). Then use these values as an input into another machine learning model to estimate age and gender.We have considered various approaches:Suppose we take an example of mandible segmentation from DICOM. The first step is to have DICOM files loaded and then, added any missing metadata; particularly, the slice thickness, that is, the pixel size in the Z direction, which was obtained from the DICOM file. The unit of measurement in CBCT scans is the Hounsfield Unit (HU), which is a measure of radiodensity. Thus, HU shall be converted to pixel values. Subsequently, it shall be resampled to an isomorphic resolution to remove the scanner resolution. The slice thickness refers to the distance between consecutive slices (when viewing a 3D image as a collection of 2D slices) and varies between scans.The final preprocessing step is bone segmentation and pixel normalization. Mandible bone extraction is complex because the surrounding bone has to be removed. An image binary thresholding and morphological opening operation for each slice shall be applied.The morphological opening operation is an essential technique in image processing, achieved by erosion and the dilation of an image. This technique helps to remove small objects while retaining more significant parts from an image. To obtain the mandible bone part, the largest areas after morphological opening shall be kept. Finally, all the slices shall be stacked together to obtain the mandible voxels.All approaches are evaluated in a classical machine learning manner—the dataset is split into three parts train, validation and test split. The test split mainly serves as a benchmarking set in order to compare our results with other approaches.When dealing with regression models in the deep learning field, we usually take into consideration two main regression metrics—Mean Absolute Error (MAE) and Mean Squared Error (MSE). Both metrics calculate the error between predicted y and ground truth labels denoted as y.MAE is defined as the mean of the sum of absolute differences between y and ŷ for each pixel:In order to evaluate the classification task, which in our case is represented by Task 2—sex classification, we need to consider the current distribution of male and female samples in our dataset. As the distribution is approximately 6:4 , we can calculate the overall accuracy and corresponding confusion matrix (CM). In the later phase, we can also test other metrics such as precision, recall or F1 score.The calculation of accuracy is defined just as the number of correct predictions divided by the total number of predictions. More interesting for use would be the CM. It is a tabular visualization of a model prediction for each class separately .This chapter is presenting summary outcome from the detailed research in previous sections of this paper. Investigation of 3D CNN modalities, their features, advantages and disadvantages and also clinical requirements for implementation in the field of forensic medicine has led to these proposed designs (guide) of future forensic research based on 3D CNN analyses.The authors of this paper have no doubts that 3D CNN, as another evolutionary step in advanced AI, will be with practical implementation a watershed moment in forensic medicine fields dealing with morphological aspects.Biological age determinationSex determinationAutomatized, precise and reliable:○3D cephalometric analysis of soft and hard tissues○3D face prediction from the skull (soft-tissues) and vice versa○Search for hidden damage in post-mortem high-resolution CT images○Asymmetry and disproportionality evaluationPredictions of:○Hard-tissue and soft tissue growth○Aging in general○Ideal face proportions respecting golden ratio proportions3D reconstructions of:○Missing parts of the skull or face○3D dental fingerprints for identification with 2D dental recordsWith considered data input as CT or CBCT (DICOM), the implementation of 3D CNN algorithms opens unique opportunities in areas of:First clinical applications of 3D CNN have shown ,126,150 The limitation of this paper is that practical examination of the proposed directions for 3D CNN implementations will require some time. Currently, there are many different 3D CNN in development, and actually, this is where most of the research activity is carried out ,154,155.Another limitation of this study is the high level of dynamics of research and development in this field of advanced AI implementations. The velocity in training the 3D CNN is high, and it is possible that a better approach can be recognized in the process.Interesting limitation of 3D CNN usage is the known fact  the any The final limitation of implementing the suggested designs for 3D CNN implementation for forensic researchers is the physical and legal availability of big data necessary for 3D CNN training. This can be solved with multicentric cooperation.There already exist many CNN processing DICOM data and are available for use ,12,14. RIn our efforts, we have faced the issue of CBCT scan distortion caused by metal artefacts . Fortunately, a novel coarse-to-fine segmentation framework was recently published based on 3D CNN and recurrent SegUnet for mandible segmentation in CBCT scans. Moreover, the experiments indicate that the proposed algorithm can provide more accurate and robust segmentation results for different imaging techniques compared to the state-of-the-art models with respect to these three datasets .As there already exists a fully automated method for 3D individual tooth identification and segmentation from dental CBCT , these aThe most complex area covered by this paper is a 3D prediction of growth and in a wider perspective of ageing. It is known that this process is laden with various variables including hormonal (sex) ,158,159 The only published study focused on predicting the facial growth direction with the implementation of Machine Learning methods is from 2021 Kazmierczak et al. 2021 ,145. TheThe principal weakness of this study is not the method but probably the input. The authors used only 2D lateral X-rays of various qualities and sizes. In addition, the evaluation was performed only in one 2D projection. The researchers focused on the angle between the Sella—Nasion line and the Mandibular plane formed by connecting the point gonion to gnathion at the inferior border of the mandible. They engaged an orthodontic expert to identify approximately 20 characteristic anatomic landmarks on LC to assess a subject. These were assessed manually on the lateral cephalogram. Some of the landmarks define angles which, from the clinical perspective, have special significance. As far as facial growth direction is concerned, there are no standardized measurements available in the literature to evaluate. The focus of supervised ML with a concentration on established cephalometric parameters might be wrong. It is the fact that they were originally chosen as well distinguished points on lateral X-ray with a priority of high reproducibility. So as considered by Hirschfeld and Moyers more than 50 years ago, we might be looking in the wrong places. Prediction of the change of SN/MP also oversimplifies the problem. The questions from the past remain, and facial growth prediction remains a complex mystery. The application of 3D CNN on this most complex task is described in more technical details and discussed later in the context of addressing other variables such as biological age, sex and genetics.In conclusion, we can assume that the 3D CNN, as an advanced AI feature, will shift the paradigm in all areas researched in this paper. Forensic experts are now guided to step into the era of artificial intelligence as a helpful tool for research and possibly even future routine forensic analyses. Proposed methods and metrics for 3D CNN application on particular forensic topics , summarized in resulting The implementation of advanced AI still requires interdisciplinary cooperation, albeit, with understanding, it can be used to crack unsolved mysteries. It definitely is not a trend that can be ignored.